Graphene oxide standardization and classification: Methods to support the leap from lab to industry
Graphene oxide standardization and classification: Methods to support the leap from lab to industry
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DOI：
10.1016/j.carbon.2018.02.091
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发表时间：
2018-07-01
期刊：
影响因子：
10.9
通讯作者：
Vecitis, Chad D.
中科院分区：
文献类型：
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作者：
Amadei, Carlo A.;Arribas, Paula;Vecitis, Chad D.
尽管氧化石墨烯(GO)已经被广泛应用于各种研究领域，但GO的实现潜力仍然存在争议。研究人员通常将GO定义为具有氧功能的2D碳纳米材料，但这个定义过于宽松，导致社区比较显著不同的纳米材料的结果。为了克服这一挑战，我们建议使用高通量的后处理GO表征技术来快速、彻底地定义GO的化学形态特性。然后，基于特征分析和一种聚类算法，将围棋分为六类。用不同厂家的GO样品对分类方法进行了验证。商业样品被单独实施以制造各种宏观器件(例如膜)，我们观察到归入同一类别的GO提供了类似的宏观性能。相比之下，不同类别的样品在宏观结果上产生了明显的差异，证实了使用标准化材料的重要性。目前提出的定性和分类方法将有助于研究界，使研究之间能够进行公平的比较。此外，它还将通过为特定应用分发具有最佳性能的GO，帮助GO生产商以更有效的方式瞄准客户，支持GO从实验室到行业的飞跃。(C)2018爱思唯尔有限公司。保留所有权利。
Although graphene oxide (GO) has been widely used in a variety of research fields, the potential for GO implementation remains controversial. Researchers commonly define GO as a 2D carbon nanomaterial with oxygen functionalities, but this definition is too loose and leads the community to compare results among significantly different nanomaterials. In order to overcome this challenge, here we suggest high-throughput post-processing GO characterization techniques to rapidly and thoroughly define GO chemomorphological properties. Then, based on characterization analysis and a clustering algorithm, we classified GO into six categories. The classification method was validated with GO samples obtained from different producers. The commercial samples were individually implemented to fabricate various macroscopic devices (e.g., membranes) and we observed that GO classified in the same category offered similar macroscopic performance. In contrast, samples from different categories resulted in a noticeable variation in macroscopic results, corroborating the importance of using standardized materials. The presented characterization and classification method will assist the research community by enabling a fair comparison between studies. Moreover, it will assist GO producers to target customers in a more-effective manner by distributing GO with optimal properties for a specific application, supporting the leap of GO from lab to industry. (C) 2018 Elsevier Ltd. All rights reserved.