Results 151 to 160 of about 252,656 (347)

FY2010 Status Report on Advanced Neutronics Modeling and Validation

, 2010
USDOE Office of Nuclear Energy (NE), M. Smith, A. Mohamed, A. Marin-Lafleche, E. Lewis, K. Derstine, C. Lee, A. Wollaber, W. Yang   +8 more
openalex   +2 more sources

About neutrons, neutron metrology and neutron spectrometry

Delivered as part of the Soil Moisture Training on 22 January 2025. The project (21GRD08 SoMMet) has received funding from the European Partnership on Metrology, co-financed from the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States.
openaire   +2 more sources

Silicon‐Based Anodes for Sulfide Solid‐State Batteries: Failure Mechanisms and Multiscale Design Strategies

Advanced Energy Materials, EarlyView.
Silicon anodes in sulfide SSBs face coupled electrochemo‐mechanical failure by interface instability. This review examined recent advances and proposed mitigation strategies via material‐, electrode/interface‐, and cell‐level‐ engineering. We further evaluate scalable synthesis of sulfide SEs.
Murugesan Karuppaiah, JaeWook Seo, SeEun Park, Seung Hyeok Chu, Jung Kyoo Lee   +4 more
wiley   +1 more source

Site‐Selective B─H Activation via HAT Toward Xanthyl‐closo‐Carboranes as Bench‐Stable Precursors of Organosulfur Boron Clusters

Angewandte Chemie, EarlyView.
A light mediated direct regioselective B─H functionalization, via hydrogen atom transfer (HAT), is described. Unexplored xanthyl‐closo‐carboranes as novel bench‐stable and versatile platforms to access sulfur‐containing boron clusters are reported for the first time.
Marco Rusconi, Eugenia Magi, Polyssena Renzi, Emanuele Azzi, Valeria Lagostina, Enrico Salvadori, Mario Chiesa, Birgit Hischa, Giovanni Ghigo, Annamaria Deagostino   +9 more
wiley   +2 more sources

Comparison of DeePMD, MTP, GAP, ACE and MACE Machine‐Learned Potentials for Radiation‐Damage Simulations: A User Perspective

Advanced Intelligent Discovery, EarlyView.
The authors evaluated six machine‐learned interatomic potentials for simulating threshold displacement energies and tritium diffusion in LiAlO2 essential for tritium production. Trained on the same density functional theory data and benchmarked against traditional models for accuracy, stability, displacement energies, and cost, Moment Tensor Potential ...
Ankit Roy, Ram Devanathan, Sarah Allec, Giridhar Nandipati, Andrew M. Casella, David J. Senor, Duane D. Johnson, Ganesh Balasubramanian, Ayoub Soulami   +8 more
wiley   +1 more source

Controlling Exsolution Dynamics in High‐Entropy Oxides for Highly Active and Selective Acetylene Semi‐Hydrogenation

Angewandte Chemie, EarlyView.
Li+‐induced lattice distortion, valence variation, and local charge redistribution in a rock salt‐structured high‐entropy oxide enable controlled metal exsolution, altering the Cu–Ni–Co exsolution sequence and delivering a surface Cu0‐enriched catalyst with exceptional activity and ethylene selectivity for acetylene semi‐hydrogenation.
Hailing Yu, Caiqi Wang, Kevin M. Siniard, Qingju Wang, Yuanpeng Zhang, J. Anibal Boscoboinik, Xiao Tong, Eliseo Perez Gomez, Shuai Yuan, Arun S. Asundi, Oliver Mueller, Murillo Longo Martins, Yongqiang Cheng, Michael Richard Koehler, De‐en Jiang, Zili Wu, Zhenzhen Yang, Sheng Dai   +17 more
wiley   +2 more sources

Autonomous AI‐Driven Design for Skin Product Formulations

Advanced Intelligent Discovery, EarlyView.
This review presents a comprehensive closed‐loop framework for autonomous skin product formulation design. By integrating artificial intelligence‐driven experiment selection with automated multi‐tiered assays, the approach shifts development from trial‐and‐error to intelligent optimisation.
Yu Zhang, Yongrui Xiao, Chunlin Chen, Dimitrios Tsaoulidis, Kai Yang, Tao Chen   +5 more
wiley   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

Advanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi, Suman Timilsina, Youn Woo Shin, Yong Nam Kwon, Kee‐Sun Sohn, Ji Sik Kim   +5 more
wiley   +1 more source

Predicting Crystal Structures and Ionic Conductivities in Li3YCl6−xBrx Halide Solid Electrolytes Using a Fine‐Tuned Machine Learning Interatomic Potential

Advanced Intelligent Systems, EarlyView.
This study refines the Crystal Hamiltonian Graph Network to predict energies, structures, and lithium‐ion dynamics in halide electrolytes. By generating ordered structural models and using an iterative fine‐tuning workflow, we achieve near‐ab initio accuracy for phase stability and ionic transport predictions.
Jonas Böhm, Aurélie Champagne
wiley   +1 more source

Numerical simulation for the control rod assembly drop time evaluation in a LFR. [PDF]

Heliyon, 2022
Arthur EM, Zhang C, Debrah SK, Yamoah S, Wang L.   +4 more
europepmc   +1 more source