A historical sketch of the MCD (magnetic circular dichroism) spectroscopy is reported in its experimental and theoretical aspects. MCPL (magnetic circularly polarized luminescence) is also considered. The main studies are presented encompassing porphyrinoid systems, aggregates and materials, as well as simple organic molecules useful for the advancement of the interpretation. The MCD of chiral systems is discussed with special attention to new studies of natural products with potential pharmaceutical valence, including Amaryllidaceae alkaloids and related isocarbostyrils. Finally, the vibrational form of MCD, called MVCD, which is recorded in the IR part of the spectrum is also discussed. A final brief note on perspectives is given.
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http://dx.doi.org/10.1016/j.saa.2024.124583 | DOI Listing |
Langmuir
January 2025
College of Chemistry and Chemical Engineering, Qingdao University, Qingdao 266071, China.
The recovery of valuable materials from spent lithium-ion batteries (LIBs) has experienced increasing demand in recent years. Current recycling technologies are typically energy-intensive and are often plagued by high operation costs, low processing efficiency, and environmental pollution concerns. In this study, an efficient and environmentally friendly dielectrophoresis (DEP)-based approach is proposed to separate the main components of "black mass" mixtures from LIBs, specifically lithium iron phosphate (LFP) and graphite, based on their polarizability differences.
View Article and Find Full Text PDFLangmuir
January 2025
Department of Environmental Chemistry and Chemical Engineering, School of Advanced Engineering, Kogakuin University, 2665-1 Nakano, Tokyo, Hachioji 192-0015, Japan.
The two-dimensional interlayer space of layered materials has been highlighted due to their adsorption property, whose nanostructure in the water-immersed state is scarcely understood by experiment. Recent developments in molecular simulation have enabled researchers to investigate the interlayer structure, but water content is necessary for accurate modeling. In the present study, we proposed a theoretical method to estimate the saturated water content and adsorption selectivity of trichlorophenol and phenol in montmorillonite modified with hexadecyltrimethylammonium ions.
View Article and Find Full Text PDFNatl Sci Rev
January 2025
Key Laboratory for Thermal Science and Power Engineering of Ministry of Education, Department of Engineering Mechanics, Tsinghua University, Beijing 100084, China.
The high thermopower of ionic thermoelectric (-TE) materials holds promise for miniaturized waste-heat recovery devices and thermal sensors. However, progress is hampered by laborious trial-and-error experimentations, which lack theoretical underpinning. Herein, by introducing the simplified molecular-input line-entry system, we have addressed the challenge posed by the inconsistency of -TE material types, and present a machine learning model that evaluates the Seebeck coefficient with an of 0.
View Article and Find Full Text PDFPurpose: To theoretically and experimentally study implant lead tip heating caused by radiofrequency (RF) power deposition in different wire configurations that contain loop(s).
Methods: Maximum temperature rise caused by RF heating was measured at 1.5T on 20 insulated, capped wires with various loop and straight segment configurations.
Front Psychol
December 2024
Department of Science and Technology, Hebei Agricultural University, Huanghua, China.
This study aims to explore various key factors influencing the academic performance of college students, including metacognitive awareness, learning motivation, participation in learning, environmental factors, time management, and mental health. By employing the chi-square test to identify features closely related to academic performance, this paper discussed the main influencing factors and utilized machine learning models (such as LOG, SVC, RFC, XGBoost) for prediction. Experimental results indicate that the XGBoost model performs the best in terms of recall and accuracy, providing a robust prediction for academic performance.
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