Tailoring the properties of graphene nanosheets during electrochemical exfoliation

IF 3.1 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY

Carbon Trends Pub Date : 2025-01-01 DOI:10.1016/j.cartre.2024.100449

Markus Ostermann , Lukas Kalchgruber , Jürgen Schodl , Peter Lieberzeit , Pierluigi Bilotto , Markus Valtiner

{"title":"Tailoring the properties of graphene nanosheets during electrochemical exfoliation","authors":"Markus Ostermann , Lukas Kalchgruber , Jürgen Schodl , Peter Lieberzeit , Pierluigi Bilotto , Markus Valtiner","doi":"10.1016/j.cartre.2024.100449","DOIUrl":null,"url":null,"abstract":"<div><div>The large-scale production of graphene remains a significant bottleneck in harnessing the potential of this material. Electrochemical exfoliation offers a green, sustainable production protocol that is suitable for industrial scale-up. However, the material produced often suffers from a low yield and limited functional groups, which restricts its use in advanced applications.</div><div>In this study, we introduce a mathematical model that elucidates the intricate influences of production parameters, such as temperature and potential, on the characteristics of the product. A comprehensive understanding of the exfoliation process is achieved through detailed insights provided by X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray diffraction, and powder conductivity measurements. Design-of-Experiment and Pareto analysis are employed to determine the optimal production conditions. As a result, graphene nanosheets, tailored with specific physical and chemical properties (<em>e.g.</em>, functional groups, conductivity), can be produced.</div><div>Furthermore, we describe the significant influence of the cation during sulfate-based anodic exfoliation, which allows for efficiency and cost optimization. In general, the tailoring aspect of this work paves the way towards the industrial production of graphene nanosheets, tailored to the intended application. Simultaneously, the experimental design lays the foundation for a data-driven machine learning method for the optimal synthesis of sustainable two-dimensional materials.</div></div>","PeriodicalId":52629,"journal":{"name":"Carbon Trends","volume":"18 ","pages":"Article 100449"},"PeriodicalIF":3.1000,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Carbon Trends","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2667056924001287","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}

引用次数: 0

Abstract

The large-scale production of graphene remains a significant bottleneck in harnessing the potential of this material. Electrochemical exfoliation offers a green, sustainable production protocol that is suitable for industrial scale-up. However, the material produced often suffers from a low yield and limited functional groups, which restricts its use in advanced applications.

In this study, we introduce a mathematical model that elucidates the intricate influences of production parameters, such as temperature and potential, on the characteristics of the product. A comprehensive understanding of the exfoliation process is achieved through detailed insights provided by X-ray photoelectron spectroscopy, Raman spectroscopy, X-ray diffraction, and powder conductivity measurements. Design-of-Experiment and Pareto analysis are employed to determine the optimal production conditions. As a result, graphene nanosheets, tailored with specific physical and chemical properties (e.g., functional groups, conductivity), can be produced.

Furthermore, we describe the significant influence of the cation during sulfate-based anodic exfoliation, which allows for efficiency and cost optimization. In general, the tailoring aspect of this work paves the way towards the industrial production of graphene nanosheets, tailored to the intended application. Simultaneously, the experimental design lays the foundation for a data-driven machine learning method for the optimal synthesis of sustainable two-dimensional materials.

Abstract Image