Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing
Crowd-Sourced Data and Analysis Tools for Advancing the Chemical Vapor Deposition of Graphene: Implications for Manufacturing
复制标题
促进石墨烯化学气相沉积的众包数据和分析工具：对制造业的影响
DOI：
10.1021/acsanm.0c02018
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发表时间：
2020
影响因子：
5.9
通讯作者：
Seong, Bomsaerah
中科院分区：
文献类型：
--
作者：
Schiller, Joshua A.;Toro, Ricardo;Shah, Aagam;Surana, Mitisha;Zhang, Kaihao;Robertson, Matthew;Miller, Kristina;Cruse, Kevin;Liu, Kevin;Seong, Bomsaerah
通过化学气相沉积（CVD）工业生产石墨烯需要的不仅仅是在实验室反应器中合成大域、高质量石墨烯的能力。石墨烯在电子器件制造过程中的集成需要在介电基底上成本有效且环境友好地生产石墨烯，但目前的方法只能在金属催化剂上生产石墨烯。石墨烯的可持续制造还应该节省催化剂和反应气体，但目前金属催化剂通常在合成后溶解。这些目标的进展受到数百个耦合合成参数的阻碍，这些参数可以强烈影响低维材料的CVD，并且在个别实验室存在的丰富实验数据的出版文献中缺乏沟通。我们在这里报告一个平台，“用于合成高质量材料的石墨烯配方”（Gr-ResQ：发音为graphene rescue），其中包括用于数据驱动的石墨烯合成的强大新工具。Gr-ResQ的核心是CVD合成配方和相关实验结果的众包数据库。该数据库可获取从催化剂材料和制备步骤等合成条件到实验室环境温度和反应器详细信息以及所得到的拉曼光谱和显微镜图像等范围内的所有参数。这些参数经过精心选择，以释放机器学习模型的潜力，以推进合成。一套相关的工具可以快速、自动和标准化地处理拉曼光谱和扫描电子显微镜图像。为了促进基于社区的努力，Gr-ResQ为研究小组之间的网络物理协作提供了工具，允许不同团队设计，执行和分析实验。Gr-ResQ还允许通过材料数据设施发布和发现配方，该设施在发布时为每个配方分配唯一的标识符，并在搜索索引中收集参数。我们设想，这种数据驱动合成的整体方法可以加速CVD配方发现和生产控制，并为石墨烯以及许多其他1D和2D材料的发展提供机会。
Industrial production of graphene by chemical vapor deposition (CVD) requires more than the ability to synthesize large domain, high-quality graphene in a lab reactor. The integration of graphene in the fabrication process of electronic devices requires the cost-effective and environmentally friendly production of graphene on dielectric substrates, but current approaches can only produce graphene on metal catalysts. Sustainable manufacturing of graphene should also conserve the catalyst and reaction gases, but today the metal catalysts are typically dissolved after synthesis. Progress toward these objectives is hindered by the hundreds of coupled synthesis parameters that can strongly affect CVD of low-dimensional materials and poor communication in the published literature of the rich experimental data that exists in individual laboratories. We report here on a platform, “graphene recipes for synthesis of high quality material” (Gr-ResQ: pronounced graphene rescue), which includes powerful new tools for data-driven graphene synthesis. At the core of Gr-ResQ is a crowd-sourced database of CVD synthesis recipes and associated experimental results. The database captures ∼300 parameters ranging from synthesis conditions such as a catalyst material and preparation steps, to ambient lab temperature and reactor details, as well as resulting Raman spectra and microscopy images. These parameters are carefully selected to unlock the potential of machine-learning models to advance synthesis. A suite of associated tools enable fast, automated, and standardized processing of Raman spectra and scanning electron microscopy images. To facilitate community-based efforts, Gr-ResQ provides tools for cyber-physical collaborations among research groups, allowing experiments to be designed, executed, and analyzed by different teams. Gr-ResQ also allows publication and discovery of recipes via the Materials Data Facility, which assigns each recipe a unique identifier when published and collects parameters in a search index. We envision that this holistic approach to data-driven synthesis can accelerate CVD recipe discovery and production control and open opportunities for advancing not only graphene but also many other 1D and 2D materials.
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DOI：
--
发表时间：
2018
期刊：
影响因子：
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作者：
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影响因子：
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影响因子：
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影响因子：
41.2
作者：
Chen, Zongping;Ren, Wencai;Cheng, Hui-Ming
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Cheng, Hui-Ming