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Anion doping engineering is an effective method to regulate the electronic structure of transition metal dichalcogenides (TMDs), especially at the electron orbital level. Based on electromagnetic wave (EMW) loss theory, this study innovatively constructs dipole polarization sites S doping in FeSe. The electronic structure of these sites is systematically analyzed to reveal charge redistribution and bond hybridization induced by dopant incorporation. Notably, controlled sulfur doping induces Fe-to-S charge transfer, enhances interatomic electron transfer, and forms numerous dipolar polarization centers, enabling precise tuning of EM parameters. Density of states and wave function calculations reveal strong Fe 3d-S 3p orbital hybridization, intensifying local charge imbalances and dipole polarization. S doping also modulates the bandgap, suppresses free electron concentration, and improves impedance matching. Compared with Pure FeSe, the effective absorption bandwidth (EAB) of FeSeS reaches 5.52 GHz at a thickness of only 1.3 mm, an increase of 132.5%. The minimum reflection loss (RL) is -59.6 dB, an improvement of 196.5%. This work provides a theoretical foundation for constructing polarization centers.
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http://dx.doi.org/10.1039/d5mh00986c | DOI Listing |
Adv Mater
September 2025
Beijing Advanced Innovation Center for Soft Matter Science and Engineering, State Key Laboratory of Organic-Inorganic Composites, Bionanomaterials & Translational Engineering Laboratory, Beijing Key Laboratory of Bioprocess, Beijing Laboratory of Biomedical Materials, Beijing University of Chemical
Sonocatalytic therapy (SCT) is a non-invasive tumor treatment modality that utilizes ultrasound (US)- activated sonocatalysts to generate reactive oxygen species (ROS), whose production critically dependent on the electronic structural properties of the catalytic sites. However, the spin state, which is a pivotal descriptor of electronic properties, remains underappreciated in SCT. Herein, a Ti-doped zirconium-based MOF (Ti-UiO-66, denoted as UTN) with ligand-deficient defects is constructed for SCT, revealing the important role of the electronic spin state in modulating intrinsic catalytic activity.
View Article and Find Full Text PDFJ Mater Chem B
September 2025
Department of Chemistry, University of Waterloo, 200 University Ave. West, Waterloo, ON N2L 3G1, Canada.
Conjugated polymer nanoparticles (CPNs), especially poly(-phenylene ethynylene) nanoparticles (PPE-NPs), are promising candidates for bio-imaging due to their high photostability, adjustable optical characteristics, and biocompatibility. Despite their potential, the fluorescence mechanisms of these nanoparticles are not yet fully understood. In this work, we modeled a spherical PPE-NP in a water environment using 30 PPE dimer chains.
View Article and Find Full Text PDFNano Lett
September 2025
Key Laboratory of Micro & Nano Photonic Structures, Department of Optical Science and Engineering, College of Future Information Technology, Fudan University, Shanghai 200433, China.
The separation and propagation of spin are vital to understanding spin-orbit coupling (SOC) in quantum systems. Exciton-polaritons, hybrid light-matter quasiparticles, offer a promising platform for investigating SOC in quantum fluids. By utilization of the optical anisotropy of materials, Rashba-Dresselhaus SOC (RDSOC) can be generated, enabling robust polariton spin transport.
View Article and Find Full Text PDFJ Chem Inf Model
September 2025
Faculty of Pharmacy, University of Medicine and Pharmacy at Ho Chi Minh City, 41 Dinh Tien Hoang, District 1, Ho Chi Minh City 700000, Vietnam.
Molecular property prediction has become essential in accelerating advancements in drug discovery and materials science. Graph Neural Networks have recently demonstrated remarkable success in molecular representation learning; however, their broader adoption is impeded by two significant challenges: (1) data scarcity and constrained model generalization due to the expensive and time-consuming task of acquiring labeled data and (2) inadequate initial node and edge features that fail to incorporate comprehensive chemical domain knowledge, notably orbital information. To address these limitations, we introduce a Knowledge-Guided Graph (KGG) framework employing self-supervised learning to pretrain models using orbital-level features in order to mitigate reliance on extensive labeled data sets.
View Article and Find Full Text PDFWater Res
September 2025
State Key Laboratory of Soil Pollution Control and Safety, Department of Environmental Science, Zhejiang University, Hangzhou 310058, China; Future Environment Laboratory, Innovation Center of Yangtze River Delta, Zhejiang University, Jiaxing 314100, China. Electronic address:
Accelerating the rate-limiting surface Fe(III)/Fe(II) redox cycling is pivotal for efficient iron-mediated Fenton-like decontamination, yet conventional reductants (e.g., toxic hydroxylamine, thiosulfate) suffer from secondary toxicity, self-quenching, and heavy metal leaching.
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