Tokyo Institute of Technology · Engineering
Professor Yusuke Shimoyama's research lab specializes in advanced materials and chemical engineering for sustainable energy and environmental applications. Key research directions include the development of functional adsorbents for carbon dioxide capture and dye removal, innovative ionic liquid-based electrolytes and ionogels for next-generation batteries, and machine learning-assisted screening of cocrystals for pharmaceutical applications. The lab also investigates thermodynamic properties of complex fluid systems and novel photothermal materials to enable energy-efficient processes.
Figures are computed from collected data and may differ slightly.
This study focuses on development of a new adsorption technique by CO<sub>2</sub>-activated chitosan. Carbon dioxide was utilized to form the functional chemical groups of chitosan on the adsorptions of anionic dyes, Brilliant Blue FCF and Congo Red, in the aqueous solution. CO<sub>2</sub>-activated chitosan results in the dye adsorption significantly faster than that of chitosan in pure water. The adsorption capacities and removal efficiencies of the dye are increased by CO<sub>2</sub>-activate
The infinite dilution activity coefficients of C1 to C5 alcohols, acetone, 2-butanone, acetylacetone, toluene, and xylene isomers in 4-methyl-N-butylpyridinium tetrafluoroborate ([bmpy][BF4]) and 1-butyl-3-methylimidazolium hexafluorophosphate ([bmim][PF6]) were measured by gas−liquid chromatography (GC) from (306.6 to 334.8) K and atmospheric pressure. In these measurements with GC, [bmpy][BF4] or [bmim][PF6] was used as a stationary phase. The infinite dilution activity coefficients in this wo
Recently, drug modification via cocrystals has attracted great attention due to its high flexibility for the modulation of drug physicochemical properties. To reduce the cost of screening experiments, machine learning (ML) algorithms have proven to be one of the most effective ways to rapidly screen cocrystal formation. However, the choice of molecular descriptors has a significant impact on its prediction accuracy. In this work, two space-charge descriptors (COSMO-based σ-profile and three-dime
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