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Crystalline Covalent Triazine Frameworks and 2D Triazine Polymers: Synthesis and Applications.
Acc Chem Res
January 2025
School of Engineering, Westlake University, Hangzhou 310024, Zhejiang Province, China.
ConspectusCovalent triazine frameworks (CTFs) are a novel class of nitrogen-rich conjugated porous organic materials constructed by robust and functional triazine linkages, which possess unique structures and excellent physicochemical properties. They have demonstrated broad application prospects in gas/molecular adsorption and separation, catalysis, energy conversion and storage, etc. In particular, crystalline CTFs with well-defined periodic molecular network structures and regular pore channels can maximize the utilization of the features of CTFs and promote a deep understanding of the structure-property relationship.
View Article and Find Full Text PDFSynthesis of chiral mesoporous silica nanoparticles for the adsorptive removal of the chiral insecticide sulfoxaflor from water.
Nanoscale Adv
December 2024
Department of Pharmaceutical Chemistry, College of Pharmacy, Taif University Taif 21944 Saudi Arabia.
Mesoporous materials have garnered significant interest because of their porous structure, large surface area and ease of surface functionalization to incorporate the functional groups of choice. Herein, chiral mesoporous silica nanoparticles (CMSNPs) were prepared using quaternary amino silane as the template, tetramethyl orthosilicate as the silica source and proline and cellulose as chiral selector. The developed CMSNPs were characterized by scanning electron microscopy (SEM), transmission electron microscopy (TEM), elemental analysis, Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction analysis, BET surface area analysis and BJH pore size/volume analysis.
View Article and Find Full Text PDFClinClip: a Multimodal Language Pre-training model integrating EEG data for enhanced English medical listening assessment.
Front Neurosci
January 2025
The Basic Department, The Tourism College of Changchun University, Changchun, China.
Introduction: In the field of medical listening assessments,accurate transcription and effective cognitive load management are critical for enhancing healthcare delivery. Traditional speech recognition systems, while successful in general applications often struggle in medical contexts where the cognitive state of the listener plays a significant role. These conventional methods typically rely on audio-only inputs and lack the ability to account for the listener's cognitive load, leading to reduced accuracy and effectiveness in complex medical environments.
View Article and Find Full Text PDFDesign of natural asphalt sulfamic acid (NA-NHSOH) as a scalable natural asphalt-derived heterogeneous Brønsted acid to catalyze multicomponent reactions meeting green chemistry goals.
Heliyon
January 2025
Department of Chemistry, Faculty of Basic Sciences, Ilam University, P.O. Box 69315516, Ilam, Iran.
This study highlights an innovative approach to catalysis by utilizing natural asphalt as a support material for developing carbon-based catalysts. By leveraging the principles of green chemistry, the research aims to create recyclable and environmentally friendly heterogeneous catalytic systems. This aligns with the growing demand for greener technologies and the use of biocompatible materials in chemical processes.
View Article and Find Full Text PDFDeciphering allosteric mechanisms in KRAS activation: insights from GTP-induced conformational dynamics and interaction network reorganization.
RSC Adv
January 2025
College of Agriculture and Biological Science, Dali University Dali 671000 China
The conformational dynamics and activation mechanisms of KRAS proteins are of great importance for targeted cancer therapy. However, the detailed molecular mechanics of KRAS activation induced by GTP binding remains unclear. In this study, we systematically investigated how GTP/GDP exchange affects the thermodynamic and kinetic properties of KRAS and explored the activation mechanism using molecular dynamics (MD) simulations, Markov state models (MSMs), and neural relational inference (NRI) models.
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