The successful synthesis of hierarchically structured titanium silicalite-1 (TS-1) with large intracrystalline macropores by steam-assisted crystallisation of mesoporous silica particles is reported. The macropore topology was imaged in 3D by using electron tomography and synchrotron radiation-based ptychographic X-ray computed tomography, revealing interconnected macropores within the crystals accounting for about 30 % of the particle volume. The study of the macropore formation mechanism revealed that the mesoporous silica particles act as a sacrificial macropore template during the synthesis. Silicon-to-titanium ratio of the macroporous TS-1 samples was successfully tuned from 100 to 44. The hierarchically structured TS-1 exhibited high activity in the liquid phase epoxidation of 2-octene with hydrogen peroxide. The hierarchically structured TS-1 surpassed a conventional nano-sized TS-1 sample in terms of alkene conversion and showed comparable selectivity to the epoxide. The flexible synthesis route described here can be used to prepare hierarchical zeolites with improved mass transport properties for other selective oxidation reactions.
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http://dx.doi.org/10.1002/chem.201903287 | DOI Listing |
BMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFInt J Biol Macromol
January 2025
School of Chemistry and Materials Science, Hubei Engineering University, Xiaogan 43200, China.
Passive Radiant Cooling and Heating are green and sustainable methods of radiant heat management without consuming additional energy. However, the absorption of sunlight and poor insulation of materials can reduce radiative cooling and also affect radiative heating performance. Herein, we have constructed porous hierarchical dual-mode silk nanofibrous aerogel (SNF) films with high mechanical toughness and stability using silk nanofibers/GO.
View Article and Find Full Text PDFACS Nano
January 2025
State Key Laboratory of Polymer Materials Engineering, Polymer Research Institute of Sichuan University, Chengdu 610065, China.
Daytime radiative cooling (DRC) materials offer a sustainable, pollution-free passive cooling solution. Traditional DRC materials are usually white to maximize solar reflectance, but applications like textiles and buildings need more aesthetic options. Unfortunately, colorizing DRC materials often reduce cooling efficiency due to colorant sunlight absorption.
View Article and Find Full Text PDFJ Am Chem Soc
January 2025
Department of Chemistry, Tsinghua University, Beijing 100084, P. R. China.
Coordinatively unsaturated copper (Cu) has been demonstrated to be effective for electrifying CO reduction into C products by adjusting the coupling of C-C intermediates. Nevertheless, the intuitive impacts of ultralow coordination Cu sites on C products are scarcely elucidated due to the lack of synthetic recipes for Cu with low coordination numbers and its vulnerability to aggregation under reductive potentials. Herein, computational predictions revealed that Cu sites with higher levels of coordinative unsaturation favored the adsorption of C and C intermediates.
View Article and Find Full Text PDFSensors (Basel)
January 2025
School of Computer Science and Technology, Beijing Institute of Technology, Beijing 100811, China.
While deep learning techniques have been extensively employed in malware detection, there is a notable challenge in effectively embedding malware features. Current neural network methods primarily capture superficial characteristics, lacking in-depth semantic exploration of functions and failing to preserve structural information at the file level. Motivated by the aforementioned challenges, this paper introduces MalHAPGNN, a novel framework for malware detection that leverages a hierarchical attention pooling graph neural network based on enhanced call graphs.
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