Results 131 to 140 of about 6,884,947 (256)

Atomic Tuning of Metal‐Support Interactions for Pathway‐Selective CO2 Photoreduction on TiO2

Advanced Science, EarlyView.
Single‐atom Fe and Cu catalysts anchored on TiO2 steer photocatalytic CO2 reduction toward distinct pathways. Fe sites favor rapid *CO desorption and selective CO formation, whereas Cu sites stabilize *CHO intermediates, enabling deep reduction and C─C coupling. Combined spectroscopy and DFT calculations reveal how metal–support interactions and oxygen
Dongyun Kim, Wonjae Ko, Byoung‐Hoon Lee, Sanghoon Kim, Yun Do Kim, Hyunsoo Ahn, Yoon Jung, Chan Woo Lee, Kug‐Seung Lee, Eunhee Gong, Junho Lee, Minho Kim, Taeghwan Hyeon, Su‐Il In   +13 more
wiley   +1 more source

Inductive transfer for learning Bayesian networks [PDF]

, 2009
In several domains it is common to have data from different, but closely related problems. For instance, in manufacturing, many products follow the same industrial process but with different conditions; or in industrial diagnosis, where there is ...
LUIS ENRIQUE SUCAR SUCCAR, EDUARDO FRANCISCO MORALES MANZANARES   +1 more
core  

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

Magnetoelectric Nanoparticle‐Based Wireless Brain–Computer Interface: Underlying Physics and Projected Technology Pathway

Advanced Science, EarlyView.
Magnetoelectric nanoparticles (MENPs) enable fully wireless, minutely invasive neuromodulation, and potentially neural recording, by converting magnetic into electric and, conversely, electric into magnetic fields, respectively, at high spatiotemporal resolution.
Elric Zhang, Max Shotbolt, Mostafa Abdel‐Mottaleb, Shawnus Chen, Victoria Andre, Jieyuan Tian, Jonathan Shulgach, Max Murphy, Brian Noga, Ping Liang, Darcy Griffin, Douglas Weber, Marta Pardo, Salvador Pane, Sakhrat Khizroev   +14 more
wiley   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

Advanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei, Yong Li, Chenchong Wang, Lingyu Wang, Xiang Song, Keming Mao, Yu Zhang, Wei Xu   +7 more
wiley   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

Advanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou, Lei Huang, Yan Zhou, Xiaohui Ning   +3 more
wiley   +1 more source

Front‐Loading Zn2+ via Lattice‐Breathing‐Enabled Early‐Stage Co‐Intercalation for Ultrastable Zn‐Ion Batteries

Advanced Science, EarlyView.
This study introduces In3+ into δ‐MnO2 to create a lattice breathing effect via a dynamic strain field, which establishes efficient ″ polaronic superhighways″ for charge transport. We uncover an unconventional two‐stage (Zn2+/H+ co‐insertion followed by Zn2+‐dominant) mechanism that enables reversible structural adjustment. This approach simultaneously
Haitao Li, Min Bu, Songlin Li, Huili Cao, Qi Lei, Yang Wang, Limin Zhou, Haigang Liu, Shumin Yang, Zhenhua Chen, Ying Zou, Bo Sun, Wen Wen, Yong Wang, Xiangzhi Zhang, Renzhong Tai   +15 more
wiley   +1 more source

Black Hole Quencher‐Enhanced Plasmonic Photothermal Conversion

Advanced Science, EarlyView.
Researchers have developed a universal method to boost plasmonic photothermal conversion by attaching black hole quencher (BHQ) molecules to matched metal nanostructures. This hybrid approach increases efficiency by roughly threefold, reaching up to 69.1% in the near‐infrared‐II window.
Ruiyuan Zhang, Xinru Chen, Lin Shen, Jin Li   +3 more
wiley   +1 more source