Predictive materials design has rapidly accelerated in recent years with the advent of large-scale resources, such as materials structure and property databases generated by ab initio computations. In the absence of analogous ab initio frameworks for materials synthesis, high-throughput and machine learning techniques have recently been harnessed to generate synthesis strategies for select materials of interest. Still, a community-accessible, autonomously-compiled synthesis planning resource which spans across materials systems has not yet been developed. In this work, we present a collection of aggregated synthesis parameters computed using the text contained within over 640,000 journal articles using state-of-the-art natural language processing and machine learning techniques. We provide a dataset of synthesis parameters, compiled autonomously across 30 different oxide systems, in a format optimized for planning novel syntheses of materials.
Depolymerization of proanthocyanidins in the presence of phloroglucinol
or benzyl mercaptan
nucleophiles was studied to develop a method for their quantitative
determination. Degradation
products were separated and quantified by either GC or HPLC and the
reaction conditions optimized.
The high depolymerization yields obtained with benzyl mercaptan
make the method suitable for
proanthocyanidin determination. The method is more specific and
sensitive than the colorimetric
methods classically used. It is particularly well suited for the
analysis of samples containing either
low amounts of proanthocyanidins or insoluble
proanthocyanidins.
Keywords: Proanthocyanidins; tannins; estimation; thiolysis;
phloroglucinol
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