Low cycle fatigue life prediction remains a challenge in the structural assessment of high-strength alloys used under demanding cyclic loading. For nickel-based superalloys, the difficulty arises because fatigue failure depends on multiple interacting factors, namely, material state, welding history, local deformation, cyclic strain evolution, and accumulated damage, which make life prediction particularly complex. GH4169 is a precipitation-hardened nickel-based alloy used where high strength and resistance to elevated-temperature degradation are required. It is commonly joined by inertia friction welding, but this introduces additional complexity: the weld joint must be evaluated not only as a nominal material but also as a structural region whose cyclic response is shaped by processing and local deformation. Traditional low cycle fatigue prediction has usually relied on strain-life relations, empirical equations, strain-energy approaches, and damage or critical-plane concepts. These methods remain important because they incorporate physically meaningful variables. However, their practical limitation lies in the dependence on empirically fitted parameters and the challenge of capturing fatigue life when multiple interacting factors are involved. In welded GH4169 joints, the prediction problem is made sharper by the need to connect measurable deformation during cyclic loading with final fatigue life in a way that is both data-efficient and physically interpretable.

Deep learning offers an alternative route by extracting patterns from complex datasets without requiring every feature to be predefined. A model trained directly on deformation images may receive information-rich input, but much of that information—such as speckle motion, contrast, and local texture—can be optically complex rather than mechanically decisive for fatigue life. The key question is: what form of experimental information allows neural network to learn a meaningful relationship between cyclic deformation and fatigue life? To address this, Professor Ming-Liang Zhu and Professor Fu-Zhen Xuan from East China University of Science and Technology developed convolutional neural network models in a recent paper published in Fatigue & Fracture of Engineering Materials & Structures for predicting the low cycle fatigue life of GH4169 inertia friction welded joints using three input formats: deformation images, average strain values, and combined average strain with damage fraction. The best-performing model used the combined strain and damage-fraction dataset and achieved the highest reported prediction accuracy in the study, whose framework is shown in Fig.1.

The research team generated a controlled low cycle fatigue dataset from GH4169 homogeneous inertia friction welded joint specimens. The material composition, welding parameters, and tensile properties were specified. Cyclic testing was performed at room temperature under stress control with a stress ratio of -1 and sinusoidal loading. Stress amplitudes ranged from 930 to 1160 MPa, producing fatigue lives from approximately 1,170 to over 25,000 cycles.

In their experimental design, the team simultaneously collected fatigue response data and optical deformation information. Speckle patterns were prepared on specimen surfaces, and a camera-based observation system was used during cyclic loading. Instead of retaining all images, they divided each specimen’s life into five equal periods and selected deformation images associated with peak strain within sampled cycles. This is important because peak strain is a mechanically relevant state within the cyclic response and by using peak-strain-associated images, the dataset was guided toward deformation states more directly related to fatigue accumulation. They generated three data streams from the same experimental foundation. The first consisted of cropped deformation images, used as input to a three-dimensional convolutional neural network (CNN). The second replaced raw images with average axial strain values extracted by digital image correlation (DIC). The third added a damage fraction calculated from the ratio of sampled cycle count to the corresponding fatigue life, following a linear cumulative damage representation. Notably, comparing these inputs was the main methodological strength of the work, because it isolated the effect of image richness from that of mechanically processed information.

The image-based network used convolutional layers to process deformation images from multiple life periods before passing concatenated features to fully connected layers for life prediction. The strain-based and combined-input models achieved higher predictive accuracy. With the smaller image dataset, the test coefficient of determination(R²) was 0.4652; with the larger dataset, it fell to 0.2089. The authors interpreted this as evidence that more image data did not necessarily provide more fatigue-relevant information. Additional images may have introduced optical variation that was less directly connected to fatigue life, increasing the complexity of the learning task when the network had to infer the connection from surface texture to deformation state and then to fatigue life. When average strain values replaced deformation images as network input, prediction accuracy improved markedly. The corresponding one-dimensional convolutional networks reached test R² of 0.8159 for the smaller dataset and 0.9371 for the larger dataset. This improvement is important because the strain values were derived from the same image source that gave poorer results when used directly. It clarifies the role of image processing: digital image correlation acted as a physics-guided feature extraction step, translating optical deformation into a compact variable with direct fatigue relevance.

The combined strain and damage fraction model achieved the strongest prediction. Under the smaller dataset, the test R² increased to 0.8478; and under the larger dataset it reached 0.9560 (Fig.2). The addition of damage fraction allowed the network to receive not only a deformation feature but also a normalized indication of where the sampled state lay within the specimen’s fatigue process. The larger combined dataset produced the most reliable predictions, with test points distributed within the narrower error band. The work therefore supports a clear hierarchy: raw deformation images were less effective, strain values were substantially more informative, and strain values combined with a physics-based damage descriptor yielded the best fatigue life prediction among the tested models.

The findings of East China University of Science and Technology researchers have direct engineering relevance for fatigue assessment of nickel-based welded components, especially where inspection must move beyond visual observation and toward measurable indicators of remaining life. GH4169 inertia friction welded joints are used in demanding mechanical systems, and their low cycle fatigue response is strongly tied to local deformation under repeated loading. By showing that average strain extracted from deformation images provides a much stronger prediction basis than raw images alone, the study points toward a practical monitoring strategy: optical measurements can be useful, but their engineering value increases when converted into mechanically meaningful strain features. For components operating under cyclic loading, this distinction matters. Surface texture, contrast, and image noise may complicate life prediction if they are treated as direct model input. In an engineering setting, the more useful route is to process deformation images through digital image correlation, extract peak strain-related information, and use those values as compact descriptors of the fatigue state. This makes the approach more compatible with inspection systems that must provide interpretable and repeatable indicators rather than opaque image-based judgments.

The integration of damage fraction adds another practical layer. By combining strain response with a measure of accumulated fatigue damage, the model connects what is observed at a given stage of loading with where the component lies in its fatigue life. This is especially relevant for non-destructive evaluation of in-service equipment, where maintenance decisions depend not only on whether deformation is occurring, but on how that deformation relates to life consumption. The demonstrated improvement in prediction accuracy suggests that data-driven fatigue assessment can benefit from physics-based descriptors when they are chosen carefully.

In design and maintenance workflows, the approach could support more informed evaluation of welded joints, fatigue-critical regions, and components subjected to controlled cyclic loading. It may help engineers compare fatigue states across specimens or service intervals using strain-based features rather than relying only on final failure data. Within the tested range, the strongest engineering message is the value of a focused data-physics framework that estimates low cycle fatigue life from experimentally accessible deformation and damage information.

Thermosets derive their dimensional stability from covalent networks that resist molecular flow under prolonged loading and elevated temperature. That same permanent connectivity, however, restricts how the material can be processed after curing and complicates its recovery at the end of service. Chemical deconstruction can break a thermoset into smaller molecular species, but rebuilding those fragments into a material with the original thermal and mechanical response requires more than bond cleavage alone. The regenerated network must recover the molecular architecture that originally controlled stiffness, toughness, creep resistance and stress dissipation.

Most recyclable thermoset designs retain dense covalent connectivity as the main source of mechanical integrity. Reversible bonds can permit reshaping or welding, although the mobility required for exchange may also allow time-dependent deformation. Cleavable comonomers offer another route by fragmenting a permanent network, but practical concentrations can produce relatively short oligomers, branched remnants or species carrying residual junctions. Reincorporating such fragments does not necessarily reproduce the strand lengths and network topology of the parent material. Molecular changes can therefore accumulate from one generation to the next even when the same broad chemistry is retained.

Chain entanglement provides a different basis for load transfer. Long polymer strands that interpenetrate one another can distribute stress and resist separation without requiring every mechanical constraint to be a covalent crosslink. Sparse junctions may then serve mainly to prevent the strands from escaping their entangled configuration. This concept has been established most clearly in soft networks, whereas glassy engineering polymers operate in a mechanically distinct regime. Their load-bearing response is commonly linked to high crosslink density, and the possibility of using entanglements as the dominant structural element has remained less developed.

A regenerative thermoset based on this principle must satisfy several connected requirements. Its backbone must form long, rigid chains with enough entanglements to support substantial loads. A small junction population must suppress terminal flow without replacing the entangled strands as the principal mechanical framework. Cleavage must return soluble linear oligomers rather than permanently branched fragments, and those oligomers must retain sufficient length, mobility and chemical functionality to enter a newly formed network. The regeneration process must also preserve these features through repeated cycles. In a recently published research paper in Nature Materials, Zhenchuang Xu, Edgar Mejia, Tyler Price, Ignacio Arretche, Shuyi Zhang, Ruishi Lei, Valerie Chen, Hannah Liu, Shaofeng Huang, Boran Chen, Sameh Tawfick, Jeremiah Johnson, Nancy Sottos and Jeffrey Moore from the University of Illinois at Urbana-Champaign and the Massachusetts Institute of Technology examined whether glassy, high-temperature thermosets could be redesigned around this separation of roles: entangled chains carrying load and sparse, selectively cleavable junctions maintaining network connectivity.

The researchers first identified linear polymers capable of forming dense entanglement networks without chemical crosslinking. Solvent-free frontal ring-opening metathesis polymerization allowed them to generate the very long chains required for this purpose. Although both candidate monomers produced glassy polymers with high transition temperatures, their behaviour above the glass transition was quite different. Poly(methyl-oxybenzonorbornadiene) softened and began to flow, whereas p(exo-H₂DCPD) maintained a broad rubbery plateau, indicating that its long chains remained strongly constrained by entanglements.

Changing the molecular mass clarified the role of chain length. Shorter chains did not greatly alter the glass transition temperature, but they weakened and narrowed the rubbery plateau. The onset of segmental motion therefore remained similar, while the ability of the polymer to sustain an entangled network at higher temperatures declined. Stereochemistry offered another way to strengthen this response. The endo-enriched H₂DCPD polymer formed a slightly denser entanglement network and showed rubbery behaviour close to that of its chemically crosslinked counterpart over a substantial temperature range.

The authors performed creep testing which showed how strongly these physical constraints affected long-time deformation. The endo polymer resisted flow and recovered most of the applied strain, even though it contained no permanent crosslinks. Longer exo-H₂DCPD chains also produced much greater resistance to creep and better recovery than shorter chains. Dense entanglement did not prevent flow indefinitely, but it allowed the uncrosslinked polymers to behave much more like thermosets within the conditions examined. They also found same architecture improved resistance to fracture. The H₂DCPD polymers matched the stiffness of the crosslinked pDCPD thermoset, yet they could stretch much further before breaking and developed clear strain hardening at large deformation. Their fracture toughness was also comparable to, or greater than, that of the conventional thermoset. These results established that long, densely entangled chains could provide stiffness, toughness and creep resistance without relying on a densely crosslinked network.

The team observed at a recovered-oligomer content of 20 wt%, increasing oligomer length raised the glass transition temperature, strengthened the rubbery plateau and increased both the equilibrium modulus and the relaxing component of the modulus at 180 °C. These responses were consistent with greater chain friction and a larger population of mechanically effective entanglements. Room-temperature tensile properties remained similar at this loading because the network was largely frozen on the measurement timescale.

The investigators also noted differences became more pronounced as the recovered fraction increased and for instance at 30 wt%, networks containing 3.2 kDa oligomers fractured shortly after yielding, whereas those containing 8.5 kDa oligomers sustained extensive deformation. At 40 wt%, 14.5 kDa oligomers preserved ductility without terminal reactivation, but shorter 8.5 kDa chains required norbornene end-functionalization to recover substantial elongation. The governing condition was therefore not recovered content alone. Mechanical performance depended on whether the oligomers were long enough to form load-bearing entanglements and sufficiently integrated into the surrounding network. Oxidative stability imposed another molecular constraint. H₂DCPD-derived oligomers remained soluble and showed no detectable oxidation after ageing in air at 80 °C, unlike corresponding DCPD oligomers, which discoloured, oxidized and became insoluble. Within the more stable H₂DCPD system, networks containing 30 wt% reactivated oligomers retained essentially unchanged glass transition temperature, rubbery modulus, stiffness, strength and elongation through six generations. Their molecular-mass distributions also remained consistent after repeated deconstruction. The approach extended to an NBCPD-based thermoset with a glass transition temperature above 200 °C, although the rigid recovered oligomers became difficult to incorporate at the highest tested loading. This result places regeneration within a clear physical framework: cleavage chemistry permits recovery, but oligomer solubility, chain mobility and entanglement capacity determine how much recovered material can be used without altering mechanical response. The same architecture functioned in carbon-fibre composites and direct-ink-written structures. Composite deconstruction released soluble oligomers and intact carbon fibres that were reused in a second-generation material. Recovered oligomers also adjusted resin viscosity and shear-thinning behaviour, allowing printed structures containing up to 30 wt% recovered material while retaining frontal curing and shape fidelity.

 

 

Cavitation erosion results from the intense interaction between a collapsing bubble and a solid surface. A vapor-filled bubble can expand and collapse very quickly, and the resulting liquid motion can break the protective surface film, deform the material, and create localized pits. Near a rigid wall, the bubble usually collapses asymmetrically. The pressure difference caused by the wall pushes the bubble toward the surface, generates a high-velocity micro-jet, and changes the rebounding bubble into a ring-shaped structure. When this structure collapses, it can produce fragmented flow directed toward the wall. Separating the damage caused by these different stages is important for understanding single-bubble erosion. The main difficulty is both temporal and physical. High-speed imaging can capture changes in bubble shape, jet penetration, movement, rebound, and fragmentation, but it does not directly track how the material responds during these events. Surface microscopy can reveal pits and depressed regions, but these features are usually examined after the collapse sequence or after repeated impacts. A depressed area may be linked to a jet, while a ring of pits may result from toroidal bubble collapse. Surface morphology alone, however, cannot always determine the contribution of each event. Chronoamperometry offers a way to connect bubble motion with surface damage on the relevant timescale. Pure aluminum carries a thin passive oxide film. When collapse-induced loading ruptures that film and exposes the underlying metal to an oxygen-containing electrolyte, oxidation produces a transient current. The height of the resulting current peak reflects the area and depth of the disturbed surface, while repassivation returns the current toward its baseline. This electrochemical response can therefore provide a real-time measure of erosion that complements optical observations rather than replacing them.

In a recently published research paper in International Journal of Multiphase Flow,  Professor Qingmiao Ding, Professor Yanyu Cui, Dr. Yunlong Shan, Dr. Jiakui Yu, and Dr. Futai Guo from the Civil Aviation University of China developed an experimental method that combines high-speed bubble imaging with real-time chronoamperometric measurement of passivation-layer damage on pure aluminum. They introduced a controlled eccentric bubble position that diverts the first-collapse micro-jet away from the working electrode while allowing ring-vortex crushing flow to reach it.

The researchers generated cavitation bubbles with a pulsed Nd:YAG laser and positioned them at controlled distances from a polished, high-purity aluminum working electrode immersed in a saline solution. They defined the dimensionless stand-off distance, γ, as the distance between the bubble centre and the wall divided by the maximum bubble radius. Repeated measurements were used to determine the mean peak current at each position.

Bubble motion changed markedly across the examined distances. Far from the wall, the cavity remained nearly spherical through much of its first contraction, moved gradually toward the surface, and formed a penetrating micro-jet during collapse. Rebound produced a ring vortex that continued toward the boundary and fragmented during later collapse, but the resulting flows had to travel a relatively long distance before reaching the aluminum.

The authors found a different sequence appeared at γ = 1.3 and the wall distorted the contracting bubble from a spherical to an ellipsoidal form. Its first collapse generated a hemispherical jet directed toward the surface, after which the bubble became a ring vortex. During the second collapse, that toroidal structure collapsed asymmetrically and produced a crushing flow accompanied by numerous small bubbles spreading close to the wall. At γ = 0.1, the bubble was tightly adjacent to the boundary and became strongly flattened. The first-collapse jet moved radially along the surface, while the subsequent ring vortex expanded, broke apart, and drove fragmented flow against the near-wall region.

They also performed chronoamperometric measurements which showed a non-monotonic dependence of erosion on stand-off distance. The largest peak current occurred at γ = 0.1, identifying this condition as the most damaging among those tested. Damage then decreased, varied at intermediate positions, and rose again near γ = 1.3 before declining with increasing separation. At sufficiently large distances, current transients became barely detectable. Distance therefore did more than scale impact strength; it altered the collapse mode and, consequently, the mechanism by which energy reached the surface.

To separate micro-jet damage from ring-vortex damage, the team introduced a dimensionless eccentricity of ε = 0.8. This design choice displaced the bubble vertically so that the first-collapse jet struck the surrounding insulating substrate rather than the aluminum electrode, while the more spatially distributed crushing flow from the toroidal collapse could still reach the metal. At γ = 1.3, the measured peak current changed by less than 1% when eccentricity was introduced. The micro-jet thus contributed little to erosion under that condition, whereas the second-collapse ring-vortex flow accounted for most of the electrochemical response. At γ = 0.1, eccentricity reduced the peak current to less than half its centred value, showing that the micro-jet made a substantial contribution there.

The team observed repeated-bubble exposure to produce surface morphologies consistent with this interpretation. At γ = 0.1, the aluminum developed a central depressed region surrounded by an asymmetric annulus of concentrated pits. The central deformation was associated with the jet, while the annular pitting corresponded to fragmented crushing flow from the later toroidal collapse. At γ = 1.3, the centre remained comparatively flat and the damage consisted mainly of densely distributed, shallower pits in an asymmetric ring. The agreement among bubble imaging, current response, and surface morphology supported the distinction between the two erosion mechanisms.

The findings of Professor Yanyu Cui and colleagues provide a useful basis for interpreting and managing cavitation erosion in hydraulic machinery, marine equipment, piping systems, valves, pumps, propellers, and other components exposed to repeated bubble collapse near solid boundaries. A key engineering implication is that erosion severity cannot be estimated from bubble proximity alone. The dimensionless stand-off distance changes the collapse mode and determines whether surface damage is driven mainly by a direct micro-jet, by the fragmented flow produced during ring-vortex collapse, or by both mechanisms acting together. A centrally depressed region surrounded by annular pitting is consistent with the combined action of a wall-directed jet and the later collapse of a toroidal bubble. In contrast, dense ring-shaped pitting with a comparatively flat centre indicates that ring-vortex crushing flow is likely to be the dominant source of erosion. Such morphological signatures may help engineers relate observed surface damage to the underlying bubble dynamics rather than treating all cavitation pits as products of a single impact mechanism.

The non-monotonic relation between stand-off distance and damage is also relevant to equipment design. The most severe erosion occurred when the bubble was very close to the wall, but pronounced damage reappeared at an intermediate distance where the direct jet contributed little. Geometric modifications intended to redirect bubbles, alter near-wall flow, or move collapse zones away from vulnerable surfaces should account for the possibility that toroidal collapse and fragmented crushing flow can remain damaging even when direct jet impact is limited. The combined measurement approach proposed in the new study has practical value for laboratory evaluation of cavitation-resistant materials and protective surfaces. Chronoamperometry records passivation-layer disruption in real time, while high-speed imaging identifies the corresponding collapse stage and surface microscopy confirms the accumulated damage pattern.

Metallic components exposed to chloride-containing environments depend heavily on the stability of their surface passive films. A material may corrode slowly near its open-circuit condition but lose protection as the potential increases. Another may remain passive across a broad potential range but still have a relatively high self-corrosion rate. To achieve low self-corrosion current density and a wide, stable passive region is a demanding materials-design problem. For protective coatings intended for marine service, the challenge is intensified by chloride penetration, local compositional differences, defects within the deposited structure, and variations in oxide growth across chemically distinct regions.

Multi-principal element coatings offer unusual freedom in addressing this problem because several elements can contribute to both matrix formation and surface passivation. Cr, Nb, Ti, and Zr provide complementary roles in these processes. Their oxides differ in thermodynamic stability, dissolution behaviour, and response to increasing electrode potential. Cr- and Zr-containing oxides can help restrain current at relatively low potentials, whereas Nb- and Ti-containing species can support passivity at higher potentials. However, these elements must be distributed properly for each one to contribute effectively to corrosion protection. Phase separation may concentrate Nb and Ti within crystalline regions while leaving other areas enriched in Cr and Zr. The resulting chemical heterogeneity can produce uneven passive-film thickness, local electrochemical differences, and sites susceptible to metastable pitting.

Oxygen has a distinct role in this design problem. It is commonly treated as an impurity during conventional alloy production, but it is also essential to passive-film formation. External pre-oxidation can create protective oxides, although it may introduce pores, cracks, or local electrochemical differences. A key question is whether oxygen can instead be added during coating deposition to modify the metallic structure before corrosion begins. This could influence phase stability, element distribution, lattice distortion, metal dissolution, and the subsequent growth of the passive film. In a recently published research paper in Corrosion Science Dr. Jun Fan, Dr. Hongwei Yao, Prof. Kai Xu, Dr. Gang Liu, and Prof. Jibin Pu from the Ningbo Institute of Materials Technology and Engineering, CAS developed an in-situ oxygen-doped CrNbTiZr multi-principal element coating deposited by magnetron co-sputtering.

The deposited coatings were dense and free of pores and columnar pathways that could inhibit chloride ingress. Their internal structure changed progressively with oxygen incorporation. The low-oxygen coating contained BCC crystallites, an amorphous phase, and nanoscale ZrCr₂ Laves regions. At an intermediate oxygen content of 13.5 at%, the crystalline fraction decreased sharply, the remaining BCC regions became more distorted, and the Laves phase disappeared. Further oxygen incorporation produced an almost fully amorphous structure. These changes arose because oxygen increased lattice distortion and destabilized the crystalline phases.

The research team found the structural transformation altered the distribution of the passivating elements. For instance, in the mixed crystalline–amorphous coatings, Nb and Ti were concentrated within the BCC regions, while Cr and Zr were more abundant in the surrounding matrix. As oxygen reduced the crystalline fraction, Nb and Ti became more evenly distributed throughout the amorphous phase. Oxygen-induced amorphization therefore did more than remove crystalline interfaces; it placed the passivating elements more uniformly across the coating.

The researchers conducted electrochemical measurements in 3.5 wt% NaCl identified the coating containing 13.5 at% oxygen as the optimum composition and observed a self-corrosion current density of 2.62 × 10⁻⁹ A cm⁻², the lowest passive current density, and the highest polarization resistance among the tested coatings. Its stable passive region exceeded 2.5 V, and the current oscillations associated with metastable pitting were absent. The lower-oxygen coatings showed current oscillations associated with metastable pitting, whereas the coating with the highest oxygen content exhibited reduced corrosion resistance despite its fully amorphous structure

The authors performed microscopy and spectroscopy to clarify why the intermediate oxygen content was most effective and found the low-oxygen coating developed a passive film of uneven thickness over Cr-rich and Nb/Ti-rich regions. By contrast, the optimized coating formed a nearly uniform film across the different underlying phases. The passive film was amorphous and consisted mainly of Cr-, Nb-, Ti-, and Zr-based oxides and hydroxides, arranged as a Cr-rich inner layer and a Ti/Zr-rich outer layer. Local oxygen–metal bonding slowed elemental dissolution. The more uniform distribution of Nb and Ti also supported balanced oxide growth across the coating and these changes resulted in a denser and more consistent barrier against chloride attack.

The oxygen-tailored CrNbTiZr coating proposed in the new study by Professor Jibin Pu and colleagues is especially relevant to metallic components that operate in chloride-rich marine environments, where low corrosion rates and stable passivation are required at the same time. Potential applications include exposed surfaces on offshore platforms, marine vessels, subsea equipment, coastal infrastructure, and components used in seawater-handling systems.

The coating may be suitable for stainless-steel components exposed to prolonged immersion or elevated electrochemical potentials. Such conditions may occur where oxygen concentration, water flow, salinity, or contact with dissimilar metallic components varies during service. Its resistance to metastable pitting is also important for engineering surfaces that cannot tolerate localized penetration. The approximately uniform passive-film thickness formed on the oxygen-optimized coating reduces differences in protection between chemically distinct regions. This behaviour is relevant to coated surfaces where localized corrosion could compromise protection of the underlying substrate. From a manufacturing perspective, the use of magnetron co-sputtering allows the oxygen content to be adjusted during coating deposition. The protective response can therefore be controlled through processing rather than through a separate pre-oxidation treatment. This processing approach allows oxygen content to be regulated directly during physical vapour deposition, avoiding the need for a separate pre-oxidation step.

The findings of Jun Fan et al also provide a practical composition-design principle. Maximum oxygen content was not the most effective condition. Instead, an intermediate oxygen level produced the best balance among amorphization, Nb and Ti redistribution, oxide composition, and passive-film compactness. Engineering development should focus on controlling oxygen within a defined processing window, while maintaining coating density and the phase balance needed for uniform passivation.

Nuclear magnetic resonance is powerful because nuclear spins can retain quantum information for relatively long times while remaining sensitive to their local magnetic and structural environment. That same weak coupling, however, also makes nuclear spins difficult to initialize and detect with high sensitivity, especially when one wishes to move from ensemble-averaged spectroscopy toward small, well-defined spin systems. Optical access changes this balance. If a nuclear spin state can be prepared, manipulated, and read through an optical transition, NMR gains a route toward low-field sensitivity, molecular-scale addressability, and direct connection to photonic quantum architectures. The central difficulty is that optical and nuclear degrees of freedom are not naturally linked in most molecular systems in a way that permits coherent control without introducing additional decoherence channels.

A common strategy is to address nuclear spins indirectly through electron spins, using optical transitions connected to magnetic electronic states. This route has been powerful in solid-state defect systems, but it brings a physical compromise: the same electron spin that enables optical access can also add magnetic noise, restrict useful spin density, and limit the nuclear coherence that makes the spin attractive in the first place. Trivalent non-Kramers rare-earth ions offer a different route. In Eu3+, the absence of a net electronic spin allows the nuclear spin to be accessed through ultranarrow optical transitions without relying on a coupled electron spin. That distinction is central to the present paper.

In a recently published research paper in Nature Materials Dr. Evgenij Vasilenko, Vishnu Unni Chorakkunnath, Dr. Jeremias Resch, Nicholas Jobbitt, Dr. Diana Serrano, Dr. Philippe Goldner, Dr. Senthil Kumar Kuppusamy, and led by Professor Mario Ruben & Professor David Hunger from the Karlsruhe Institute of Technology in Germany  developed an optically detected nuclear magnetic resonance approach for coherently controlled 151Eu3+ nuclear spins in a stoichiometric europium molecular crystal. They combined spectral-pit-based optical spin initialization, RF control of two nuclear quadrupole transitions, and optical read-out of spin population changes through ultranarrow 7F0 to 5D0 transitions. The technically distinct advance is the demonstration of Rabi oscillations, Hahn-echo coherence, and CPMG dynamical decoupling in a molecular rare-earth complex with direct optical nuclear spin access. They also established a measurable correlation between optical transition frequency and nuclear spin resonance properties, linking local molecular crystal-field variation to both optical and RF response.

The researchers began with millimetre-sized single crystals of the europium complex grown by slow solvent evaporation, then incorporated an individual crystal into a fibre-based ferrule arrangement operated in liquid helium at 4.2 K. This experimental choice mattered because the optical transition itself served as the entry point to the nuclear spin system. The crystal quality therefore had immediate consequences for how selectively the Eu3+ ions could be addressed. Optical characterization of the 7F0 to 5D0 transition gave an inhomogeneous linewidth of 1.94 GHz, substantially narrower than previously reported for a microcrystalline powder of the same molecular material. Spectral hole burning yielded a homogeneous linewidth of 310 kHz, corresponding to an optical dephasing time just above one microsecond, while optical free-induction decay and photon echo measurements provided a more direct view of instantaneous optical coherence and optical coherence time. They also established that high-quality molecular crystals could provide a sufficiently narrow optical interface for nuclear spin experiments. The optical line was then used to prepare a spin-polarized sub-ensemble by burning a spectral pit through optical pumping. Rather than performing full hyperfine class preparation, the team used a 10 MHz-wide chirped optical burn that depleted one hyperfine ground-state population for a selected class of ions. The consequence of this design choice was a practical one with direct spectroscopic value: the spin preparation was fast enough and produced enough contrast to support repeated optically detected NMR measurements.

The authors obtained nuclear spin lifetime by monitoring recovery of the spectral pit. The decay required two time constants, a shorter component of 4.4 s and a longer component of 120 s. The long persistence of the optically prepared population made it possible to interrogate the quadrupole transitions of 151Eu3+ with radio-frequency pulses and detect the resulting population redistribution optically. Two ground-state nuclear quadrupole resonances were resolved at 21.475 MHz and 33.944 MHz, assigned to the |±1/2〉 to |±3/2〉 and |±3/2〉 to |±5/2〉 transitions, respectively. Their linewidths were not equivalent. The 34 MHz transition showed an 88 kHz inhomogeneous linewidth, while the 21.5 MHz transition was broader but gave stronger signal contrast under the same pulse conditions. They probed the 21.5 MHz transition at different positions across the optical inhomogeneous line, the researchers found that the spin transition frequency shifts with optical probing frequency, with an approximate gradient of −4 kHz GHz−1. The spin linewidth also increased toward the wings of the optical distribution. This correlation tied the nuclear quadrupole environment to the optical transition energy and showed that strain or local ligand-field variation affects both degrees of freedom in a linked, material-specific manner. In a molecular system where the crystal field symmetry differs from common inorganic hosts, this observation is especially informative because it connects the optical read-out channel to the local quadrupolar parameters that set the nuclear resonance.

The team tested coherent manipulation on the 21.5 MHz transition. Radio-frequency driving produced nuclear Rabi oscillations with a Rabi frequency of 14 kHz at 92 W, and the expected square-root dependence of Rabi frequency on RF power was observed. The damping of the oscillations reflected the inhomogeneous distribution of transition frequencies, which is precisely the kind of dephasing that pulsed NMR methods are designed to refocus. A Hahn-echo sequence extended the measurement from driven population oscillations to coherent spin evolution, giving a nuclear spin coherence time of 0.61 ms. Carr–Purcell–Meiboom–Gill dynamical decoupling then increased the observed coherence to 2.0 ms with eight refocusing pulses. The authors afterwards measured exponent, 0.53, differed from the value expected for a simple correlated noise bath of a single spin species. The authors attributed the more complex decoherence environment to a combination of nearby proton spins, randomly distributed 13C spins, possible residual paramagnetic impurities from the europium salt precursor, and quasi-localized low-frequency vibrational modes. The result is a coherent molecular nuclear spin system whose dephasing is not dominated by a single idealized noise source, but by the chemically and structurally specific environment of the molecular crystal.

The importance of the research work of Karlsruhe Institute of Technology scientists is its direct experimental connection between optical spectroscopy and coherent nuclear spin control in a molecular rare-earth complex. Previous molecular europium systems had already shown properties needed for optical nuclear spin access, but the present work completes a more demanding sequence: optical initialization, optically detected nuclear magnetic resonance, coherent RF-driven spin manipulation, spin echo refocusing, and dynamical decoupling. That combination establishes molecular Eu3+ nuclear spins as experimentally controllable quantum objects rather than only long-lived spectroscopic states. The findings also sharpen how molecular design should be viewed in this area. The ligand field is not a passive host environment surrounding an otherwise standard rare-earth ion. It determines the quadrupolar structure, influences the correlation between optical and RF transition frequencies, and contributes to the strain-sensitive inhomogeneous broadening observed across the optical line. Because the molecular complex has a defined coordination environment, these correlations can be treated as part of the material’s controllable physics. The paper therefore supports a design logic in which optical linewidth, nuclear quadrupole structure, spin lifetime, and spin-bath composition are considered together.

The coherence times reported here are measured in an ensemble molecular crystal at liquid-helium temperature, and the authors keep their future expectations tied to specific physical routes: stronger dynamical decoupling, lower temperature operation, magnetic-field polarization of paramagnetic impurities, suppression of low-frequency vibrational modes, isotopic or chemical purification, and ligand deuteration. These are not abstract claims of improvement; they follow from the dephasing sources identified in the measurements. The work also suggests that optically detected NMR in such complexes may become a sensitive probe of material properties, since optical and spin inhomogeneities carry linked information about strain and local crystal-field variation. For molecular quantum technologies, the result is technically meaningful because it brings together atomically defined molecular architecture with direct optical access to coherent nuclear spins. The demonstrated millisecond-scale nuclear coherence, optical spin preparation, and RF control form a platform on which more elaborate molecular spin registers could be explored, especially if future experiments move toward single-molecule read-out or nanophotonic integration.

 

 

Extrusion-based additive manufacturing gives polymer processing a form of geometric freedom that conventional molding cannot easily match and can build customized parts directly from digital toolpaths, produce internal features without dedicated molds, and adapt quickly from one geometry to another. However, the same layer-by-layer and road-by-road deposition that gives the process its flexibility also limits performance when dense, load-bearing polymer parts are needed. A printed component made by fused deposition or fused filament fabrication is not simply a shaped polymer body; it is an assembly of deposited roads whose interfaces record the thermal and mechanical history of printing. The weakest direction is often the build direction, where tensile loading must be carried through interlayer bonds rather than through a fully consolidated material volume. A second challenge is production rate and increasing extrusion speed alone cannot fully resolve it because the melt must still be generated, delivered, deposited, and bonded along a sequential path. Higher flow rates also affect nozzle pressure, material residence time, and deposition stability. The trade-off between resolution and throughput therefore remains deeply connected to the physics of material extrusion, not only to machine settings.

In a recently published research paper in Journal of Manufacturing Processes, Zexin Wang, Shilin Wang, Jiawei Zhu and Professor Ranran Jian from Qingdao University of Science and Technology working together with Dr. Xiang Cheng from China Aerospace Science and Technology Corporation developed an injection-based additive manufacturing process in which an extrusion-printed thermoplastic shell serves as an intrinsic mold for a subsequently injected core. The work is unique in the unified reciprocating screw mechanism, which performs shell printing by rotation and core filling by axial injection within the same material system. They also developed a coupled simulation strategy to analyze shell-core interfacial melting, pressure-driven deformation, cooling shrinkage, and debonding.

During shell fabrication, screw rotation controls the extrusion and deposition of successive layers. The same unit then shifts to axial motion and injects molten polypropylene into the cavity enclosed by the printed shell. This dual operation replaces the slowly deposited internal infill with a dense core formed through rapid volumetric filling. The researchers treated the new mechanism as a coupled thermal, fluid, and structural problem. They examined flow simulations and how nozzle diameter and injection speed influence shear heating during core delivery. The authors found for polypropylene, small nozzles sharply restricted the safe injection rate because the melt temperature rose too quickly. Larger nozzle diameters provided a better balance between extrusion stability and injection capacity, while very small nozzles generated higher pressure and stronger velocity fluctuations during shell printing. The nozzle used for validation was therefore selected to perform reliably in both phases of the process.

The research team paid good attention to the shell-core interface  and found that molten polypropylene entering the cavity transferred heat to the colder printed shell and partially re-melted its inner surface. This local re-melting promoted fusion across the interface rather than leaving the core in mechanical contact with the shell alone. At the same time, injection pressure could deform the shell, and uneven cooling could generate shrinkage stresses between the two regions. The co-simulation therefore considered interfacial melting, shell deformation, structural collapse, and debonding. Shell thickness and injection flow rate proved especially important. Thicker shells resisted deformation and retained greater thermal stability, whereas slower filling prolonged heat transfer into the shell.

The investigators printed tensile specimens with the build direction aligned with the loading direction. This placed the comparison in the mechanically weakest orientation for conventional extrusion-based manufacturing and made the test particularly sensitive to interlayer bonding. The reference specimens contained fully printed grid infill, whereas the hybrid specimens combined a printed shell with an injected core. The injection-based parts showed an approximately 80% increase in ultimate tensile strength and an approximately 89% increase in elongation at break. These changes indicate that the hybrid process improved both resistance to failure and the capacity for deformation before fracture. They also examined fracture morphology and noticed the extrusion-based specimens retained the layered structure and local voids associated with deposited roads, and failure occurred mainly along weak interlayer boundaries. Their fracture surfaces also showed stress whitening and localized crazing, consistent with greater plastic deformation. The improvement arose from two related effects: injection replaced much of the road-by-road internal structure with a consolidated core, and the heat of the incoming melt strengthened bonding where the core met the printed shell.

The findings of Professor Ranran Jian and colleagues point to practical engineering use wherever polymer components need the geometric flexibility of additive manufacturing but also require better consolidation, higher build-direction strength, and shorter production time than conventional extrusion-based printing can usually provide. The shell-mold printing and core-filling strategy is especially relevant for customized structural polymer parts in which the outer geometry must be digitally defined, but the interior does not need to be built slowly as a printed grid. By replacing printed infill with an injected polymeric core, the process creates a denser internal structure and reduces the number of weak interlayer interfaces that normally control failure in the build direction. This makes the approach useful for load-bearing or semi-load-bearing thermoplastic components where conventional FDM or FFF parts may be limited by anisotropy. Housings, brackets, fixtures, tooling aids, protective casings, and customized mechanical supports could benefit from the combination of printed shape control and injection-like core consolidation, provided the part can be designed with cavities that can be filled through suitable gates and venting paths. The work also suggests value for small-batch manufacturing, where traditional injection molding may be too costly or slow because a dedicated mold is required. Here, the printed shell becomes the mold, allowing mold fabrication and part fabrication to merge into one additive route.

In the authors’ analysis, the time saved becomes more substantial as the volume of printed infill that would otherwise be deposited line by line is replaced by rapid cavity injection. This is important for industrial additive manufacturing, where production time often determines whether a process is practical beyond prototyping. From a design standpoint, we can think of the new study gives engineers useful guidance  and shell thickness, nozzle diameter, injection speed, cavity height, and filling pressure must be chosen together because they control thermal penetration, shell deformation, interfacial fusion, and shrinkage behavior. Controlled shell deformation may even help compensate for cooling shrinkage, but excessive pressure or heat must be avoided.

Ultrafast pump–probe measurements are often built around repetition. A laser pulse perturbs a material, a delayed probe records one part of the response, and many such events are averaged until a reproducible dynamical trace appears. That strategy has been remarkably powerful when the response is effectively the same from one excitation event to the next. It becomes less straightforward when the material itself does not return along a single deterministic path. In systems where fluctuations, metastability, local disorder, mobile ions, or heterogeneous strain influence the response, averaging can erase precisely the behavior that carries physical meaning. The difficulty is separating fluctuations that belong to the sample from fluctuations introduced by the apparatus. A small drift in probe intensity, instability of the pump pulse, limited photon counts, or insufficient temporal sampling can all imitate or obscure variations in the material response. Single-shot approaches can avoid some of the averaging problem, but they often record only one delay time after a given pump event, or they divide the probe signal across many temporal slices so that the information from each slice becomes weak. The scientific gap addressed here lies in extracting correlations within apparently random nonequilibrium trajectories while still using a stroboscopic measurement architecture.

In a recently published research paper in Nature Communications, Dr. Jackson McClellan, Professor Alfred Zong, Dr. Kim Pham, Dr. Hanzhe Liu, Dr. Zachery  Iton, Dr. Burak Guzelturk, Donald A. Walko, Haidan Wen, Professor Scott Cushing & Professor Michael Zuerch from University of California and from California Institute of Technology developed nonequilibrium noise correlation spectroscopy as a statistical method for extracting correlations from stochastic pump-induced trajectories in stroboscopic time-resolved measurements. They applied it to the c-axis lattice response of a single LLTO grain measured by synchrotron X-ray micro-diffraction after above-bandgap photoexcitation. The technically distinct element is the use of two-time correlation analysis on repeated local lattice-parameter scans to quantify persistence between neighboring excitation events. This enabled them to infer a trajectory-switching probability and an associated activation barrier linked to lithium-ion motion.

The researchers’ experimental strategy used synchrotron-based time-resolved X-ray micro-diffraction to monitor the c-axis lattice parameter of LLTO after ultraviolet excitation. The sample was prepared as a sintered polycrystalline pellet, and the X-ray beam size was comparable to the scale of individual grains. That design choice mattered scientifically because it avoided averaging over a powder ensemble and allowed the stochastic lattice motion of a single grain to be examined directly. The pump laser operated at 1 kHz, while the X-ray probe recorded diffraction at delay times from before excitation to tens of microseconds afterward. Each delay point averaged about one thousand pump–probe pairs, and the full delay scan was repeated ten times with almost no waiting between scans. The authors found photoexcitation produced a sudden c-axis expansion larger than 0.1%, followed by a slower recovery over tens of microseconds. However, the individual scans were not smooth replicas of that mean behavior. After time zero, the lattice parameter displayed abrupt discontinuities and streaks of similar values. Before photoexcitation, the fluctuations were much smaller. This contrast was central to the interpretation, since instability of the X-ray measurement would be expected to affect all delay times in a similar way. The researchers also examined pump laser power fluctuations and found that they were far too small to account for the observed c-axis variations. A separate fluence-dependent measurement showed a linear lattice displacement with pump power, arguing against a hidden nonlinear amplification of small pulse-energy changes.

The team noticed stochastic response had two important statistical signatures. First, the scan-to-scan variation was strongest soon after excitation and decreased at longer delays. The standard deviation across scans followed a temporal form similar to the averaged lattice expansion and relaxation, yet the relaxation time associated with the standard deviation was substantially shorter than that of the mean response. Simulations showed that this relation could be reproduced when the initial lattice expansion and the relaxation time were negatively correlated. In physical terms, larger initial expansion was associated with faster recovery, consistent with transient c-axis lattice stiffening rather than a softening response. Second, the streaks in the time traces implied that neighboring pump-induced trajectories were not independent. To quantify that behavior, the researchers computed Pearson correlation coefficients between lattice-parameter values recorded at different delay positions within the repeated scans. Because each microsecond delay increment corresponded to roughly one thousand elapsed pump shots in the acquisition scheme, correlations between nearby delay indices reflected persistence across neighboring excitation events. The resulting two-time correlation analysis revealed enhanced positive correlation near the diagonal, and the averaged autocorrelation decayed exponentially with a characteristic length of about 1,500 ± 300 pump shots.

A simple stochastic simulation helped give this number physical meaning. The researchers modeled individual photoinduced lattice trajectories using the phenomenological form that described the averaged response, but allowed the initial expansion amplitude to switch only with a small probability. With a switching probability of about 0.09 ± 0.02% per pump shot, the simulation reproduced the observed correlation matrix, histogram distribution, and exponential correlation decay. Interpreting that probability through an activated process at the estimated photoinduced lattice temperature gave an energy barrier of 0.4 ± 0.1 eV. That value falls close to the reported energy range for lithium-ion migration in LLTO, supporting the interpretation that photo-assisted lithium displacement can slightly alter the metastable lattice structure after individual pump events.

The study is important because it shows that fluctuations in the photoinduced lattice response can carry measurable information about microscopic ionic and structural dynamics. Conventional averaging would retain the photoinduced expansion and recovery of the LLTO lattice, but would largely suppress the correlated variations between successive excitation events. By analyzing how deviations persist across repeated pump shots, the researchers connected stochastic lattice trajectories to an energy scale associated with lithium motion. The paper therefore changes the interpretation of pump–probe variability in this solid electrolyte: the irregularity is not simply experimental inconvenience, but a measurable signature of coupled ionic and structural dynamics.

For LLTO, the new findings support a picture in which photoexcitation does more than transiently heat the lattice. Ultraviolet excitation can drive lattice vibrations and thermal expansion, but harmonic phonon excitation alone would not account for persistent changes in the temporally averaged lattice structure. The analysis points instead toward interaction between excited lattice modes and lithium-ion motion, allowing the system to enter slightly different metastable structures from one pump event to another. The negative relation between expansion amplitude and relaxation time is especially informative, because it links the stochastic structural response to a transiently stiffer lattice state rather than to a slowing near a softened structural instability.

The methodological contribution is equally significant, but it should be described within the demonstrated scope. The researchers showed that a highly stable, high-flux synchrotron X-ray probe can recover correlations from averaged stroboscopic data when the experiment is designed around local measurement, low instrumental noise, appropriate repetition rate, and statistical reconstruction. This does not replace single-shot dynamics; rather, it provides another route for identifying persistence and switching in systems where individual trajectories are hidden inside repeated measurements. The approach is particularly suited to microscopic regions whose local dynamics would be lost in ensemble averages.  In ionic conductors, lattice deformation and ion mobility are not separable in a simple static sense, and this paper gives an experimental route to observe their coupling through correlated noise in the transient response. Its strongest message is restrained but valuable: under photoexcitation, a solid-state ionic conductor can display stochastic lattice dynamics with measurable memory, and that memory carries an activation scale consistent with lithium migration.

Modern high-performance computing is moving toward architectures built on massive parallelism, instead of depending on further improvements in individual transistor performance. With device miniaturization approaching fundamental physical limits and , integrating larger numbers of cores onto a single chip has become the dominant strategy for sustaining computational growth. Graphics processing units (GPUs), especially those designed for artificial intelligence workloads, are a prime example. However, thermal management is arising as a real limitation and this is because the concentration of power dissipation within densely packed functional units leads to spatially heterogeneous temperature fields, where localized hot spots emerge and evolve dynamically in response to workload fluctuations. These thermal gradients directly influence device reliability, operational stability, and long-term degradation. As cooling strategies approach practical limits, sustaining performance increasingly depends on predictive models that can resolve fine spatial and temporal temperature variations across large-scale chips. Classical numerical approaches, including finite element, finite volume, and finite difference methods, remain the reference standard for thermal analysis due to their physical fidelity. However, their computational burden scales poorly with system size and resolution, which make them not suitable for real-time applications. Other methods were tried to reduce the cost by simplifying the physics or introducing surrogate representations such as circuit-based thermal models which can achieve speed through coarse spatial aggregation but may miss intra-unit hot spots and can approximate heat transfer pathways in a coarse way. Also, purely data-driven methods, such as neural networks, can provide efficiency, but they lack enforcement of the underlying equations, which can reduce confidence in predictions outside the training regime and make interpretation less direct.

Physics-based reduced-order modeling is currently emerging as a promising middle ground with proper orthogonal decomposition (POD), combined with Galerkin projection (GP), providing a structured way to reduce dimension but in the same time retain the underlying physics of heat transfer. Earlier implementations have demonstrated that such models can achieve high accuracy with a small number of modes when applied to systems with relatively few cores. The difficulty arises when extending these methods to architectures with thousands of interacting heat sources. Training such models becomes prohibitively expensive, as the number of possible power configurations grows rapidly. The central challenge, then, lies in preserving the efficiency and accuracy of physics-enforced reduced-order models while making them scalable to the size and complexity of modern many-core GPUs. In a recent research paper published in Structural and Multidisciplinary Optimization, Professor Lin Jiang from the College of Information Science and Engineering at Northeastern University  in China and Professors Yu Liu & Ming-Cheng Cheng from the Department of Electrical and Computer Engineering at Clarkson University developed a local ensemble POD-GP thermal modeling method that combines domain truncation with reusable generic sub-models.  The new approach is built around a different treatment of training locality. The underlying strategy begins by reconsidering how training data are generated and how the domain of the problem is represented. Instead of constructing a single global model or even assembling multiple full-domain models for individual power sources, the approach partitions the chip into a set of power source blocks, each representing one or several functional units. For each block, the thermal response is characterized independently. The key conceptual shift is that thermal influence from a localized source attenuates with distance, so the full chip does not need to be included in every training problem. By exploiting this behavior, the researchers truncate the computational domain around each power source block, restricting training to a localized region where temperature variations remain significant. This choice directly reduces the number of spatial degrees of freedom required in the simulations used to generate training data.

The authors estimated thermal length scales by analyzing how temperature decays away from a heat source, and the domain is extended to several multiples of this length to balance accuracy and efficiency. The analysis shows that extending the domain to approximately five thermal lengths captures the majority of the thermal contribution while avoiding unnecessary computational expense. This decision reflects a clear connection between physical behavior and model construction: the spatial extent of the training domain is dictated by heat diffusion characteristics rather than by geometric convenience. Once the research team established localized training domains, they applied POD to temperature fields generated from high-fidelity finite element simulations. The resulting POD modes provide a compact representation of the dominant thermal patterns within each truncated region. They used Galerkin projection to map the heat transfer equations onto this reduced space, producing a set of ordinary differential equations that describe the temporal evolution of modal coefficients. This step ensures that the reduced-order model remains anchored in the physics of heat conduction, avoiding the extrapolation issues associated with purely data-driven methods.

Many functional units within the GPU share identical or nearly identical geometries. Rather than training a separate model for each such unit, the method introduces generic truncated domains. A single trained model can therefore represent multiple identical regions, substantially reducing the total number of models required. For units located less than five thermal lengths from a chip boundary, a distinct truncated domain is trained separately to capture the boundary‑condition variations encoded in the finite‑element–generated temperature fields. In the case examined, a system comprising hundreds of power source blocks is represented by only a small set of generic models, each reused across multiple locations with appropriate spatial mapping. The assembled model reconstructs the global temperature field by superposing the contributions from all localized models. Because the underlying heat transfer process is linear within the operating temperature range considered, this superposition remains valid. The approach therefore combines localized accuracy with global coverage, without incurring the cost of full-domain simulations for every configuration. When applied to a large GPU architecture with more than ten thousand cores, the method produces detailed spatiotemporal temperature predictions that closely match those obtained from full finite element simulations. At the same time, the computational cost is reduced by several orders of magnitude. The model captures the emergence and evolution of dynamic hot spots across the chip, resolving fine spatial features that would be inaccessible to coarser modeling approaches.

The new approach of Professors Jiang, Liu & Cheng reconciles accuracy and computational efficiency by making locality part of the model architecture, rather than treating it as an after-the-fact simplification.  This distinguishes the method from purely data-driven surrogates, particularly in situations where operating conditions deviate from those seen during training. The ability to estimate error based on the eigenvalue spectrum of the POD modes introduces a level of predictability that is often absent in alternative approaches. It provides a quantitative link between model complexity and expected accuracy, which can guide practical implementation. The treatment of repeated structures within the chip further reflects a pragmatic understanding of modern hardware design. Many-core GPUs are characterized by a high degree of structural regularity, and the method turns this repetition into an opportunity for model reuse. The resulting approach enables detailed thermal analysis at scales that would otherwise be inaccessible for real-time or near-real-time applications. This has implications for dynamic thermal management strategies, where rapid prediction of temperature distributions is essential for controlling power and performance. Also, while the study focused on a specific GPU architecture, the underlying ideas could, in principle, extend to other large-scale integrated systems.

FIGURE LEGEND: Temperature maps of the Tesla Volta GV100 GPU at an instant in time calculated using (a) the finite element (FEniCS‑FEM) method and (b) the local ensemble POD‑GP (LEnPOD‑GP) model with six modes per truncated domain. Temperature profiles along the (c) x and (d) y directions through the high‑peak temperatures predicted by FEniCS‑FEM and LEn‑POD‑GP, along with the deviation of LEn‑POD‑GP from FEniCS‑FEM.  CREDIT: Struct Multidisc Optim 68, 235 (2025). https://doi.org/10.1007/s00158-025-04165-x

 

Water-rich hydrogels are important in soft materials engineering because they are polymer networks filled with large amounts of water. This hydration gives them flexibility, permeability, and biological relevance, but it also makes mechanical reinforcement difficult. When the polymer chains are loosely connected or poorly organized, the network may deform easily and transfer stress inefficiently. Poly(vinyl alcohol) (PVA) hydrogels are a good example for examining this strength–hydration conflict because their physical networks can be shaped by hydrogen bonding, crystallite formation, freeze-thaw processing, and water-mediated chain mobility. One route to overcoming this difficulty is to move beyond isotropic network reinforcement and instead introduce directional organization. In many load-bearing soft biological materials, strength does not arise from uniform density alone, but from aligned hierarchical structures that distribute stress along preferred directions. To apply this idea to hydrogels requires a processing method that can align polymer chains and larger structural domains without preventing the final material from rehydrating. Methods that create anisotropy may also require multiple steps, harsh processing conditions, or solvent-intensive treatments, which weakens their practical and environmental appeal. Another challenge is what happens after the hydrogel is damaged or discarded because many hydrogel systems are designed around initial performance, however, recyclability and material regeneration receive less attention. Physically cross-linked hydrogels should, in principle, be recyclable. The challenge is finding a way to press the damaged pieces back into a continuous network without harsh chemistry or large amounts of solvent.

Professor Jiefeng Gao and his master students from Yangzhou University working together with Professor Longcheng Tang from Hangzhou Normal University developed a low-temperature, solvent-free green calendering method for fabricating anisotropic PVA hydrogels from freeze-thawed physical gels. The process uses coupled shear and compression to align PVA chains, form hierarchical lamellar structures, reduce crystallinity, and preserve high water content after rehydration. They also developed a closed-loop recycling route in which damaged hydrogels are reprocessed into regenerated hydrogels without chemical reagents. By adding carbon-based fillers during calendering, they converted recycled hydrogel material into photothermal composite hydrogels for solar-driven interfacial evaporation.

Briefly, the researchers applied coupled shear and compressive forces as the hydrogel passes through counter-rotating rollers. This processing choice is central to the scientific logic of the paper: shear promotes directional chain alignment, while compression reorganizes and densifies the network without relying on chemical cross-linking or high-temperature treatment. After calendering, the material is rehydrated, producing anisotropic hydrogels whose structure and properties depend on the roll gap. They also found  original freeze-thawed gel possessed a more random porous network and lower optical transparency, whereas the strongly calendered AH-0.3 hydrogel became highly transparent, with high visible-light transparency in the visible range. This optical change was not treated as a cosmetic observation; it reflected a more homogeneous internal architecture with fewer scattering centers. When AH-0.3 was loaded parallel to the aligned PVA chains, it deformed less than when loaded perpendicular to that orientation, confirming that calendering had imposed a mechanically meaningful anisotropy.

The authors conducted scanning electron microscopy which showed the transition from the heterogeneous porous morphology of the freeze-thawed hydrogel to a progressively aligned lamellar architecture in AH-0.6 and AH-0.3. Small-angle X-ray scattering supported this interpretation: the freeze-thawed hydrogel displayed isotropic scattering, while the calendered materials showed increasingly elliptical patterns and angular intensity peaks consistent with uniaxial chain orientation.

The team noticed the structural changes were not a simple increase in crystallinity and in fact, calendering increased the long period between crystalline domains and reduced crystallinity from 4.7 wt % in the wet freeze-thawed hydrogel to 3.4 wt % in AH-0.3. That point is important because it separates this strategy from conventional densification-based strengthening. The mechanical forces partially disrupted crystalline domains and increased interdomain spacing, while still promoting chain alignment and directional hydrogen-bonding organization. ATR-FTIR analysis supported the evolution of hydrogen bonding during gelation and calendering. The result was a network that combined aligned load-bearing features with water accessibility rather than sacrificing hydration for crystallinity.

The authors reported that AH-0.3 tested parallel to the alignment direction reached a tensile strength of 3.20 ± 0.77 MPa while retaining 90.2 wt % water. The perpendicular direction showed lower strength and stiffness, consistent with an anisotropic architecture rather than a uniformly densified network. A comparison with dry-annealed hydrogels clarified the role of dehydration. Although annealing improved strength and toughness by promoting network densification, the dry-annealed samples contained less water and did not match the combined strength, stiffness, and hydration achieved through calendering-induced alignment. They also performed fatigue and cyclic loading experiments  and showed that the fatigue threshold increased sharply after calendering, and AH-0.3 resisted crack propagation during extended cyclic loading. Under repeated stretching, AH-0.3 also showed inverse work-hardening, with increasing maximum stress over repeated cycles. The aligned nanofibrillar architecture appears to provide chain slippage, interfibrillar friction, crack deflection, and load redistribution, giving the material a way to dissipate energy while preserving its network integrity. They extended the same calendering concept to recycling and found that mixed fragments of freeze-thawed and anisotropic hydrogels were reprocessed into recycled PVA membranes and rehydrated into regenerated hydrogels. The first recycled hydrogel reached 4.03 ± 0.24 MPa tensile strength and 2.48 ± 0.55 MJ m−3 toughness, matching or exceeding the pristine anisotropic hydrogel in several measures Later cycles retained useful performance after some reduction in mechanical properties, indicating that the process can reconstruct a functional hydrogel network from damaged material. The researchers also incorporated carbon black, expanded graphite, and acid-treated carbon nanotubes into recycled PVA matrices. These composite hydrogels absorbed broadly across the solar spectrum and, in solar-driven interfacial evaporation tests, showed the strongest evaporation performance among the tested composite hydrogels.

The findings of Professor Jiefeng Gao and colleagues have several engineering applications, especially where hydrogels must remain highly hydrated while also tolerating mechanical handling, repeated deformation, or long service conditions.  By using low-temperature green calendering to align PVA chains and create a lamellar anisotropic network, the authors showed that a hydrogel can retain very high water content while achieving substantially improved tensile strength, fracture resistance, and fatigue tolerance. This is relevant to engineered soft components that must deform repeatedly without rapid structural failure, such as soft robotic elements, flexible actuators, hydrogel-based joints, and load-bearing soft interfaces.

A second application is in biomedical and tissue-related engineering. The paper specifically links high water content to the need to mimic hydrated physiological environments, while the improved strength and fatigue threshold make the material more suitable for mechanically active settings. Hydrogels intended for artificial cartilage, soft implants, tissue scaffolds, or wearable biomedical devices often need both hydration and durability. The calendered PVA hydrogels offer a design route in which stiffness and strength can be enhanced directionally, rather than by simply drying or over-densifying the material. This matters because many biological tissues are anisotropic; they resist load differently depending on direction. A hydrogel with tunable directional mechanics could therefore be useful when engineers want soft materials that better match tissue-like mechanical behavior. The work also has practical implications for sustainable hydrogel manufacturing. The calendering process is solvent-free, low-temperature, and relatively rapid, which makes it attractive for scalable production compared with multistep or solvent-intensive fabrication methods. More importantly, damaged or discarded PVA hydrogels can be reprocessed into regenerated hydrogels with useful mechanical performance.  Another application is environmental water treatment. By incorporating carbon-based fillers such as carbon black, expanded graphite, and acid-treated carbon nanotubes into recycled PVA hydrogels, the authors produced photothermal composite hydrogels for solar-driven interfacial evaporation. These materials combine water transport, broadband solar absorption, and porous hydrogel architecture, making them relevant to solar desalination and sustainable water harvesting systems. The value of the study is practical: the same calendering process can strengthen the hydrogel, recycle damaged material, and add functional fillers for water-evaporation applications.

Global Navigation Satellite System precise point positioning with ambiguity resolution (PPP-AR) relies on the accurate treatment of hardware-dependent code and carrier-phase delays originating from satellites and receivers. These delays vary across frequencies and signal channels and can destroy the integer properties of carrier-phase ambiguities if they are not properly corrected. Observable-specific bias (OSB) products address this problem by providing satellite- and signal-specific corrections for raw code and phase observations, enabling users to recover integer ambiguities and flexibly process observations from multiple frequencies and constellations. OSB products have therefore become a critical component of modern multi-GNSS PPP-AR services, with their quality directly affecting ambiguity estimation, validation, fixing, positioning convergence, and solution continuity. However, OSB products may contain discontinuities, abnormal fluctuations, missing data, and structured periodic variations arising from datum definitions, clock-reference conventions, satellite operations, and daily processing procedures. These characteristics are not always fully revealed by evaluating positioning accuracy alone.

Most previous OSB assessments have focused on PPP-AR performance, particularly positioning accuracy after ambiguity fixing. Although such tests demonstrate whether a product can support successful positioning, they provide limited insight into the intrinsic behavior of the bias series. An OSB product may produce acceptable positioning results over a short period while still containing day-boundary jumps, unstable intervals, or periodic variations that affect long-term processing and operational reliability. A more comprehensive assessment is therefore required to characterize the continuity, variability, internal consistency, and spectral behavior of OSB products and to support product screening, quality control, adaptive weighting, anomaly detection, and ambiguity recovery after data interruptions.

In a recently published paper in Advances in Space Research, Ge Ge, Zhetao Zhang, Haijun Yuan, and Huaqing Xu established a unified time-space-frequency framework for the comprehensive evaluation of code and phase OSB products. The framework moves beyond conventional positioning-based validation by examining approximately one year of rapid and final OSB series covering GPS, Galileo, BDS-2, and BDS-3. In the time domain, daily stability tests, box-plot distributions, and peak-to-peak variations are used to reveal discontinuities, fluctuations, and long-term stability. In the spatial domain, standard deviations and Pearson correlation coefficients characterize the dispersion and common variation patterns of OSB series across satellites and signals. In the frequency domain, fast Fourier transform analysis identifies periodic components and structured spectral signatures that cannot be readily distinguished through time-domain inspection alone.

Dr. Zhetao Zhang and his colleagues analyzed OSB products from day of year 060 in 2023 to day of year 060 in 2024. They found that most satellites showed high data availability, although interruptions remained in some series. Rather than smoothing out discontinuities caused by reference changes, daily reinitialization, and other practical processing operations, the research team retained these features so that the evaluated series could reflect actual operational behavior. Their results showed that code OSBs were generally more stable, with relatively limited peak-to-peak variations, whereas phase OSBs exhibited wider ranges, stronger fluctuations, and more visible discontinuities. Dr. Zhang and his colleagues also identified several satellite-specific variations, demonstrating that orbital maneuvers, signal characteristics, and processing events may introduce localized anomalies that require targeted monitoring rather than relying solely on product-wide quality indicators.

The spatial- and frequency-domain analyses conducted by Dr. Zhetao Zhang and his colleagues provided further insight into the internal behavior of OSB products. The team found that phase OSBs showed greater dispersion and more complex relationships than code OSBs. Code-bias series generally exhibited clearer common variation patterns, whereas phase-bias series showed weaker correlations and stronger satellite-, signal-, and processing-dependent characteristics. After reducing the influence of missing data through interpolation, the researchers also identified low-frequency draconitic patterns and periodic components associated with orbital resonance. Distinct spectral peaks appeared in some medium Earth orbit bias series, together with low-frequency harmonics. These findings demonstrate that OSB fluctuations are not purely random but may contain structured and physically meaningful periodic components. By revealing such hidden signatures, the work of Dr. Zhang and his colleagues enables monitoring systems to distinguish recurring behavior from isolated anomalies, abrupt product changes, or processing failures.

The findings of Dr. Zhetao Zhang and his colleagues have direct implications for the development of more reliable multi-GNSS PPP-AR services. Phase OSBs are essential for restoring the integer properties of carrier-phase ambiguities, and undetected jumps or unstable corrections may contaminate ambiguity estimates, delay successful fixing, trigger unnecessary reinitialization, or increase the risk of incorrect validation. The multi-domain indicators proposed by the research team provide complementary tools for addressing these risks: time-domain measures can detect jumps and unstable intervals before ambiguity fixing, spatial-domain measures can support satellite- and signal-specific weighting or screening, and frequency-domain characteristics can separate structured periodic behavior from abnormal events. Together, these capabilities provide a foundation for real-time OSB monitoring, adaptive stochastic modeling, ambiguity management, and automated quality control. Although the study focused on OSB product characteristics rather than directly quantifying positioning improvements, the framework developed by Dr. Zhang and his colleagues establishes a clear connection between product-level quality and PPP-AR performance. More stable, continuous, and well-characterized OSB corrections can improve ambiguity-estimation consistency, reduce unnecessary ambiguity resets, support faster recovery of fixed solutions, and ultimately contribute to shorter convergence times, higher ambiguity-fixing reliability, and more continuous centimeter-level positioning. Overall, the study transforms OSB assessment from a simple positioning-result check into a multi-domain product-quality diagnosis, providing an important methodological foundation for more intelligent OSB selection, monitoring, and adaptive use in next-generation multi-GNSS PPP-AR services.

Pecan oil has high proportion of unsaturated fatty acids. It can be processed using two commercially relevant processing methods: cold pressing and hot pressing which can shape the composition, sensory character, and storage behavior of the resulting oils. Cold-pressed pecan oil retains more functional constituents and is associated with favorable color and acid-value characteristics, while, hot pressing involves roasting or baking before extraction and produces an oil with a more developed roasted flavor profile. However, pecan oil remains vulnerable to oxidative deterioration during storage with light, oxygen, moisture, temperature, and the chemical nature of the lipid phase can all contribute to progressive rancidity, which alter sensory quality and reduce the stability of valuable oil constituents. Assessing oil oxidation outside a laboratory remains difficult. Peroxide value, acid value, p-anisidine value, thiobarbituric acid reactive substances, conjugated dienes, and related indices can provide useful information about oxidative change, but many require reagents, controlled procedures, trained personnel, or analytical instrumentation. Such methods are well suited to formal quality assessment, although they are less convenient when the practical question is simply whether an oil has remained within an acceptable oxidation range during storage. Sensory assessment can address that question to some extent, particularly because rancidity changes odor character. Its interpretation, however, depends on panel training and prior familiarity with the sensory features of fresh and oxidized oils. A further complication arises from the chemistry of lipid deterioration itself. Peroxide value reflects primary oxidation products, whereas the volatile compounds that shape rancid odor are often secondary products produced as hydroperoxides decompose. The relation between chemical oxidation indices, volatile aldehydes, and sensory change therefore needs to be clarified before a simple visual detection strategy can be justified. Acid value may also remain relatively stable even when oxidative deterioration is advancing, making it less suitable as a sole marker for shelf-life assessment in this particular system.

In a recently published paper in Molecules, Dr. Li Cui, and Dr. Haijun Zhu of the Jiangsu Academy of Agricultural Sciences, developed a visual filter-paper sensor to assess oxidation in cold-pressed and hot-pressed pecan oils. The research team began with storage experiments conducted at room temperature, 40 °C, 50 °C, and 60 °C. They selected peroxide value as the principal quality index because acid value changed little during accelerated oxidation and because the authors had previously observed expired pecan oil samples whose acid values did not exceed the relevant national standard. The peroxide-value data for both cold-pressed pecan oil and hot-pressed pecan oil were fitted to first-order kinetic models. They found that, across the tested temperatures, the coefficients of determination ranged from 0.9183 to 0.9841, indicating that the time-dependent increase in peroxide value was described effectively by the first-order kinetic model.

The authors found that storage temperature influenced the rate constant in both oils, with higher temperatures accelerating peroxide formation. Incorporation of the Arrhenius relationship then enabled shelf-life prediction using the national peroxide-value limit of 0.25 g/100 g as the endpoint. The predicted and measured shelf-life values agreed closely and for instance the correlation between predicted and measured shelf life for  cold-pressed oil reached 0.9993, while hot-pressed oil gave a correlation of 0.9866. Also at room temperature, cold-pressed oil reached the peroxide-value limit after 330 days, compared with 100 days for hot-pressed oil. Sensory evaluation linked these chemical changes to the odor characteristics of the oils. The team reported fresh samples to have stronger nut aroma and lower oxidation-associated notes, whereas expired samples were characterized by diminished nut aroma together with stronger oxidized hala flavor and stimulating taste. The pattern differed somewhat between cold-pressed and hot-pressed oils, reflecting their different original sensory profiles. Hot-pressed samples contained roasted notes associated with their processing history, while cold-pressed samples retained a more direct nut-related aroma. The authors treated sensory analysis as a qualitative screening tool rather than a quantitative measurement, since its outcome remained dependent on evaluator training and subjective interpretation.

The team conducted volatile-compound analysis which provided the chemical bridge between peroxide-value increase and the proposed visual sensor. Headspace solid-phase microextraction coupled with gas chromatography-mass spectrometry identified 55 volatile compounds across fresh and expired cold-pressed and hot-pressed oils. Aldehydes emerged as the key oxidation-related group. Hexanal increased in both expired oil types, while compounds such as (Z)-2-heptenal, nonanal, (E)-2-octenal, and (E,Z)-2,4-decadienal also differed substantially between fresh and expired samples. Hexanal and (Z)-2-heptenal showed positive correlations with peroxide value in both oils. Thus, the analytical strategy linked peroxide formation to the accumulation of aldehydes that could be targeted chemically.

The investigators impregnated filter paper with Congo red and hydroxylamine sulfate, then dried to yield a color-sensitive material. Hydroxylamine sulfate reacts with aldehydes to form aldoximes and sulfuric acid. The resulting decrease in pH changes Congo red from its red azo form toward a purple-blue quinone-associated form. This design choice gave aldehyde accumulation a visible consequence: as oxidation-generated aldehydes increased, the paper sensor shifted from red toward purple-blue. Moreover, they optimized the sensor preparation conditions through variation of the filter-paper immersion time in the aldehyde-sensitive solution. A 150-minute immersion produced a sufficiently bright red starting color and was selected for fabrication. For testing, the sensor was fixed inside the cap of a sealed vial containing pecan oil, without direct contact between the paper and the oil, and exposed to the headspace at 35 °C for 24 hours. Fresh cold-pressed and hot-pressed oils retained a red sensor appearance, whereas expired oils produced the purple-blue transition.

The filter-paper sensor developed for pecan oil in the new study provides a simple approach for translating chemical deterioration into an immediately visible quality signal.     A user does not need to handle the oil directly, perform titration, or interpret chromatographic data. This format could be incorporated into small-scale pecan oil packaging and quality-control workflows. For producers handling cold-pressed and hot-pressed oils, the different a* thresholds identified for the two products provide a basis for processing-specific interpretation. Cold-pressed oil was classified as expired when a* reached 11 or lower, while hot-pressed oil reached the corresponding condition at 15 or lower. The distinction matters because the oils did not display identical oxidation behavior or initial volatile profiles. A single visual method could therefore be adapted without treating all pecan oils as chemically equivalent.

The sensor also has value during accelerated storage trials and product-development work. Conventional peroxide-value measurement remains necessary when a quantitative oxidation profile or kinetic model is required. Yet the paper strip offers a convenient complementary screening tool for identifying samples that have moved toward the regulatory peroxide-value limit. In the reported experiments, the obvious red-to-purple-blue color shift of the test paper visible to the naked eye occurs at a peroxide value above 0.25 g/100 g. Slight color deviation can be detected by a colorimeter when the peroxide value reaches approximately 0.20 g/100 g, yet the test paper still appears red to human vision at this concentration, which can serve as an early warning range before reaching the national standard limit. The sensing layer uses ordinary filter paper rather than a more elaborate film substrate, with Congo red and hydroxylamine sulfate providing the functional chemistry. Preparation requires immersion, drying, and storage under dry conditions, while actual measurement involves headspace incubation at 35 °C for 24 hours. That separation between one-time fabrication and later use supports batch preparation of sensors for repeated testing. This connection between oxidation chemistry and visible response gives the sensor a clear basis for practical pecan-oil quality screening.

Magnetic random access memory is built around the magnetic tunnel junction, in which two ferromagnetic layers separated by an ultrathin insulating barrier produce different resistance states when their magnetizations are parallel or antiparallel. In the spin-transfer-torque version of this technology, the written state is changed by a spin-polarized current that transfers angular momentum to the free magnetic layer. The main difficulty is the need to satisfy two requirements that tend to pull the device in opposite directions. Reliable data retention requires a large thermal stability factor, which is usually obtained by increasing the effective perpendicular magnetic anisotropy of the free layer. A larger anisotropy helps the stored magnetic state resist thermal fluctuation. At the same time, however, stronger anisotropy reduces the effectiveness of the spin current in driving magnetization reversal. That increase is not a minor device-level inconvenience, because a large current can place additional electrical stress on the thin MgO tunnel barrier. Dual-free-layer magnetic tunnel junctions offer a more refined way to approach this problem. If the layers are coupled too strongly, they behave almost like one thicker free layer and reverse coherently. If they are coupled too weakly, one layer may reverse without producing the desired full switching of the magnetic state. Between these limits, however, the two layers may reverse sequentially, allowing one layer to initiate reversal before the other follows.

The unresolved issue is therefore not whether two free layers can be placed in a magnetic tunnel junction, but how their coupling controls the actual reversal path. In a recently published research paper in Advanced Electronic Materials Professor Shujun Ye and Professor  Koichi Nishioka from Beijing Institute of Technology developed a theoretical analysis of spin-transfer-torque switching in magnetically coupled dual free layers with unequal perpendicular magnetic anisotropy. They derived switching conditions that separate weak, intermediate, and strong coupling regimes and classify the resulting reversal modes into FL1-only, sequential, simultaneous incoherent, and coherent switching, as shown in Figure 1.

Ye and Nishioka analysis treated the free layer as two ferromagnetic layers, FL1 and FL2, separated by a thin MgO spacer. Both layers have their easy axes along the out-of-plane direction and are assigned the same saturation magnetization, but FL1 is given lower effective perpendicular magnetic anisotropy than FL2. That asymmetry matters because it makes FL1 more susceptible to spin-transfer-driven reversal. It also allows the model to test whether the motion of the softer layer can initiate or assist the reversal of the more stable layer.

Magnetic coupling is represented through a positive coupling energy, Jcpl, with the associated coupling field determined by the layer magnetization and thickness.  In the calculations, the researchers varied Jcpl while keeping the basic layer structure and material assumptions fixed.

The authors solved the Landau-Lifshitz-Gilbert equations under spin-transfer torque led to a classification of the reversal behavior into four switching phases. Under weak coupling, FL1 can reverse while FL2 remains unchanged (Phase1). With a higher write current, FL1 reverses first and FL2 follows (Phase2). At still higher current, the two layers begin reversing at the same time but remain magnetically incoherent (Phase3). Under strong coupling, FL1 and FL2 reverse coherently (Phase4). This classification is not only descriptive; it follows from threshold current densities for each layer, which depend on the effective anisotropy fields and the coupling field. The critical write current showed a clear dependence on Jcpl. As coupling increased from zero, the critical current first decreased, reached a minimum, and then rose again until it saturated in the strong-coupling regime. Across the anisotropy values examined, the minimum critical current remained close to half of the strong-coupling value and occurred in the sequential switching regime, not the coherent regime.

The team performed switching-time analysis and found a moderately coupled dual free layer required less write current than a strongly coupled one over both long and short switching-time ranges. The reason lies partly in the unequal spin-current amplitudes of the two layers. Because FL1 has the smaller effective anisotropy field, the spin current acts more effectively on it, so FL1 reverses more readily and can complete much of its motion before FL2 has moved far from its original direction. In this way, the softer layer does more than switch first; it changes the magnetic condition under which the second layer begins to reverse. The energy-surface analysis gives the clearest physical explanation for the current reduction. The magnetic energy contains the anisotropy energy of each free layer together with the coupling term between their magnetization directions. Under moderate coupling, the switching path develops two energy peaks: one associated with FL1 reversal and another associated with FL2 reversal. Between them lies an intermediate magnetic state that lowers the effective barrier for completing the second step. Strong coupling removes this separated pathway and produces a single coherent barrier. The lower effective switching barriers in the separated reversal process explain why sequential or incoherent switching can operate at lower current than coherent reversal.

The authors’ findings are most directly relevant to the engineering design of perpendicular STT-MRAM cells, where the free-layer stack must be tuned to satisfy both write efficiency and retention requirements.  The more useful engineering target is a controlled magnetic coupling strength that allows FL1 to reverse first and then assists the reversal of FL2. In that regime, the write current can be substantially reduced while the coupled structure still preserves a high thermal stability factor.

This has practical meaning for MTJ stack design and the magnetic coupling energy between FL1 and FL2 becomes a design parameter. Since the total coupling can include magnetostatic and interlayer exchange contributions, engineers can use layer thickness, spacer quality, interface design, and magnetic geometry to adjust the coupling field.

For scaled memory devices, the reported current reduction is especially important. The calculations show that an optimized coupling strength can bring the critical write current to about half of the strong-coupling coherent-switching value. Lower write current would reduce electrical stress on the MgO tunnel barrier and may support better write endurance in dense STT-MRAM arrays. At the same time, the calculated thermal stability factors at the minimum-current coupling condition remain high, including values above 128 for 30 nm devices. This suggests that current reduction does not have to come at the expense of data retention, provided that the coupling is selected within the proper regime.

The findings of Ye and Nishioka also provide a practical interpretive tool for device optimization. Instead of treating switching behavior as a single threshold event, the free-layer reversal can be classified into FL1-only, sequential, simultaneous incoherent, and coherent modes. This classification can guide how experimental MTJ stacks are evaluated: a low write current is most meaningful when it corresponds to complete sequential reversal rather than partial switching. In this sense, the work gives MRAM engineers a clearer design logic for using dual free layers in low-current, thermally stable STT-MRAM cells.

Nickel-based single-crystal superalloys operate in components exposed simultaneously to mechanical loading, high temperatures, and oxidation. Performance depends on the γ/γ′ microstructure: a Ni-rich γ matrix containing several solid-solution elements surrounds γ′ precipitates based principally on Ni₃(Al,Ta) and this two-phase arrangement contributes to the alloy’s high-temperature mechanical behaviour, but it also creates a chemically heterogeneous surface when the material is exposed to an oxidizing atmosphere. Local differences in Al, Ni, Co, Cr, Ta, and other alloying elements can influence which oxides form first, where oxygen penetrates, and how the surface reaction zone develops with time.

Oxidation becomes especially important when a protective coating cracks, peels away, or is otherwise damaged, leaving the underlying alloy directly exposed. The oxide scale that forms under such conditions is not a passive and uniform reaction product. It develops through coupled transport of oxygen inward and alloying elements outward, while the phases within the scale may themselves change as oxidation proceeds. After prolonged exposure near 1100 °C, the oxide layer of Ni-based single-crystal superalloys is often described in terms of an outer Ni–Co-rich oxide, intermediate complex oxides, and an inner Al₂O₃-rich region. Such descriptions establish the final layered morphology, but they do not reveal how the individual oxide layers developed over time. The mature scale records the outcome of oxidation, not the sequence of structural and chemical changes that produced it. Early oxidation involves surface disordering, selective oxidation of alloying elements, diffusion through chemically distinct γ and γ′ regions, and reactions among oxides that may already be present. Aluminium has a strong tendency to oxidize, but its behaviour must be considered together with the availability and mobility of Ni, Co, Cr, and Ta, as well as the local oxygen concentration. The γ and γ′ phases add further complexity because γ′ contains more Al and may respond differently to oxygen exposure, whereas the γ matrix can provide pathways for oxygen to penetrate beneath the immediate surface.

Earlier studies have proposed different sequences for the earliest stages of oxidation. Some suggest that Al₂O₃ forms first, followed by development of the Ni–Co-rich surface oxide, whereas others indicate that Ni and Co may react directly with oxygen as exposure begins. The respective roles of the γ and γ′ regions during this early period have also remained unclear. Clarifying the process requires observations that track the evolving microstructure over short oxidation times instead of inferring the sequence solely from the final oxide scale.

In a recently published research paper in Corrosion Science, Dr. Zhiqiang Zhou, Professor Pan Xie, Professor Cuilan Wu, and Professor Jianghua Chen from Hunan University and Hainan University examined how the oxide scale forms on a nickel-based single-crystal superalloy. Their analysis separates the early solid–gas oxidation of alloying elements from later solid–solid reactions among the oxides already present. It also identifies γ channels as preferred routes for inward oxygen diffusion and explains why secondary Al₂O₃ forms before the intermediate NiAl₂O₄, CoCr₂O₄, and CrTaO₄ layers.

The researchers performed alloy oxidation in air at 1100 °C for periods ranging from 10 s to 1 h. The longest exposure established the stable oxide sequence from the external surface inward: (Ni₀.₉Co₀.₁)O, NiAl₂O₄, CoCr₂O₄, CrTaO₄, and α-Al₂O₃. Atomic-resolution imaging and compositional mapping identified the crystal structures and dominant elemental distributions within each layer.   They found after only 10 s, both γ and γ′ surface regions were covered by a thin, continuous amorphous-like oxidation product.   By 15 s, Ni-rich and O-rich nanoparticles had appeared at the outer surface, while an internal oxidation front beneath them showed Al and O enrichment together with Ni depletion. That compositional separation already indicated two linked processes: outward formation of a Ni-rich oxide and inward penetration of oxygen into the near-surface alloy.

The authors also found at 60 s, the developing scale had acquired a bilayer character. An outer Ni–Co oxide layer lay above an ultrafine-grained primary Al₂O₃ layer. Both γ and γ′ regions participated, although the Al-rich γ′ regions oxidized more rapidly.  Higher Al availability in γ′ favoured faster formation of alumina, whereas the γ channels carried oxygen more deeply into the substrate. The γ/γ′ microstructure therefore influenced not only local oxide composition but also the route by which oxygen reached regions below the initial surface reaction zone. The team performed elemental line scans to understand the sequence within the two phases and noticed in γ regions, Ni and Co first migrated toward the surface and formed the outer Ni–Co oxide. The region below became enriched in Al and O before converting more slowly toward alumina. In γ′ regions, the same outer oxide appeared first, followed by more rapid transformation into Al₂O₃. Oxygen concentration was higher within γ channels than in adjacent γ′ regions, supporting the interpretation that γ channels acted as preferred inward diffusion paths.

This diffusion route became especially consequential after 120 s. A second Al₂O₃ layer appeared at depth, separated from the primary Al₂O₃ layer by a remaining slice of unoxidized alloy. Its position showed that oxygen had travelled through γ channels and reacted with Al in deeper γ′ blocks before the intermediate oxide layers had fully developed. The secondary alumina layer contained chiefly Al and O, whereas the earlier primary alumina layer retained a more mixed chemical character, including contributions from Ta, W, Co, Cr, and other alloying elements.

The primary Al₂O₃ layer then evolved into three intermediate sub-layers. At this stage, a thin NiAl₂O₄ layer appeared near the boundary with the outer Ni–Co oxide. Within the primary alumina, Ta-rich oxide particles were identified as Ta₂O₅, while α-Al₂O₃ remained the dominant phase. With continued oxidation to 30 min, reactions among the simple oxides produced stable NiAl₂O₄, CoCr₂O₄, and CrTaO₄ layers. The final five-layer architecture therefore developed through overlapping oxidation and solid-state transformation processes, not through a single outward-to-inward sequence.

The authors’ findings provide a more specific basis for evaluating oxidation resistance in nickel-based single-crystal superalloys under high-temperature exposure. The mature oxide scale should be considered in relation to the local γ/γ′ arrangement, the availability of Al-bearing regions, and the paths available for oxygen transport. The observation that γ channels promote inward oxygen diffusion is especially relevant when interpreting subsurface oxidation.

According to the authors, this behaviour matters for alloy microstructure design and for post-exposure assessment of service components. A surface oxide scale may appear continuous at low magnification while the substrate beneath it has already undergone internal chemical redistribution. The formation of a γ′-free region below the secondary alumina layer shows that oxidation can alter the near-surface microstructure beyond the visible external scale. For components exposed after coating cracking or local coating loss, inspection strategies may therefore need to consider both the oxide layer and the modified alloy region immediately below it. The distinction between primary and secondary Al₂O₃ formation also has value for interpreting oxidation kinetics.   These two alumina regions arise through different local conditions even though both are Al-rich oxides. Assessments based only on the presence or thickness of alumina may not fully capture how oxygen has moved through the underlying microstructure and where Al has been consumed.

The later emergence of NiAl₂O₄, CoCr₂O₄, and CrTaO₄ further indicates that intermediate oxide layers should be understood as products of reactions among earlier simple oxides.   This provides a basis for interpreting oxide scales formed after different exposure durations, because the absence of a mature complex oxide layer does not necessarily indicate limited oxidation. For materials development, the refined scenario offers a route for linking alloy chemistry and γ/γ′ morphology to oxidation-layer evolution.  Elemental diffusion rates, local oxygen concentration, and phase-specific composition act together in determining which oxide products appear, where they form, and when they transform.

Flexible piezoelectric sensors are becoming increasingly important in intelligent electronic systems because they convert mechanical contact, deformation, or vibration directly into electrical signals. When arranged as spatially resolved arrays, piezoelectric elements can also register where pressure occurs, how it is distributed, and how a contact pattern evolves across a surface. The electrical response of a flexible polymer sensor depends closely on molecular order, local stress concentration, device geometry, and the fidelity with which individual sensing sites can be addressed. Poly(vinylidene fluoride), or PVDF, is well suited to this purpose because it combines flexibility, chemical stability, and piezoelectric activity. Its strongest piezoelectric response is associated with the electroactive β-phase, where molecular chains adopt an all-trans conformation and the dipoles are arranged in a more favorable orientation. Generating a substantial β-phase fraction without sacrificing the sensor’s ability to distinguish small, localized forces requires careful control of material organization and device architecture. Fillers can promote molecular ordering and interfacial polarization, but their effect depends strongly on exfoliation, dispersion, loading level, and interaction with the polymer. A filler that remains poorly dispersed or aggregates within the matrix can interrupt rather than reinforce the structural organization needed for an effective piezoelectric response.

Two-dimensional tungsten disulfide provides a useful material system for addressing this problem. Few-layer or odd-layer WS₂ possesses intrinsic piezoelectricity because the broken inversion symmetry permits polarization under deformation, and sulfur atoms on its surface can interact electrostatically with PVDF chains. However, achieving these effects simultaneously requires processing conditions capable of transforming bulk WS₂ into well-dispersed nanosheets and preserving their orientation during device fabrication.

In a recently published research paper in Composites Part B: Engineering  Mr. Xinwen Zhou, Dr. Haoran Pei, Mr. Zhicheng Li, Mr. Haihao He, and led by Professor Yinghong Chen from State Key Laboratory of Advanced Polymer Materials set at Sichuan University and also Polymer Research Institute of Sichuan University developed a PVDF/WS₂ nanosheet micro-pyramid array sensor fabricated through solid-state shear milling followed by microinjection molding. Its technical distinction lies in using shear processing both to exfoliate and activate WS₂ within PVDF and to orient the resulting nanocomposite during formation of the microstructured sensing array. A signal-processing and convolutional-neural-network workflow translated the array outputs into pressure maps for letter recognition.

The research team first mixed bulk WS₂ with PVDF and subjected to solid-state shear milling and this process imposed intense three-dimensional shear fields that exfoliated the original particles into few-layer nanosheets and, in part, smaller quantum dots. Electron microscopy showed that most nanosheets contained fewer than ten layers and were oriented along the polymer-flow direction. The authors performed X-ray photoelectron spectroscopy and identified a C–S signal after milling, indicating mechanochemically induced bonding between PVDF chains and the sulfur-containing WS₂ surface. Molecular-dynamics simulations complemented this observation by showing progressive adsorption of PVDF chains toward the nanosheets through electrostatic interaction. That association encouraged closer interfacial contact and favored the transition of PVDF chains toward the all-trans configuration characteristic of the β-phase. Temperature-programmed infrared measurements supported this interpretation: the composite containing 1 wt% WS₂ displayed a higher β-phase content during cooling than neat PVDF.

Milling initially promoted crystallization, but excessively prolonged treatment reduced crystallinity because repeated shear could disrupt molecular regularity and introduce imperfect crystalline regions. Likewise, increasing WS₂ content provided additional nucleation sites, yet excessive nanosheet loading led to aggregation and reduced molecular orientation. The 1 wt% composition, designated MWP1, emerged as the most structurally favorable balance. It contained well-exfoliated nanosheets, showed strong orientation along the flow direction, and developed a compact shish-kebab crystal morphology with long shish structures and relatively small lateral lamellar dimensions.

The authors also found using finite-element analysis that the polymer melt experienced very high shear rates during filling, while rapid cooling limited subsequent relaxation. Under these conditions, PVDF chains stretched along the melt-flow direction and formed oriented shish-kebab crystals. The design choice of combining a low WS₂ loading with high-shear microinjection molding therefore had a clear scientific consequence: exfoliated nanosheets could interact with and orient PVDF chains without the aggregation that would interfere with flow-induced crystal organization. The resulting micro-pyramid array consisted of regularly formed pyramids spaced 1 mm apart across a 10 mm sensing area. The team showed using simulations that taller pyramids produced greater local strain and electrical potential under compression, which guided the selection of the array geometry. Moreover, they performed piezo-response force microscopy which revealed striped regions of high piezoelectric activity aligned with the flow direction and this is consistent with oriented β-phase crystals and WS₂ nanosheets. For MWP1, the local longitudinal piezoelectric coefficient measured by piezo-response force microscopy was −24.9 pm/V.

Under periodic mechanical impact, the 1 wt% WS₂ formulation produced the strongest piezoelectric response, reaching an open-circuit voltage of 15.2 V. The sensor achieved a sensitivity of 128 mV/kPa below 10 N, placing its strongest response in the low-force range associated with light pressing. Sensitivity declined at higher loads as deformation of the pyramids reduced their effective height and increased stiffness. Stable output was maintained over 4000 impact cycles. Fourteen conductive channels addressed 37 sensing units through intersecting row and column connections. Signals produced by miniature “S,” “C,” and “U” molds were filtered, subjected to peak extraction, interpolated into two-dimensional pressure maps, and analyzed with a LeNet-5 convolutional neural network. The trained network reached 100% classification accuracy for the three tested letter patterns.

The PVDF/WS₂ micro-pyramid array developed in this study by Professor Yinghong Chen  and colleagues is suited to interfaces that must detect light contact, resolve its location, and convert pressure patterns into control signals. Its strongest sensitivity occurs below 10 N, making it relevant to finger pressing and tactile interaction. Spatially differentiated piezoelectric signals allow the array to operate as an active tactile interface rather than a single-point pressure switch. One direct application proposed by the authors is secure authentication for intelligent electronic devices. The sensor array can translate a prescribed pressing pattern into a distributed electrical signature, which is then processed and compared with patterns stored in a microcontroller unit. The study discusses possible integration with unmanned aerial vehicles, quadruped robots, humanoid robots, laptops, and related smart devices. By assigning active and inactive states to sensing units through an intensity threshold, the authors calculate a large theoretical combination space within a compact sensing area. They presented this security configuration as a conceptual design, but it establishes a clear engineering direction for compact tactile password systems.

Robotic tactile sensing represents a second application area of Professor Yinghong Chen and team work. A micro-pyramid array could be incorporated into a robotic finger, gripper surface, or human-operated robot interface where light pressing must be detected with positional detail. The individual sensing units generate independent signals, allowing the system to reconstruct contact distributions rather than only measuring total force. Combined with filtering, interpolation, and convolutional-neural-network classification, this capability could support the interpretation of deliberately applied shapes or control patterns. The demonstrated recognition of three molded letters illustrates the underlying principle: localized deformation of the array becomes a pressure map that can be identified computationally. The authors also connect the sensor’s low-pressure response and small size to wearable electronics and physiological monitoring. Signals from arterial pulse waves, respiration, and skin-contact events fall within the type of mechanical input the device is intended to detect. In such settings, the flexibility of the PVDF-based structure and its piezoelectric operation could permit sensing without an external power supply at the point of measurement. A further practical role lies in low-level energy harvesting. Under repeated impacts, the array charged a capacitor through a bridge rectifier, reaching 1.4 V within 100 seconds at 5 Hz. This does not replace the sensor’s primary role in tactile recognition, but it shows that the same mechanical events used for sensing can also provide a small electrical output. In integrated intelligent systems, that dual function may be useful where pressure sensing, pattern recognition, and limited self-powered operation are desired within a single flexible component.

Copper is an important engineering material because it combines efficient electrical and thermal transport with useful structural integrity. Its high electrical and thermal conductivity make it suitable for components that carry current, remove heat, or operate in thermally demanding assemblies. At the same time, many such components increasingly require geometries that are difficult to produce by conventional subtractive or forming methods. Laser powder bed fusion offers an attractive manufacturing approach in this setting because it can build dense metal parts directly from powder while allowing considerable geometric freedom. The main challenge comes from the interaction between copper and the near-infrared lasers commonly used in commercial LPBF systems. Copper reflects much of the incident radiation at these wavelengths, so only a limited fraction of the applied energy is absorbed by the powder bed. Also the high thermal conductivity of copper can bring another challenge because heat spreads rapidly away from the irradiated region, which make it harder to maintain a stable melt pool and reliable fusion between adjacent scan tracks and layers. The result is a narrow processing range in which relatively small changes in laser power or scan speed can shift the material toward lack-of-fusion defects, porosity, spheroidization, or unstable keyhole behaviour.

Several approaches have been explored to improve the LPBF processing of copper. Higher-power near-infrared lasers can increase energy delivery, while shorter-wavelength laser sources can improve absorption. These solutions, however, may require costly equipment or introduce practical limitations related to system capability and laser–material interaction. A different route is to modify the powder itself. Alloying additions can improve printability, but elements that dissolve substantially in copper may also impair electrical conductivity through electron scattering. For applications in which copper is selected primarily for its transport properties, this trade-off is difficult to ignore.

Particle-based modification provides another possibility. A dispersed additive can alter the optical and thermal response of the powder bed while remaining distinct from the copper matrix to a meaningful extent. LaB₆ is of particular interest because of its high absorptivity under near-infrared irradiation and its potential to remain as a second phase in copper. Earlier studies have considered nanoscale LaB₆ additions, but nanoparticles are prone to agglomeration and may reduce powder flowability, complicating powder spreading during LPBF. Their preparation can also be costly.

Micrometre-scale LaB₆ particles offer a potentially more practical alternative, yet their behaviour during copper LPBF has remained insufficiently understood. Whether such particles can improve laser absorption and densification while also influencing microstructural development, mechanical response, and electrical or thermal transport requires direct examination. The present investigation addresses that question by introducing LaB₆ microparticles into copper powder and examining how they affect the printing process and the resulting material.

In a recently published research paper in Journal of Manufacturing Processes Dr. Jinchao Zhao, Professor  Jiabin Liu, Dr.  Liuyi Huang, Professor  Kai Ren, and Professor Yanlong Cao from Zhejiang University developed a 1 wt% LaB₆ microparticle-modified copper feedstock for near-infrared laser powder bed fusion. The powder preparation combined ultrasonic vibration mixing with three-dimensional mixing to distribute LaB₆ across copper particles. During printing, the material produced a dual-scale LaB₆ structure composed of retained larger particles and in situ precipitated nanoscale LaB₆ particles. This combination increased near-infrared absorptivity, broadened the high-density processing window, and strengthened the printed copper without eliminating its high electrical and thermal conductivity.

The researchers prepared the composite feedstock by combining gas-atomized copper powder with irregular LaB₆ microparticles using ultrasonic vibration mixing followed by three-dimensional mixing under argon protection. Elemental mapping showed that the LaB6 particles were distributed across the copper-powder surfaces rather than concentrated in isolated regions. This dispersion was important because the intended effect depended on LaB6 being available throughout the powder bed to interact with incident laser energy. They found that at 1080 nm, adding LaB₆ microparticles increased powder absorbance by 50.4%, changing the densification behaviour during LPBF. Across a substantially broader range of processing conditions than pure copper, the composite achieved relative densities above 99.9%, whereas unmodified copper remained more susceptible to unfused regions and porosity when laser input was insufficient.

The benefit was therefore not confined to a single optimized condition. The authors found that the high-density processing window of the composite was approximately twice as wide as that of pure copper, which indicates greater tolerance to parameter variation during printing. The composite also developed larger melt-pool widths and depths under comparable conditions. By improving laser-energy coupling within the powder bed, LaB6 promoted more stable melting and supported fusion between neighbouring tracks and successive layers.

The team performed microstructural analysis which showed that the initial microparticle population did not behave uniformly during LPBF. Larger LaB₆ particles remained partially or fully unmelted in some regions and were redistributed within the liquid copper by melt-pool flow before becoming embedded on solidification. Their number decreased as volumetric energy density increased, consistent with greater particle melting at higher energy input. At the same time, transmission electron microscopy identified a second LaB6 population: regularly shaped nanoparticles with an average size of approximately 60 nm dispersed in the copper matrix.

This nanoscale population was interpreted as the result of complete melting of smaller LaB₆ particles, dissolution into the liquid copper, and subsequent precipitation during rapid solidification. La and B became supersaturated in the melt and reprecipitated as LaB₆ nanoparticles within grains and at grain boundaries. They found using X-ray diffraction no measurable shift in copper lattice parameters or the formation of new phases, supporting the interpretation that LaB6 remained a second phase rather than producing a substantially altered copper solid solution. The team found that both pure copper and composite samples retained a columnar grain structure aligned with the build direction and the composite showed weaker texture, a higher fraction of low-angle grain boundaries, and greater kernel average misorientation, consistent with increased lattice distortion and dislocation density. These changes were reflected in the tensile response: the LaB₆/Cu composite reached a yield strength of 260 MPa and an ultimate tensile strength of 385 MPa, compared with 127 MPa and 246.5 MPa for LPBF pure copper. Its elongation decreased from 34% to 25.6%, although the composite remained appreciably ductile.

Overall, the findings of Zhejiang University researchers are relevant to copper components that must retain high electrical and thermal transport while being manufactured in geometries that are difficult to obtain through conventional routes. The composite retained 87.4% IACS electrical conductivity and a calculated thermal conductivity of 359 W·m⁻¹·K⁻¹ while providing substantially higher strength than the LPBF pure-copper material.  Electrical components with complex internal or external geometries are one clear area of relevance. Copper is widely used where current must be carried efficiently, but the present results indicate that a modest LaB₆ addition can preserve much of that electrical capability while improving the mechanical response of LPBF-built material.

Thermal-management hardware is another direct application area identified by the paper’s starting context. The retained thermal conductivity supports the use of the material where heat must be transferred through a printed copper body, while LPBF provides access to geometrically intricate forms. Such flexibility matters for designs that depend on internal passages, local thickness variations, integrated mounting features, or compact heat-transfer pathways that would be difficult to machine from bulk copper. The present study does not demonstrate a specific thermal-management device, but it establishes a materials-processing route compatible with that class of component.

The manufacturing implications are equally important and by introducing 1 wt% LaB₆ increased powder absorptivity by 50.4% and expanded the high-density parameter range by more than 1.8 times relative to pure copper. This broader process window gives engineers greater latitude when selecting laser power and scan speed, and it can reduce the sensitivity of production to small parameter variations.

Microparticle-based feedstock modification may also be attractive where powder preparation must remain practical. The paper emphasizes that micrometre-scale particles distribute more readily than nanoparticles and can reduce preparation complexity and cost. Their behaviour during printing is especially useful: larger LaB₆ particles can remain dispersed in the copper matrix, whereas smaller particles melt and reprecipitate as nanoscale LaB₆ during solidification. The resulting dual-scale structure links processability with strengthening, while retaining the conductive character required for electrically and thermally functional copper parts.

Urban rail construction depends on shield tunneling because it allows underground space to be developed beneath active streets, buildings, and infrastructure with comparatively limited surface disruption. However, the method inevitably disturbs the surrounding ground and as the shield advances, excavation, face support, segment erection, and tail void closure interact within a narrow construction window, and even small mismatches between volume loss and compensation can appear later as surface settlement. In densely built ground, that settlement is not just a geometric response of soil; it becomes a serviceability and safety concern for nearby structures, utilities, and transport corridors. Tail grouting sits at the center of this control problem. The annular gap between the excavated soil and the newly installed tunnel lining must be filled by a material that can be transported and injected as a fluid, occupy the tail void effectively, and then develop enough stiffness and strength to restrain subsequent deformation. The scientific difficulty is that the grout is not a material with one fixed performance state. During mixing, pumping, and injection, its liquid properties dominate: density, bleeding rate, fluidity, consistency, and stone rate determine whether it can be placed reliably. After injection, the same material enters a different physical regime, where pressure, soil permeability, water dissipation, consolidation, and cementitious hardening govern compressed deformation and strength development. This liquid-to-solid transition makes grout proportioning more difficult than ordinary empirical mix adjustment. This is important because field performance depends on a balance between pumpability, filling stability, deformation resistance, and strength development. The challenge, therefore, is multi-performance optimization under construction-relevant conditions, not simply maximizing one index. In a recent research paper published in Computer-Aided Civil and Infrastructure Engineering, Dr. Jiaxin Liang, Prof. Wei Liu, Dr. Jingyi Gong, and Prof. Xiaoqiang Dong from Taiyuan University of Technology and Soochow University, working with Dr. Cheng Chen of Suzhou City University and Dr. Chunqing Fu of Beijing Uni-Construction Group Co. Ltd., addressed this problem by developing an explainable intelligent system for shield tunnel tail grout optimization. The system combines experimental liquid and solid performance databases, physics-constrained GAN data augmentation, Bayesian-optimized machine learning, and SHAP interpretation. Its technical distinction is the use of different optimal algorithms for different grout responses rather than a single uniform model. The system links mix proportions and ground conditions to workability, deformation, strength, and settlement-control performance.

The researchers organized the database around the actual performance sequence of tail grout. Fresh grout behavior was represented through water–binder ratio, bentonite–solid ratio, bentonite–water ratio, and cement–fly ash ratio, with five measured outputs: density, bleeding rate, fluidity, consistency, and stone rate. After hardening, the authors incorporated compressed deformation, 3-day unconfined compressive strength, and 28-day unconfined compressive strength. This separation was technically important because it allowed the system to distinguish workability during injection from mechanical performance after consolidation and hardening.

The authors conducted solid-performance tests under three soil conditions and three pressures. They used sand, silt, and clay to represent different permeability environments, while the applied pressures ranged from 100 to 300 kPa. This design choice links the testing strategy directly to the scientific problem: after grout enters the tail void, soil permeability and ground pressure influence water migration, compressed deformation, and the development of stiffness. The authors therefore did not evaluate hardened grout as a detached laboratory material, but as a material responding to boundary conditions that resemble the underground environment. The researchers used a physics-constrained generative adversarial network to expand the available data and their liquid-performance dataset was enlarged to 526 sets and the solid-performance dataset to 582 sets.  The generated data were guided by physical rules and screened to remain consistent with the expected mechanical and chemical behavior of cementitious materials.  

The team examined four algorithms: artificial neural network, random forest, extreme gradient boosting, and support vector regression. Bayesian optimization and 5-fold cross-validation were used to tune and evaluate these models. The final system did not force one algorithm onto all performance indicators. For liquid-state properties, the artificial neural network gave the strongest and most stable prediction across density, bleeding rate, fluidity, consistency, and stone rate. For solid-state behavior, the best algorithm depended on the output: extreme gradient boosting was selected for compressed deformation, artificial neural network for 3-day strength, and support vector regression for 28-day strength. This property-specific structure is technically important because liquid workability, consolidation deformation, and strength development do not have identical data behavior. The interpretive layer was built through SHAP analysis. Water–binder ratio appeared as the dominant variable for liquid properties and also strongly affected deformation. Cement–fly ash ratio was the main factor for strength development, especially early strength. Bentonite-related variables influenced bleeding and fluidity through water retention and thickening behavior, while confining pressure and soil permeability mainly influenced solid-state deformation. The parameter analysis sharpened these relationships: increasing water–binder ratio improved fluidity but increased bleeding and deformation sensitivity, whereas increasing cement–fly ash ratio enhanced both 3-day and 28-day strength, with stronger sensitivity at lower cement–fly ash ratios. Validation gave the system both laboratory and field grounding. Independent laboratory comparisons showed strong agreement between measured and predicted values for density, bleeding rate, consistency, stone rate, compressed deformation, and 28-day strength. In the Harbin Metro Line 2 case, the optimized grout reduced compressed deformation and lowered the required grouting volume per ring from 4.24 to 3.46 m³. The maximum surface settlement was reduced from about 6 mm to about 3.5 mm, corresponding to a reported reduction of approximately 42%. The paper also contrasts the rapid prediction time of the intelligent system with the lengthy traditional testing workflow, which can require about two months for a typical orthogonal experiment.

The findings of Dr. Jiaxin Liang and Prof. Wei Liu and colleagues have direct engineering value for shield tunnel construction, especially in projects where tail grouting must control both constructability and ground deformation. The developed system can be used to optimize grout formulations before construction by balancing fluidity, bleeding rate, density, consistency, stone rate, compressed deformation, and early- and later-age strength. By linking mix proportions, soil permeability, and pressure conditions to multiple grout responses, the new system gives engineers a rational basis for selecting mixtures suited to different ground conditions rather than applying one empirical formulation across an entire tunnel alignment. This approach enables site-specific optimization and adapts to varying geological conditions encountered along the tunnel, significantly improving project efficiency and deformation control. The intelligent system eliminates trial-and-error testing, substantially reducing testing time, labor costs, and material waste while enabling efficient multiperformance optimization. Engineers can now rapidly evaluate numerous formulation scenarios before construction, ensuring optimal grout selection tailored to site-specific ground conditions and project requirements, ultimately enhancing both construction efficiency and deformation control in complex tunneling projects.

Fenestration design can influence commercial building energy performance because windows function as architectural features, environmental interfaces, as well as thermal pathways. They provide daylight, view, and façade articulation, but they also create pathways for solar heat gain, conductive heat transfer, and seasonal shifts in heating and cooling demand. The same amount of glazing may perform differently depending on orientation, shape, and position on the façade. A horizontal opening may expose a different solar profile than a vertical one; a south-facing window may be desirable in one climate but burdensome in another; and a larger glazing ratio may reduce one energy load while increasing another. This means a fenestration strategy that reduces energy use in Honolulu or Houston may require a different balance of glazing area, orientation, and shape in Helena or Minneapolis.

Previous studies have examined individual window parameters such as window-to-wall ratio, aspect ratio, orientation, and glazing area, and they have shown that these features can influence building energy performance. However, the practical design problem is more complex than optimizing one variable at a time. Window-to-wall ratio, aspect ratio, and fenestration location interact with one another, and their combined effect determines the balance between heat gain, heat loss, and mechanical conditioning demand. This creates a need for a multi-parameter approach that can search for energy-efficient combinations rather than relying on isolated design rules. In a new research paper published in Journal of Cleaner Production, Associate Professor Reza Foroughi from the Department of Sustainable Technology and the Built Environment at Appalachian State University  developed a genetic-algorithm optimization model coupled with EnergyPlus to identify energy-efficient window-to-wall ratio, aspect ratio, and fenestration location for small commercial buildings.  They applied the model to a two-story commercial building across representative United States climate zones. The output is a climate-specific set of fenestration design parameters intended to reduce total building energy use at the early design stage.

Briefly, the building was square in plan, with eight windows distributed across the four orientations and two floors. The baseline model used centrally placed windows with a relatively large glazing proportion, and provided a consistent reference point for optimization. The envelope, glazing, HVAC system, occupancy schedule, and internal assumptions were specified so that changes in energy performance could be attributed to the geometric fenestration variables rather than to shifting building specifications.

The optimization model coupled a genetic algorithm with EnergyPlus simulations. This choice mattered because the design problem involved many coordinate-based variables and a search space with possible local optima. Instead of limiting the analysis to predefined window configurations, the algorithm repeatedly adjusted window coordinates within practical limits and evaluated the resulting total energy use. The objective function combined heating, cooling, lighting, and equipment energy, although the central trade-off was between heating and cooling loads. A design choice with direct scientific consequence was the decision to allow window coordinates to vary within architectural constraints; this converted window-to-wall ratio, aspect ratio, and placement into linked variables, which make it possible to identify energy-relevant configurations that would not necessarily arise from one-factor comparisons. The team found that hot climates generally favored smaller window-to-wall ratios, keeping glazing more restrained to limit cooling demand. On the other hand, cold climates behaved differently. In colder locations such as Helena and Minneapolis, the optimized designs allowed more south-facing glazing while keeping the other orientations more limited. The optimized aspect ratios also varied strongly by orientation and location. The authors observed although the Memphis case showed that a vertical south-facing window could be favored under specific climate and orientation conditions. In Helena, by contrast, the optimization favored a strongly elongated horizontal opening on the east façade.

These results show that window shape functioned as an energy-relevant design variable; it influenced the balance between incident solar gain and thermal demand. They found the optimized cases reduced total energy consumption in every climate zone and the decrease ranged from 15% in Honolulu to 2% in Helena and Minneapolis. Cooling energy dropped substantially under optimized fenestration, while heating energy increased slightly in several cases. The total balance still improved, meaning the reduction in cooling demand outweighed the added heating burden within the modeled conditions. Primary energy comparisons reinforced the same pattern, with larger gains in hot climates and smaller but still measurable reductions in cold climates.

The economic assessment translated these savings into annual cost terms for the modeled building where Honolulu showed the largest annual saving, while Helena showed the smallest. The authors emphasize that when these decisions are made at the early design stage of new construction, selecting optimized window dimensions and locations does not necessarily add construction cost in the same way that a technology retrofit might. The finding emphasizes the value of informed early-stage design, where placement and proportion can improve energy performance before costly changes are required.

The findings of Professor Reza Foroughi et al. have direct engineering value for early-stage design of small commercial buildings, where fenestration decisions are still flexible and can be changed without major cost penalties. The study shows that window-to-wall ratio, aspect ratio, and window placement should not be selected as independent architectural preferences, but as linked design variables that affect heating and cooling demand together. For engineers, this supports a more climate-responsive approach to envelope design, especially when preparing schematic layouts, façade studies, or energy models for small offices, retail buildings, educational facilities, and similar commercial structures. One practical application is the development of climate-specific fenestration guidelines. In hot locations such as Honolulu, Houston, and Memphis, the optimized results support restrained glazing areas to limit cooling demand. In colder climates such as Helena and Minneapolis, the findings support more selective use of larger south-facing windows while keeping north, east, and west glazing smaller. This gives designers a clearer basis for balancing solar heat gain against envelope heat loss rather than relying on uniform glazing ratios across all façades.

The study is also useful for simulation-driven building design. By coupling a genetic algorithm with EnergyPlus, the authors demonstrate how engineers can use optimization workflows to test many window configurations before construction. This is valuable for projects pursuing reduced operational energy, near-zero energy design, or improved façade performance while complementing later decisions about mechanical systems or renewable energy integration. When existing small commercial buildings undergo envelope renovation, the same logic can help determine whether reducing, reshaping, or relocating glazing would meaningfully reduce cooling or heating loads. Overall, the findings give engineers a practical method for converting fenestration design from a rule-of-thumb decision into a climate-specific energy optimization task.