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Takafumi Ishii

Publications and source records attributed to Takafumi Ishii.

2 recordsLinked to original sources

Unveiling the Influence of Graphitic Nanocarbon Structures, Defects, and Surface Functional Groups on Supercapacitive Performance in LiTFSI Electrolyte

When investigating the performance of supercapacitors (SCs), it was found that they offer high-power density and long life but are limited in capacity and energy density. Water-in-salt electrolytes, i.e., 21___m lithium bis(trifluoromethanesulfonyl) imide (LiTFSI), offer a solution by expanding the electrochemical window compared to a diluted aqueous environment. While significant research has been dedicated to improving these electrolytes, the impact of carbon properties on energy storage remains relatively underexplored. This study investigated the influence of carbon structures, defects, and surface functional groups on supercapacitive performance in LiTFSI electrolytes. For this purpose, a series of carbon materials, including carbon nano-onions and porous conductive carbon black (ENSACO), were synthesized and systematically modified by oxidative and reductive thermal treatments. Our results indicated that higher specific surface area and active surface area improve the ion adsorption and capacitance, while specific oxygen functionalities, such as carboxylic and anhydride groups, were found to significantly enhance capacitance. Furthermore, crystallinity and graphitic characteristics play a role in capacitance, with smaller graphene domains and greater disorder promoting ion storage. The study demonstrates a direct correlation between carbon microstructure and electrochemical behavior, providing insights into the rational design of carbon electrodes in high-performance SCs.

physics.chem-ph↗

Towards Universal Neural Network Potential for Material Discovery Applicable to Arbitrary Combination of 45 Elements

Computational material discovery is under intense study owing to its ability to explore the vast space of chemical systems. Neural network potentials (NNPs) have been shown to be particularly effective in conducting atomistic simulations for such purposes. However, existing NNPs are generally designed for narrow target materials, making them unsuitable for broader applications in material discovery. To overcome this issue, we have developed a universal NNP called PreFerred Potential (PFP), which is able to handle any combination of 45 elements. Particular emphasis is placed on the datasets, which include a diverse set of virtual structures used to attain the universality. We demonstrated the applicability of PFP in selected domains: lithium diffusion in LiFeSO${}_4$F, molecular adsorption in metal-organic frameworks, an order-disorder transition of Cu-Au alloys, and material discovery for a Fischer-Tropsch catalyst. They showcase the power of PFP, and this technology provides a highly useful tool for material discovery.

cond-mat.mtrl-sci↗