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Liquid Phase Exfoliation of 2D Materials and Its ElectrochemicalApplications in the Data-Driven Future
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作者 Panwad Chavalekvirat Wisit Hirunpinyopas +2 位作者 krittapong deshsorn Kulpavee Jitapunkul Pawin Iamprasertkun 《Precision Chemistry》 2024年第7期300-329,共30页
The electrochemical properties of 2D materials,particularly transition metal dichalcogenides(TMDs),hinge ontheir structural and chemical characteristics.To be practicallyviable,achieving large-scale,high-yield product... The electrochemical properties of 2D materials,particularly transition metal dichalcogenides(TMDs),hinge ontheir structural and chemical characteristics.To be practicallyviable,achieving large-scale,high-yield production is crucial,ensuring both quality and electrochemical suitability forapplications in energy storage,electrocatalysis,and potentialbasedionic sieving membranes.A prerequisite for success is a deepunderstanding of the synthesis process,forming a critical linkbetween materials synthesis and electrochemical performance.Thisreview extensively examines the liquid-phase exfoliation technique,providing insights into potential advancements and strategies tooptimize the TMDs nanosheet yield while preserving theirelectrochemical attributes.The primary goal is to compiletechniques 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 potentialimpact of these parameters on the structural and chemical properties of TMD nanosheets,highlighting their pivotal role inelectrochemical applications.Acknowledging evolving research methodologies,the review explores integrating machine learning anddata science as tools for understanding relationships and key characteristics.Envisioned to advance 2D material research,includingthe optimization of graphene,MXenes,and TMDs synthesis for electrochemical applications,this compilation charts a coursetoward data-driven techniques.By bridging experimental and machine learning approaches,it promises to reshape the landscape ofknowledge in electrochemistry,offering a transformative resource for the academic community. 展开更多
关键词 Liquid Phase Exfoliation 2D Materials Transition Metal Dichalcogenides TMDS Size Selection ELECTROCHEMISTRY Energy Storage Ionic Sieving Machine Learning
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