Results 181 to 190 of about 1,072,947 (315)

Atomistic Mechanisms Triggered by Joule Heating Effects in Metallic Cu‐Bi Nanowires for Spintronics

Advanced Materials, EarlyView.
Bi doped metallic Cu nanowires are promising for spintronics thanks to the stabilization of a giant spin Hall effect. However, heat resulting from current injection forces Bi to leave solution, forcing segregation into monoatomic decorations which evolve into coherent crystalline aggregates.
Alejandra Guedeja‐Marrón, Juan Ignacio Beltrán, Matilde Saura‐Múzquiz, Paolo Perna, M. Carmen Muñoz, Lucas Pérez, Maria Varela   +6 more
wiley   +1 more source

Stochastic mutation as a mechanism for the emergence of SARS-CoV-2 new variants. [PDF]

Virus Res
Luo L, Lv J.
europepmc   +1 more source

An Efficient Decision Tree Algorithm Using Rough Set Theory

, 2011
Manisha Tantuway
openalex   +1 more source

Reassessing Electrolyte Design for Non‐Aqueous Magnesium Batteries: Atomistic Structures and Performance Optimization

Advanced Materials, EarlyView.
This review systematically examines recent advances in non‐aqueous electrolytes for rechargeable magnesium batteries, focusing on chlorine‐containing and chlorine‐free systems. It highlights the interdependent relationship between electrolyte compositions, atomistic structures, and electrochemical performance.
Hao Xu, Xiaoqian He, Yue Li, Quanquan Guo, Xinyu Sun, Xinlong Xie, Lingyue Liu, Richard M. Laine, Jianxin Zou, Xinliang Feng, Minghao Yu   +10 more
wiley   +1 more source

Batesian Mimicry Converges toward Inaccuracy in Myrmecomorphic Spiders. [PDF]

Syst Biol
Kelly MBJ, Derkarabetian S, McLean DJ, Shofner R, Grismado CJ, Haddad CR, Cassis G, Giribet G, Herberstein ME, Wolff JO.   +9 more
europepmc   +1 more source

Learning Crystallographic Disorder: Bridging Prediction and Experiment in Materials Discovery

Advanced Materials, EarlyView.
Machine learning based computational materials discovery workflows have recently proposed thousands of potentially stable crystalline materials. However, the experimental realization of these predictions is often challenging because the models assume perfectly ordered structures.
Konstantin S. Jakob, Aron Walsh, Karsten Reuter, Johannes T. Margraf   +3 more
wiley   +1 more source

Surface optimization governs the local design of physical networks. [PDF]

Nature
Meng X, Piazza B, Both C, Barzel B, Barabási AL.   +4 more
europepmc   +1 more source

Active Learning‐Guided Accelerated Discovery of Ultra‐Efficient High‐Entropy Thermoelectrics

Advanced Materials, EarlyView.
An active learning framework is introduced for the accelerated discovery of high‐entropy chalcogenides with superior thermoelectric performance. Only 80 targeted syntheses, selected from 16206 possible combinations, led to three high‐performance compositions, demonstrating the remarkable efficiency of data‐driven guidance in experimental materials ...
Hanhwi Jang, Wooseok Lee, Hwa‐Jung Kim, Sohyang Cha, Hosun Shin, Won Bo Lee, Min‐Wook Oh, Yeon Sik Jung, YongJoo Kim   +8 more
wiley   +1 more source

The promise and challenge of spatial inference with the full ancestral recombination graph under Brownian motion. [PDF]

G3 (Bethesda)
Deraje P, Kitchens J, Coop G, Osmond MM.
europepmc   +1 more source

Moth‐Wing‐Inspired Multifunctional Metamaterials

Advanced Materials, EarlyView.
This study develops a moth‐wing‐inspired heterogeneous metamaterial that achieves synergistic broadband sound absorption, mechanical energy dissipation, and thermal insulation within a lightweight architected framework. Combining bioinspired gradient design, genetic‐algorithm optimization, and additive manufacturing, the bionic heterogeneous acoustic ...
Haoran Pei, Hang Yang, Ning Zhang, Tian Li, Xinxin Wang, Miao Zhao, Shuwei Ding, Xin Wang, Qinniu Lv, Zijie Xu, Yinghong Chen, Xinwei Li, Wei Zhai   +12 more
wiley   +1 more source