Solvent screening for efficient chemical exfoliation of graphite
Solvent screening for efficient chemical exfoliation of graphite
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DOI：
10.1088/2053-1583/abc08a
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发表时间：
2020-11
期刊：
影响因子：
5.5
通讯作者：
Nhan Nu Thanh Ton;Minh-Quyet Ha;Takuma Ikenaga;A. Thakur;H. Dam;Toshiaki Taniike
中科院分区：
文献类型：
--
作者：
Nhan Nu Thanh Ton;Minh-Quyet Ha;Takuma Ikenaga;A. Thakur;H. Dam;Toshiaki Taniike
石墨的化学剥离是制备相对高质量石墨烯的有效方法，其中溶剂的选择对产物的产率和质量起着重要作用。在这里，我们进行了筛选不同的溶剂和它们的混合物的超声波下的石墨的液相剥离。芳香族，胺和卤素基团之间的协同效应被确定。当这些官能团通过溶剂混合物结合时，与当它们被引入单一溶剂的分子结构中时相比，更有效地利用了协同作用。筛选结果用于基于Dempster-Shafer证据理论的新型机器学习技术，以系统地研究协同效应并推荐新的溶剂组合。实验和数据驱动的方法的建议组合被证明是强大的探索协同溶剂组合。
Chemical exfoliation of graphite is an effective method to produce graphene of relative high quality, where the choice of solvents plays an important role in the product yield and quality. Here, we performed screening of different solvents and their mixtures for the liquid-phase exfoliation of graphite under ultrasonication. A synergistic effect among aromatic, amine, and halogen groups was identified. The synergy was more effectively exploited when these functional groups were combined through solvent mixtures compared to when they were introduced in the molecular structure of single solvents. The screening results were utilized for a novel machine learning technique based on the Dempster–Shafer theory of evidence to systematically investigate synergistic effects and recommend new solvent combinations. The proposed combination of the experiment and the data-driven approach was demonstrated to be powerful for exploring synergistic solvent combinations.