Application of 534-03-2In 2021 ,《Machine-learning-assisted low dielectric constant polymer discovery》 appeared in Materials Chemistry Frontiers. The author of the article were Liang, Jiechun; Xu, Shangqian; Hu, Linfeng; Zhao, Yu; Zhu, Xi. The article conveys some information:
Machine learning (ML) has excellent potential for mol. property prediction and new mol. discovery. However, real-world synthesis is the most vital part of determining a polymer’s value. This paper demonstrates automatic polymer discovery through ML and an intelligent cloud laboratory to find new environmentally friendly polymers with low dielec. constants that have potential applications in high-speed communication networks. In the machine learning discovery, we use ML on SMILES from databases to identify ideal functional groups with reasonable solutions Moreover, the solutions are sent to the cloud and synthesized via our intelligent system. A few of them can be successfully synthesized and two of them have excellent performance in low-dielec.-constant applications. This autonomous system enables reliable and efficient combinations of data-driven research and synthesis, reduces both the time and cost of polymer-discovery experiments, and accelerates the overall process for low-dielec.-constant polymer discovery. In the experimental materials used by the author, we found 2-Aminopropane-1,3-diol(cas: 534-03-2Application of 534-03-2)
2-Aminopropane-1,3-diol(cas: 534-03-2) belongs to anime. Milder oxidation, using reagents such as NaOCl, can remove four hydrogen atoms from primary amines of the type RCH2NH2 to form nitriles (R―C≡N), and oxidation with reagents such as MnO2 can remove two hydrogen atoms from secondary amines (R2CH―NHR′) to form imines (R2C=NR′). Tertiary amines can be oxidized to enamines (R2C=CHNR2) by a variety of reagents.Application of 534-03-2
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