Tracking Twin Boundary Jerky Motion at Nanometer and Microsecond Scales


The mechanical behavior of numerous solid materials is governed by the twining process – a vital plastic deformation mode that occurs through nucleation and twin boundary motion. Besides, the twinning reorientation mechanism is responsible for the attenuation of seismic waves in the Earth’s lower mantle. Recently, it has been discovered that twinning reorientation in shape memory alloys (SMAs), ferroelectric materials and ferromagnetic SMA (FSMA) produce significant straining effects that provide a fundamental mechanism for facilitating the transformation between mechanical, electrical, thermal and magnetic energy. This interesting property finds many applications in actuation, energy harvesting and sensing.

Twinning boundaries propagate in a jerky motion consisting of avalanches when driven at slow rates. At higher speeds, the propagation of the twin boundary motion also displays a jerky motion that follows kinetic relations dependent on the boundary lattice barriers. Nevertheless, the magnitude of the jerks in both cases remains unclear. Also, the connection between the avalanches at slow rates and the changes in velocity at higher rates, if any, remains largely underexplored. Different methods have been used to measure and study the twin boundary motion at slow and high rates. However, most have several drawbacks that prevent them from being used to study jerky motion at micro- and nano-scale.

Recently, the potential application of magnetic emission measurements in the study of avalanches has been demonstrated. Its sensitivities to small-scale avalanches are comparable to acoustic emission (AE). It also captures the overall magnetization change rate and can directly measure the displacement of a moving interface. This is often difficult to achieve due to the material behavior complexities. In FSMA, for instance, the twinning reorientation-induced magnetization changes are often coupled with that of ordinary domain switching, hindering the ability to relate the motion of a particular twin boundary to the measured magnetic emission (ME) events.

To overcome these challenges, PhD candidate Emil Bronstein, Professor Ronen Talmon and Professor Doron Shilo from the Technion – Israel Institute of Technology in collaboration with Dr. László Tóth, Dr. Lajos Daróczi and Prof. Dezsö Beke from the University of Debrecen studied the jerky motion of twin boundaries in the FSMA Ni-Mn-Ga during slow compression. The generated avalanche events were simultaneously measured by means of force and ME and sensors. The experiment was carefully designed to eliminate the possible process complexity to permit the study of single twin boundary motion. Their work is currently published in the journal, Advanced Functional Materials.

The research team observed numerous small and rapid ME avalanches with features akin to jerky twin boundary motion during and between stress drop avalanches. For each stress drop, the overall ME correlated well with the stress drop amplitude, suggesting that the measured ME was directly related to the twin boundary motion. Moreover, the ME measurements were shown to capture avalanches that occur at nanometer and microsecond scales. Further statistical analysis of the ME events during and between the stress drops showed that they were created through the same process, with the difference between them being the delay times between the events. This implied that the avalanches observed during the slow rate twin boundary motion and the subsequent changes in the velocity during high-rate motion represent similar behaviors that can be explained using the same theory.

In summary, the authors successfully used the simultaneous stress and ME measurements to study the twin boundary jerky motion in FSMA Ni-Mn-Ga. The values of the transformed volumes calculated by the ME signals agreed well with those calculated based on the stress drops. Although the same process was observed during and between stress drops, the avalanche occurrence was much greater during stress drops. The results also revealed the possibilities of unexplored avalanche hierarchies with much smaller sizes and durations that could not be detected via ME detection sensor. In a statement to Advances in Engineering, the authors said their findings contribute to developing a unified theory for describing the twin boundary motion at different rates and scales.

Tracking Twin Boundary Jerky Motion at Nanometer and Microsecond Scales

Credit Adv Funct Materials, Volume: 31, Issue: 50, First published: 17 September 2021, DOI: (10.1002/adfm.202106573)

About the author

Professor Doron Shilo received his BSc. (Summa Cum Laude) in physics and materials engineering and his MSc. and Ph.D. in materials engineering from the Technion. In the years 2003-2004 he was a postdoctoral fellow at Caltech and in 2004 he joined the Faculty of Mechanical Engineering at the Technion.

Prof. Shilo studies material mechanics at small scales, with emphasis on “smart” materials, which function as motors or sensors, and biological materials. Of particular interest are multi-disciplinary problems in which the mechanical behavior of the material is coupled with electric, magnetic, thermal, or chemical effects. These phenomena often involve sub-processes, such as the motion of material interfaces or collagen fibrils, that occur at different time and length scales.

Prof. Shilo’s research group has expertise in developing innovative experimental instruments and methods that provide unique and vital information on the mechanics of materials at the nano and micro scales. Prof. Shilo’s studies combine experimental characterization with theoretical modeling and simulations to provide fundamental scientific insights and engineering knowledge.

During his years as a professor at the Technion, Prof. Shilo received the Ray and Miriam Klein Research Prize (2015) and the Henry Taub Prize for Academic Excellence (2016).

About the author

Emil Bronstein received the B.Sc. and M.Sc. (cum laude) degrees from the Faculty of Mechanical Engineering, Technion – Israel Institute of Technology, in 2016 and 2019, respectively, where he is currently pursuing the Ph.D. degree.

Emil’s research field is materials mechanics, and he focuses specifically on mechanical avalanches, which are small-scale, impulsive events that occur during a variety of phenomena in numerous material classes. To this end, Emil develops novel experimental and data-driven methods for the investigation of acoustic and magnetic emissions from materials during avalanche events. So far, the relationship between emitted acoustic signals and the physics of avalanches is unclear. By developing data-driven artificial intelligence and machine learning tools for the analysis of acoustic emissions and their mutual relationship with magnetic emissions, Emil aims to discover the underlying physics and principles of avalanches and associated acoustic emissions.

Emil is the recipient of the Azrieli Fellowship, Daniel Scholarship, Jacobs Prize for Excellent Publication, and several Excellent Teaching Assistant awards.

About the author

Prof. Ronen Talmon is an Associate Professor of electrical engineering at the Technion — Israel Institute of Technology, Haifa, Israel. I received the B.A. degree (cum laude) in mathematics and computer science from the Open University in 2005, and the Ph.D. degree in electrical engineering from the Technion in 2011.

Prof. Talmon’s research interests lie at the intersection of signal processing, data analysis, and machine learning. In particular, He develops methods for high dimensional data and signal analysis with diffusion operators, making use of recent transformative results in manifold and geometry learning. He finds exciting that the development of unsupervised nonlinear data analysis techniques facilitates the transition from data observations to creating representations, without deriving models in closed-form. In his work, the focus shifts from processing the data samples themselves and considering instead structured data through the lens of operators. This approach introduces new powerful “handles” to data, capturing their complexity efficiently. In Prof. Talmon’s research group, the basic theory behind this nonlinear analysis is studied, new operators for this purpose are developed, and efficient data-driven algorithms are devised.

His recent contributions involve intrinsic representations, sensor fusion, dynamical systems analysis, and transports on manifolds. In addition, he explores how his approach can be leveraged for devising efficient solutions to a broad range of open real-world data analysis problems, including problems in neuroscience, medicine, bioinformatics, as well as in audio and acoustics.

Prof. Talmon is the recipient of the Irwin and Joan Jacobs Fellowship, the Andrew and Erna Fince Viterbi Fellowship, the Horev Fellowship, the Norman Seiden Excellence Award, and the Schmidt Career Advancement Chair in Artificial Intelligence. Since 2018, Prof. Talmon is funded by the ERC starting grant, entitled “Nonlinear Data and Signal Analysis with Diffusion Operators”.


Bronstein, E., Tóth, L., Daróczi, L., Beke, D., Talmon, R., & Shilo, D. (2021). Tracking Twin Boundary Jerky Motion at Nanometer and Microsecond ScalesAdvanced Functional Materials, 31(50), 2106573.

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