Results 111 to 120 of about 1,519 (247)
Deciphering Intricacies in Directional CO2 Conversion From Electrolysis to CO2 Batteries
This review will delve into the inherent connections and distinctions of CO2‐directed conversion in ECO2RR and CO2 batteries, in terms of product types, catalyst selection, catalytic mechanisms, and electrochemical performances, while proposing a benchmarking framework for the evaluation of CO2 batteries and innovative CO2 battery configurations for ...
Changfan Xu +5 more
wiley +1 more source
针对齿轮故障领域识别率低和识别时间长的问题,基于多尺度加权排列熵(multiscale weighted permutation entropy,简称MWPE)、蜣螂算法(dung beetle optimizer,简称DBO)与支持向量机(support vector machine,简称SVM)的原理,提出基于MWPE和DBO结合SVM的故障识别方法。首先,由于MWPE的嵌入维数难以确定且对结果影响较大,通过MWPE熵值分析引入变异系数(coefficient of variation,简称CV ...
doaj +1 more source
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
wiley +1 more source
Deep Learning‐Assisted Coherent Raman Scattering Microscopy
The analytical capabilities of coherent Raman scattering microscopy are augmented through deep learning integration. This synergistic paradigm improves fundamental performance via denoising, deconvolution, and hyperspectral unmixing. Concurrently, it enhances downstream image analysis including subcellular localization, virtual staining, and clinical ...
Jianlin Liu +4 more
wiley +1 more source
HEO‐regulated electronic structure drives nonradical PI activation, promoting ETP‐mediated polymerization of 4CP into high‐molecular‐weight products with enhanced surface enrichment. Concurrent dechlorination occurs via radical coupling and •OHads attack, enabling efficient carbon retention and selective product evolution at the liquid–solid interface.
Ziwei Yao +8 more
wiley +2 more sources
Topology‐Aware Machine Learning for High‐Throughput Screening of MOFs in C8 Aromatic Separation
We screened 15,335 Computation‐Ready, Experimental Metal–Organic Frameworks (CoRE‐MOFs) using a topology‐aware machine learning (ML) model that integrates structural, chemical, pore‐size, and topological descriptors. Top‐performing MOFs exhibit aromatic‐enriched cavities and open metal sites that enable π–π and C–H···π interactions, serving as ...
Yu Li, Honglin Li, Jialu Li, Wan‐Lu Li
wiley +1 more source
Hsf1 undergoes phase separation around target DNA, activating stress‐responsive transcription. DNA binding tunes conformational dynamics of Hsf1DBD, which propagates through large‐scale domain rearrangements involving DBD–IDR interactions to regulate phase separation. This hierarchical framework illustrates how local structural events at the side chain
Soichiro Kawagoe +2 more
wiley +2 more sources
How the nonstationarity in the atmosphere turbulent vertical velocity series affects its organization degree of multiscale structures is quantified by permutation entropy (PE) and complexity-entropy causality plane (CECP), and marked PE and CECP ...
Li Qinglei, Fu Zuntao
core +1 more source
Evolution of Physical Intelligence Across Scales
By following the evolution of physical intelligence across scales, this article shows how intelligence arises from materials, structures, physical interactions, and collectives. It establishes physical intelligence as the evolutionary foundation upon which embodied intelligence is built.
Ke Liu +7 more
wiley +1 more source
Cell Segmentation Beyond 2D—A Review of the State‐of‐the‐Art
Cell segmentation underpins many biological image analysis tasks, yet most deep learning methods remain limited to 2D despite the inherently 3D nature of cellular processes. This review surveys segmentation approaches beyond 2D, comparing 2.5D and fully 3D methods, analyzing 31 models and 32 volumetric datasets, and introducing a unified reference ...
Fabian Schmeisser +6 more
wiley +1 more source

