Tokyo Institute of Technology · 공학
유지오 쇼마 교수의 연구실은 탄소 포집 및 활용, 에너지 저장, 그리고 환경 친화적 재료 개발을 핵심으로 하는 고부가가치 화학 공정 기술을 연구하고 있습니다. 특히 이산화탄소를 활용한 고분자 기반 흡착제 개발, 이온 액체 기반 이온겔을 활용한 리튬-산소/이산화탄소 배터리 개발, 그리고 태양열을 이용한 에너지 절감형 탄소 포집 기술에 초점을 맞추고 있습니다. 또한 머신러닝 기반의 결정체 형성 예측 모델링을 통해 약물 제형 최적화에도 기여하고 있습니다.
표시된 성과는 수집된 데이터 기준으로 산출되며, 일부 차이가 있을 수 있습니다.
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
