石墨烯和石墨烯基纳米复合材料的合成与应用:传统到人工智能的方法

W. Tariq, Faizan Ali, C. Arslan, Abdul Nasir, S. Gillani, Abdul Rehman
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引用次数: 3

摘要

石墨烯研究的最新进展使其纳米复合材料能够用于许多基于能源和环境的应用。近年来,石墨烯基聚合物纳米复合材料的研究进展备受关注,特别是在合成和应用方面。石墨烯基纳米复合材料表现出惊人的电学、机械、化学和热特性。石墨烯纳米复合材料(GNCs)的合成方法多种多样,包括共价法和非共价法、化学沉积法、水热生长法、电泳沉积法和物理沉积法。化学方法是在低温下小批量生产石墨烯的最可行途径。该技术还可以在各种衬底材料上生产石墨烯薄膜。利用人工智能(AI)合成人工智能制造的纳米颗粒最近受到了广泛关注。这些纳米复合材料在环境、能源和农业等领域具有良好的应用前景。由于载流子迁移率高,石墨烯基材料增强了半导体材料的光催化性能。同样,由于这些材料的高表面积,它们具有很高的去除污染物,特别是重金属的潜力。本文重点介绍了石墨烯基纳米复合材料的合成,特别提到了利用现代人工智能工具的力量来更好地了解GNC材料的特性,以及如何将这些知识用于更好地应用于可持续未来的发展。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Synthesis and applications of graphene and graphene-based nanocomposites: Conventional to artificial intelligence approaches
Recent advances in graphene research have enabled the utilization of its nanocomposites for numerous energy-based and environmental applications. Recently, the advancement in graphene-based polymer nanocomposites has received much attention with special emphasis on synthesis and application. Graphene-based nanocomposites show astonishing electrical, mechanical, chemical, and thermal characteristics. Graphene nanocomposites (GNCs) are synthesized using a variety of methods, including covalent and non-covalent methods, a chemical-based deposition approach, hydrothermal growth, electrophoresis deposition, and physical deposition. Chemical methods are the most viable route for producing graphene in small quantities at low temperatures. The technique can also produce graphene films on a variety of substrate materials. The use of artificial intelligence (AI) for the synthesis of AI-created nanoparticles has recently received a lot of attention. These nanocomposite materials have excellent applications in the environmental, energy, and agricultural sectors. Due to high carrier mobility, graphene-based materials enhance the photocatalytic performance of semiconductor materials. Similarly, these materials have high potential for pollutant removal, especially heavy metals, due to their high surface area. This article highlights the synthesis of graphene-based nanocomposites with special reference to harnessing the power of modern AI tools to better understand GNC material properties and the way this knowledge can be used for its better applications in the development of a sustainable future.
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