Results 71 to 80 of about 65,426 (218)

Programmable Variable‐Stiffness Multilayer Elastomer Composites With High‐Aspect‐Ratio Low‐Melting‐Point Alloy Inclusions

Advanced Materials Technologies, EarlyView.
High‐aspect‐ratio low‐melting‐point alloy inclusions are embedded within multilayer silicone laminates to create thermally programmable stiffness. Sequential activation of alloy‐rich layers produces four distinct mechanical states and reconfigures tendon‐driven actuator trajectories under the same load.
Palpolage Don Shehan Hiroshan Gunawardane, Gyan Edbert Zesiro, Hao Zhou, Gursel Alici, Mu Chiao   +4 more
wiley   +1 more source

Spatial Stiffness Programming in Single‐Filament Material Extrusion Through Data‐Driven Thermal Foaming

Advanced Materials Technologies, EarlyView.
Single‐filament material extrusion enables spatial stiffness programming through locally controlled thermal foaming of TPU. A data‐driven G‐code framework tunes porosity from 15.7% to 40.7% and hardness from 90 to 63 Shore A within and across layers. Monolithic hands and soft actuators consequently achieve distinct programmed motions without material ...
Rosanna Rifino, Gianni Stano, Mahdi Bodaghi, Gianluca Percoco   +3 more
wiley   +1 more source

Physical Origin of Temperature Induced Activation Energy Switching in Electrically Conductive Cement

Advanced Science, EarlyView.
The temperature‐induced Arrhenius activation energy switching phenomenon of electrical conduction in electrically conductive cement originates from structural degradation within the biphasic ionic‐electronic conduction architecture and shows percolation‐governed characteristics: pore network opening dominates the low‐percolation regime with downward ...
Jiacheng Zhang, Xinyuan Ke, Andrew Heath, Richard J. Ball, Jiawei Xie, Jinchen Fan, Guisheng Li, Kevin Paine   +7 more
wiley   +1 more source

Phase Diagrams and Piezoelectric Properties of Wurtzite Al1−x−yScxGdyN Heterostructural Alloys

Advanced Science, EarlyView.
This study demonstrates ferroelectricity and piezoelectric properties improvement of quaternary wurtzite Al1−x−yScxGdyN${\rm Al}_{1-x-y}{\rm Sc}_x{\rm Gd}_y{\rm N}$ films, guided by density functional theory calculations. Wurtzite Al1−x−yScxGdyN${\rm Al}_{1-x-y}{\rm Sc}_x{\rm Gd}_y{\rm N}$ films have a high optical bandgap, enhanced piezoelectric ...
Julia L. Martin, Cheng‐Wei Lee, Nate S.P. Bernstein, Thi Nguyen, Ande Bryan, Eli Cooper, John S. Mangum, Sage R. Bauers, Andriy Zakutayev, Keisuke Yazawa, Prashun Gorai, Rebecca W. Smaha   +11 more
wiley   +1 more source

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

Harnessing Phase Separation for the Development of High‐Performance Hydrogels

Advanced Science, EarlyView.
ABSTRACT Hydrogels are indispensable for the development of next‐generation bioelectronics, soft robotics, and biomedical devices, where their mechanical properties determine performance and reliability. Among strategies to enhance hydrogel mechanics, phase separation enables controlled heterogeneity resulting in gel networks that are reinforced by ...
Yue Shao, Yiming Ma, Baihao Shao, Molly M. Stevens   +3 more
wiley   +1 more source

Comparison Between Numerical and Experimental Methodologies for Total Enthalpy Determination in Scirocco PWT

Aerospace
Arc-jet facility tests are critical for replicating the extreme thermal conditions encountered during high-speed planetary entry, where the precise determination of flow enthalpy is essential.
Antonio Smoraldi, Luigi Cutrone
doaj   +1 more source

Interpretable Machine Learning Framework for Nb─Si Based Alloy Design with Enhanced Fracture Toughness

Advanced Science, EarlyView.
An interpretable machine learning framework integrating SHAP and PDP analysis identifies critical design descriptors from 139 physicochemical features for Nb─Si alloys. The framework achieves <7% prediction error and guides the discovery of Nb38.5Ti38.5Si3Zr18V2 alloy with 22.791 MPa·m1/2 fracture toughness, breaking the 20 MPa·m1/2 barrier.
Dezhi Chen, Chao Xu, Jingyue Yu, Qi Wang, Hongze Fang, Shuo Yin, Turab Lookman, Ruirun Chen   +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

Development and Applications of a Magnetism‐Aware General‐Purpose Atomic Cluster Expansion Potential for the Fe‐O‐H Ternary System

Advanced Science, EarlyView.
Hydrogen‐driven iron oxide reduction, water‐iron corrosion, and hydrogen embrittlement of metallic iron share the same Fe‐O‐H chemistry. A general‐purpose Atomic Cluster Expansion potential covers this chemistry across the full concentration range with explicit Ising‐like treatment of magnetism.
Baptiste Bienvenu, Mira Todorova, Jörg Neugebauer, Dierk Raabe, Matous Mrovec, Ralf Drautz   +5 more
wiley   +1 more source