Results 101 to 110 of about 14,407 (260)

Triple Matrix Confinement‐Induced Ultrabright Afterglow From Carbon Dots With Multivariate Responsive Afterglow Colors for Advanced Dynamic Information Encryption

Angewandte Chemie, EarlyView.
A novel carbon dots material (BUL‐CDs) with an initial afterglow brightness (406 cd m−2) far exceeding that of other afterglow luminescent materials was developed. Furthermore, the BUL‐CDs exhibited time‐dependent afterglow colors, excitation‐dependent afterglow colors, and thermochromic afterglow in response to triplet variables.
Yupeng Liu, Yiming Hao, Yi Li, Xiaofan Xia, Xichang Ou, Xue Wu, Jun Wu, Ruifeng Zheng, Dongbo Guo, Shi Chen, Jing Li, Jinyang Zhu, Qijun Li, Songnan Qu   +13 more
wiley   +2 more sources

Inverse Design of Alloys via Generative Algorithms: Optimization and Diffusion within Learned Latent Space

Advanced Intelligent Discovery, EarlyView.
This work presents a novel generative artificial intelligence (AI) framework for inverse alloy design through operations (optimization and diffusion) within learned compact latent space from variational autoencoder (VAE). The proposed work addresses challenges of limited data, nonuniqueness solutions, and high‐dimensional spaces.
Mohammad Abu‐Mualla, Ellis Crabtree, Fredrick Michael, Yayue Pan, Jida Huang   +4 more
wiley   +1 more source

Deep Learning‐Assisted Design of Mechanical Metamaterials

Advanced Intelligent Discovery, EarlyView.
This review examines the role of data‐driven deep learning methodologies in advancing mechanical metamaterial design, focusing on the specific methodologies, applications, challenges, and outlooks of this field. Mechanical metamaterials (MMs), characterized by their extraordinary mechanical behaviors derived from architected microstructures, have ...
Zisheng Zong, Pu Wen, Zhiping Chai, Dunxuan Wu, Han Ding, Zhigang Wu   +5 more
wiley   +1 more source

Possibilities of constructing design load diagrams of a helical cable shock damper when the direction of inertia forces changes

RUDN Journal of Engineering Research
Currently, experimental load diagrams in several main directions of load perception are used to describe the stiffness characteristics of cable shock dampers.
Elena M. Reizmunt, Sergey V. Doronin
doaj   +1 more source

Advances in Thermal Modeling and Simulation of Lithium‐Ion Batteries with Machine Learning Approaches

Advanced Intelligent Discovery, EarlyView.
Heat generation in lithium‐ion batteries affects performance, aging, and safety, requiring accurate thermal modeling. Traditional methods face efficiency and adaptability challenges. This article reviews machine learning‐based and hybrid modeling approaches, integrating data and physics to improve parameter estimation and temperature prediction ...
Qi Lin, Kang Fu, Junjie Ding, Kai Sun, Peng Tan   +4 more
wiley   +1 more source

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

Advanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Gut microbiota–derived short‐chain fatty acids regulate group 3 innate lymphoid cells in HCC

Hepatology, EarlyView., 2022
Abstract Background and Aims Type 3 innate lymphoid cells (ILC3s) are essential for host defense against infection and tissue homeostasis. However, their role in the development of HCC has not been adequately confirmed. In this study, we investigated the immunomodulatory role of short‐chain fatty acids (SCFAs) derived from intestinal microbiota in ILC3
Chupeng Hu, Bingqing Xu, Xiaodong Wang, Wen‐Hua Wan, Jinying Lu, Deyuan Kong, Yu Jin, Wenhua You, Hua Sun, Xiaoxin Mu, Dongju Feng, Yun Chen   +11 more
wiley   +1 more source

Application of multivariate statistics and geostatistical techniques to identify the spatial variability of heavy metals in groundwater resources

Caspian Journal of Environmental Sciences, 2015
The performance of geostatistical and spatial interpolation techniques were investigated for estimation of spatial variability of heavy metals and water quality mapping of groundwater resources in Ramiyan district (Golestan province, Iran).
Khanduzi, F., Parizanganeh, A., Zamani, A.   +2 more
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