Liquid Phase Exfoliation of 2D Materials and Its Electrochemical Applications in the Data-Driven Future

• Published in: Precision Chemistry Vol. 2; no. 7; pp. 300 - 329
• Main Authors: Chavalekvirat, Panwad, Hirunpinyopas, Wisit, Deshsorn, Krittapong, Jitapunkul, Kulpavee, Iamprasertkun, Pawin
• Format: Journal Article
• Language: English
• Published: United States University of Science and Technology of China and American Chemical Society 22.07.2024; American Chemical Society
• Subjects: Review; Ionic Sieving; 2D Materials; Energy Storage; Transition Metal Dichalcogenides; Electrochemistry; Liquid Phase Exfoliation; Machine Learning; TMDs; Size Selection
• ISSN: 2771-9316; 2771-9316
• DOI: 10.1021/prechem.3c00119

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.