6 Materialchemie
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- 2D chromatography (1)
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- 1 Analytische Chemie; Referenzmaterialien (2) (entfernen)
In this contribution different ways are explored with the aim to generate suitable training data for ‘non-ideal’ samples using various approaches, e.g., computer-generated images or unsupervised learning algorithms such as generative adversarial networks (GANs). We used these data to train simple CNNs to produce segmentation masks of SEM images and tested the trained networks on real SEM images of complex nanoparticle samples. The novel use of CNN for the automated analysis of the size of nanoparticles of complex shape and with a high degree of agglomeration has proved to be a promising tool for the evaluation of particle size distribution on a large number of constituent particles. Further development and validation of the preliminary model, respectively larger training and validation data sets are necessary.
Simultaneous characterization of poly(acrylic acid) andpolysaccharide polymers and copolymers
(2020)
Copolymer products that result from grafting acrylic acid and other hydrophilicmonomers onto polysaccharides have recently gained significant interest in researchand industry. Originating from renewable sources, these biodegradable, low toxicity,and polar copolymer products exhibit potential to replace polymers from fossil sourcesin several applications and industries. The methods usually employed to character-ize these copolymers are, however, quite limited, especially for the measurement ofbulk properties. With more sophisticated applications, for example, in pharmaceu-tics requiring a more detailed analysis of the chemical structure, we describe a newapproach for this kind of complex polymers. Our approach utilizes chromatographyin combination with several detection methods to separate and characterize reactionproducts of the copolymerization of acrylic acid and chemically hydrolyzed starch.These samples consisted of a mixture of homopolymer poly (acrylic acid), homopoly-mer hydrolyzed starch, and – in a lower amount – the formed copolymers. Several chro-matographic methods exist that are capable of characterizing either poly (acrylic acid)or hydrolyzed starch. In contrast, our approach offers simultaneous characterization ofboth polymers. The combination of LC and UV/RI offered insight into the compositionand copolymer content of the samples. Size exclusion chromatography experimentsrevealed the molar mass distribution of homopolymers and copolymers. FTIR inves-tigations confirmed the formation of copolymers while ESI-MS gave more details onthe end groups of hydrolyzed starches and poly (acrylic acids). Evidence of copolymerstructures was obtained through NMR measurements. Finally, two-dimensional chro-matography led to the separation of the copolymers from both homopolymers as wellas the additional separation of sodium clusters. The methods described in this work area powerful toolset to characterize copolymerization products of hydrolyzed starch andpoly(acrylic acid). Together, our approach successfully correlates the physicochemicalproperties of such complex mixtures with their actual composition.