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Conventional cement-based materials are faced with significant challenges, including large carbon emissions, high density, and quasi-brittleness. Here, inspired by hierarchical porous structures existing in nature, we develop a low carbon, lightweight, strong and tough cement-based material (LLST), which is obtained by a rapid gelation of hydrogel as skeleton and subsequent deposition of cement hydrates as a skin. As a result, the LLST exhibits hierarchical structure consisting of sponge-like micropores (1 ~ 50 μm) and nanopores (5 ~ 100 nm), without detrimental macropores that compromise light weight, strength, and toughness. Compared with the normal cement paste, LLST displays a 54% reduction in density, 145% and 1365% improvement in specific compressive strength and fracture energy, with only 51% carbon emission. These properties are further investigated with machine learning force field molecular dynamics along with well-tempered metadynamics simulations, indicating that strong chemical bonding is generated at the atomic level between functional groups in the hydrogel and Ca ion released from cement hydration. These findings not only demonstrate a strategy for developing lightweight building materials with low-carbon emission and remarkable mechanical properties, but also provide valuable insights for realizing the coexistence of light weight, strength and toughness by tailoring the hierarchical pore structure.
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http://dx.doi.org/10.1038/s41467-025-58339-8 | DOI Listing |
Drugs Aging
September 2025
Dalla Lana School of Public Health, University of Toronto, V1 06, 2075 Bayview Avenue, Toronto, ON, M4N 3M5, Canada.
Background And Objectives: Older adults living with dementia are a heterogeneous group, which can make studying optimal medication management challenging. Unsupervised machine learning is a group of computing methods that rely on unlabeled data-that is, where the algorithm itself is discovering patterns without the need for researchers to label the data with a known outcome. These methods may help us to better understand complex prescribing patterns in this population.
View Article and Find Full Text PDFJ Chem Theory Comput
September 2025
State Key Laboratory of Fine Chemicals, Frontiers Science Center for Smart Materials Oriented Chemical Engineering, Department of Pharmaceutical Sciences, Institute of Chemical Process Systems Engineering, School of Chemical Engineering, Dalian University of Technology, Dalian 116024, China.
Organometallic catalysis lies at the heart of numerous industrial processes that produce bulk and fine chemicals. The search for transition states and screening for organic ligands are vital in designing highly active organometallic catalysts with efficient reaction kinetics. However, identifying accurate transition states necessitates computationally intensive quantum chemistry calculations.
View Article and Find Full Text PDFRadiol Artif Intell
September 2025
Department of Radiology, Shanghai Jiao Tong University Medical School Affiliated Ruijin Hospital, No. 197 Ruijin Er Road, Shanghai 200025, China.
Purpose To assess the effectiveness of an explainable deep learning (DL) model, developed using multiparametric MRI (mpMRI) features, in improving diagnostic accuracy and efficiency of radiologists for classification of focal liver lesions (FLLs). Materials and Methods FLLs ≥ 1 cm in diameter at mpMRI were included in the study. nn-Unet and Liver Imaging Feature Transformer (LIFT) models were developed using retrospective data from one hospital (January 2018-August 2023).
View Article and Find Full Text PDFChemistry
September 2025
Hainan Institute of East China Normal University, State Key Laboratory of Petroleum Molecular & Process Engineering, Shanghai Key Laboratory of Green Chemistry and Chemical Process, School of Chemistry and Molecular Engineering, East China Normal University, 3663 North Zhongshan Rd., Shanghai, 20006
A novel sulfinamide reagent was developed that enables the one-step installation of sulfinamides through palladium-catalyzed coupling with aryl iodides. This method offers distinct advantages, including the use of readily available starting materials and broad substrate compatibility. Moreover, the strategy was successfully extended to the synthesis of complex functional molecules.
View Article and Find Full Text PDFSmall
September 2025
College of Chemistry, Chemical Engineering and Materials Science, Collaborative Innovation Center of Functionalized Probes for Chemical Imaging in Universities of Shandong, Key Laboratory of Molecular and Nano Probes, Ministry of Education, Shandong Normal University, Jinan, 250014, P. R. China.
The functionality of covalent organic frameworks (COFs) is usually highly related to their morphologies. Among various morphologies, the hollow-structured COFs have recently attracted intense attention due to their unique properties. Herein, the synthesis of hollow structured COFs are first reported with the chiral internal sites via combining the chiral templating method with the acid etching approach.
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