Chavalekvirat Panwad, Hirunpinyopas Wisit, Deshsorn Krittapong, Jitapunkul Kulpavee, Iamprasertkun Pawin
School of Bio-Chemical Engineering and Technology, Sirindhorn International Institute of Technology, Thammasat University, Pathum Thani 12120, Thailand.
Research Unit in Sustainable Electrochemical Intelligent, Thammasat University, Pathum Thani 12120, Thailand.
Precis Chem. 2024 Mar 29;2(7):300-329. doi: 10.1021/prechem.3c00119. eCollection 2024 Jul 22.
The electrochemical properties of 2D materials, particularly transition metal dichalcogenides (TMDs), hinge on their structural and chemical characteristics. To be practically viable, achieving large-scale, high-yield production is crucial, ensuring both quality and electrochemical suitability for applications in energy storage, electrocatalysis, and potential-based ionic sieving membranes. A prerequisite for success is a deep understanding of the synthesis process, forming a critical link between materials synthesis and electrochemical performance. This review extensively examines the liquid-phase exfoliation technique, providing insights into potential advancements and strategies to optimize the TMDs nanosheet yield while preserving their electrochemical attributes. The primary goal is to compile techniques for enhancing TMDs nanosheet yield through direct liquid-phase exfoliation, considering parameters like solvents, surfactants, centrifugation, and sonication dynamics. Beyond addressing the exfoliation yield, the review emphasizes the potential impact of these parameters on the structural and chemical properties of TMD nanosheets, highlighting their pivotal role in electrochemical applications. Acknowledging evolving research methodologies, the review explores integrating machine learning and data science as tools for understanding relationships and key characteristics. Envisioned to advance 2D material research, including the optimization of graphene, MXenes, and TMDs synthesis for electrochemical applications, this compilation charts a course toward data-driven techniques. By bridging experimental and machine learning approaches, it promises to reshape the landscape of knowledge in electrochemistry, offering a transformative resource for the academic community.
二维材料,特别是过渡金属二硫属化物(TMDs)的电化学性质取决于其结构和化学特性。要在实际应用中可行,实现大规模、高产率的生产至关重要,这要确保材料在储能、电催化和基于电位的离子筛分膜应用中的质量和电化学适用性。成功的一个先决条件是深入了解合成过程,这在材料合成和电化学性能之间形成了关键联系。本综述广泛研究了液相剥离技术,深入探讨了在保留TMDs纳米片电化学属性的同时优化其产量的潜在进展和策略。主要目标是汇编通过直接液相剥离提高TMDs纳米片产量的技术,考虑诸如溶剂、表面活性剂、离心和超声动力学等参数。除了探讨剥离产量外,本综述还强调了这些参数对TMD纳米片结构和化学性质的潜在影响,突出了它们在电化学应用中的关键作用。认识到研究方法的不断发展,本综述探索将机器学习和数据科学整合为理解关系和关键特性的工具。旨在推进二维材料研究,包括优化用于电化学应用的石墨烯、MXenes和TMDs的合成,本汇编绘制了一条通向数据驱动技术的道路。通过弥合实验和机器学习方法,它有望重塑电化学知识领域,为学术界提供一种变革性资源。