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.
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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