Molecular weight prediction in polystyrene blends


Mixture analysis is a complex task especially when the polymers are of identical nature thereby leaving molecular weight as the only differentiating factor to fingerprint individual polymer molecules. Recently, increased interest in applying gradient high performance liquid chromatography techniques for determining the composition of polymer blends, compositional drift of copolymers or for the analysis of polymer additives has seen significant strides taken in this field. Currently, diffusion nuclear magnetic resonance spectroscopy is being used to study polymers, nanoparticles, organometallic complexes amongst other systems where diffusion coefficient is the physical observable and is related to the molecular weight. The latter can be observed empirically by inversion of Laplace Transform. Unfortunately, this technique is vulnerable to noise and prone to numerical instability. Such unprecedented shortcomings mandate the need for an alternative approach.

Researchers led by professor Ignacio Fernández at University of Almerıa in Spain, proposed a study on the first application of the novel procedure based on a genetic algorithm which utilizes boxcar functions for the quantitative determination of the diffusion coefficients. Their objective was that this novel technique would lead to more accurate molecular weight prediction. They also purposed to present a comparison of the novel technique with the commonly applied algorithms such as ITAMeD, CONTIN and TRAIn. Their work is now published in the research journal, Soft Matter.

The research team commenced the empirical procedure by preparing the nuclear magnetic resonance spectroscopy samples in an oven dried specific nuclear magnetic resonance spectroscopy tube. They then placed the samples in a Bruker Avance III 500 spectrometer equipped with a microprocessor-controlled gradient unit and a third radiofrequency channel using an indirect 5 mm triple probe with an actively shielded Z-gradient coil so as to record the required measurements.

The authors observed that the boxcar functions technique did not fail in the estimation of accurate D-values. This showed that the technique could be applied for accurate prediction of molecular weight. This was all in comparison with previously used methods. The researchers also observed that the boxcar functions technique displayed desired strength and rapidity in a mixture of small molecules, therefore portraying comparable performance and noise vulnerability to the ITAMeD. They noted that the number of boxcar functions used in the genetic algorithms were the main factors in terms of computational costs.

Ignacio Fernandez and coworkers successfully presented a novel description of a genetic algorithm that has been applied to pulse field gradient spin echo diffusion nuclear magnetic resonance spectroscopy. The results obtained have showed that the novel approach reconstructs satisfactorily diffusion coefficients in a ternary blend of polystyrene polymers. The new algorithm developed in the study is expected to be extremely useful for many applications in the polymer field, and specifically in the area of blends.

Molecular weight prediction in polystyrene blends-Advances in Engineering

About the author

Francisco Manuel Arrabal-Campos was born in Granada, Spain. He graduated as industrial engineer in 2008 in the University of Malaga and obtained his Master of Computer Science degree at the university of Almeria in 2012. In 2013 started his PhD in NMR methodology and diffusion NMR under the supervision of Ignacio Fernández. He his author of 10 articles and has patented the software package DiffAtOnce: Molecular Diffusion. Since 2017 has joined the School of Engineering at the University of Almería where he is currently lecturing.

About the author

José Domingo Álvarez is a postdoctoral researcher at the University of Almería, Spain. He received the Computer Science Engineering degree from the University of Almería, Spain in 2003. In 2008, he obtained, from the same university, the Ph.D. degree in Automatic Control in Solar Plants which was performed in a leading research centre in this field: la Plataforma Solar de Almería. Then, he spent three years in the University of Seville (Spain) with a Juan de la Cierva postdoctoral grant. Currently, he is a Ramón y Cajal associate researcher in the University of Almería.

His research interests are focused on the fields of repetitive control, predictive control and classical PID control with applications to solar power plants and energy efficient buildings. He is a member of the “Automatic control, Robotics and Mechatronics” research group. He is member of the Comité Español de Automática (main Spanish Association in Automatic Control).

About the author

Amador García-Sancho was born in Spain in 1981 and obtained his Ph.D. from the University of Valencia in 2010. During his Ph D he was involved in an undergraduate exchange program in Pittsburgh University with Prof. Dennis P. Curran. He joined AIMPLAS in 2009 as researcher and in 2010 received the Plastic and Innovation Award for the development of ultra-hydrophobic-heatable coatings (patented). During 2014 created the Synthesis Laboratory and in 2017 was promoted to Technology Director in AIMPLAS where he has remained ever since. His research interests are focused on advanced polymeric materials, 3D printing technologies, anti-ice materials and reactive extrusion processes.

About the author

Ignacio Fernández was born in Spain in 1976 and obtained his Ph.D. from the University of Almería in 2003. He has been involved in several undergraduate student exchange programs, which took him first to Bath (2000) with Prof. Matthew G. Davidson, to Wurzburg with Prof. Dietmar Stalke (2001) and to Zürich with Paul S. Pregosin (2003). After postdoctoral studies at the Swiss Federal Institute of Technology in Zurich (2003-2006) under Paul S. Pregosin guidance, and Cornell University in Ithaca New York (2007) under the supervision of Paul J. Chirik, he moved to the University of Almería as a Ramón y Cajal associate researcher. In 2011 promoted to associate professor in the University of Almería where he has remained ever since. In 2013 created his own research group “Advanced NMR Methods and Metal-based Catalysts ”. His research interests include organometallic chemistry, homogeneous catalysis, multinuclear NMR spectroscopy and metabolomics applied to the agri-food sector.


Francisco M. Arrabal-Campos, José D. Álvarez, Amador García-Sancho and Ignacio Fernández. Molecular weight prediction in polystyrene blends. Unprecedented use of a genetic algorithm in pulse field gradient spin echo (PGSE) NMR. Soft Matter, 2017, volume 13, page 6620


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