Results 121 to 130 of about 7,196 (229)

Decoupling Chemical and Mechanical Contributions to Capacity Fading in Ni‐Rich Cathodes for Sulfide‐Based All‐Solid‐State Batteries

Advanced Energy Materials, EarlyView.
Ni‐rich cathodes in sulfide‐based ASSBs undergo intertwined (electro)chemical and mechanical degradation. Here, a new diagnostic framework is presented to decouple each contribution by partitioning capacity fading into charge and discharge fading.
Jangwhan Seok, Chanyou Chung, Hyunbeom Lee, Subin Ahn, Seongeun Lee, Sangbin Park, Sunhyun Hwang, Jaeyoung Kim, Jeonghyun Seo, Sungwoo Noh, Juyeong Seong, Sangheon Lee, Won‐Sub Yoon   +12 more
wiley   +1 more source

Fusion of multi-scale geometric features and frequency domain decomposition for stereo matching network. [PDF]

PLoS One
Hou H, Wang D, Xu J, Wang Y.
europepmc   +1 more source

Alcohol‐Adduct Mediated Precursor Stabilization Strategies for Conformal SnO2 Layers in Highly Efficient and Stable Perovskite Solar Modules

Advanced Energy Materials, EarlyView.
An alcohol‐adduct‐mediated strategy is employed to stabilize tin isopropoxide for the formation of a homogeneous double electron transport layer (ETL) incorporating SnO2 nanoparticles (NPs). This double‐ETL exhibits improved optical and charge‐transport properties due to reduced defect density and better energy level alignment, resulting in high ...
You‐Hyun Seo, Eun Young Park, Chan Su Moon, Dong Gyu Lee, Junho Park, Fabian Rotermund, Gill Sang Han, Tae Kyung Lee, Nam Joong Jeon, Bong Joo Kang, Young Yun Kim   +10 more
wiley   +1 more source

Research on anomaly detection and operational status evaluation methods for smart electricity meters based on hybrid deep learning. [PDF]

PLoS One
Zhang J, Zhao Y, Zhang J, Bai Z.
europepmc   +1 more source

Verification and Mitigation of Proton‐Induced Non‐Ionizing Damage in Perovskite Solar Cells for Space Applications

Advanced Energy Materials, EarlyView.
Metal‐halide perovskite solar cells offer high‐efficiency power for orbital missions but suffer from permanent proton‐induced degradation. This work identifies non‐ionizing energy loss (NIEL) as the primary driver of irreversible failure, inducing atomic displacements and microcracks in the high‐fluence regime. Through energy‐tuned mapping and scalable
Jangwon Byun, Sung‐Pil Yoon, Beomjun Park, Sung‐Jun Park, Dong Hoe Kim, Sunwoo Kim, Jin‐Wook Lee, Sangwook Lee, Hyun Suk Jung, Nam‐Gyu Park, Man‐Jong Lee   +10 more
wiley   +1 more source

A novel qutrit representation for RGB digital images. [PDF]

Sci Rep
Rofail M, Montaser R, Younes A.
europepmc   +1 more source

A Closed‐Loop Direct Recycling Process for Prussian White From Scrap and End‐of‐Life Sodium‐Ion Batteries

Advanced Energy Materials, EarlyView.
A closed‐loop direct recycling route for Prussian White sodium‐ion battery cathodes is demonstrated. Ice‐stripped scrap and end‐of‐life electrodes are regenerated via low‐temperature aqueous resodiation (80°C), restoring sodium content, Fe2+ redox chemistry, and structural integrity.
Subha Samanta, Bowen Liu, Dongrui Xie, Joel Cornelio, Wilgner Lima da Silva, Manoj Mayaji Ovhal, Anton Zorin, Peter R. Slater, Emma Kendrick   +8 more
wiley   +1 more source

Deep Learning‐Assisted Coherent Raman Scattering Microscopy

Advanced Intelligent Discovery, EarlyView.
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, Rujia Liu, Xinyan Zhang, Xun Chen, Shuhua Yue   +4 more
wiley   +1 more source

A wavelet-integrated framework for feature extraction and background refinement in hyperspectral anomaly detection. [PDF]

Sci Rep
Küçük F.
europepmc   +1 more source

Harnessing Machine Learning to Understand and Design Disordered Solids

Advanced Intelligent Discovery, EarlyView.
This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley   +1 more source