Results 121 to 130 of about 64,537 (299)

Dissimilar Electrolyte Decouples Zn and MnO2 Redox Chemistry Enabling Dual‐Electrode‐Free Lean‐Electrolyte Batteries

open access: yesAngewandte Chemie, EarlyView.
While dual‐electrode‐free Zn‐MnO2 batteries offer high voltage, intrinsic safety and simple manufacturing, their efficiency and stability are limited by incompatible Zn and MnO2 redox chemistries. This work introduces a dissimilar electrolyte architecture to decouple Zn and MnO2 reactions, endowing a dual‐electrode‐free and lean‐electrolyte Zn‐MnO2 ...
Xian Xie   +11 more
wiley   +2 more sources

Dissimilar Joining of Metals by Powder Metallurgy Route [PDF]

open access: yes, 2015
Dissimilar metal joints have a wide range of applications in electronic connectors, due to its physical and mechanical properties. In the present work powder brazing is chosen as a tool for joining of Cu-SS, Cu-Fe, and Cu-Ni. Powder brazing of dissimilar
Das, Abhijit Kumar
core  

Universally Accurate or Specifically Inadequate? Stress‐Testing General Purpose Machine Learning Interatomic Potentials

open access: yesAdvanced Intelligent Discovery, EarlyView.
We investigate MACE‐MP‐0 and M3GNet, two general‐purpose machine learning potentials, in materials discovery and find that both generally yield reliable predictions. At the same time, both potentials show a bias towards overstabilizing high energy metastable states. We deduce a metric to quantify when these potentials are safe to use.
Konstantin S. Jakob   +2 more
wiley   +1 more source

Practical small-scale explosive seam welding [PDF]

open access: yes, 1983
A small-scale explosive seam welding process has been developed that can significantly contribute to remote metal joining operations under hazardous or inaccessible conditions, such as nuclear reactor repair and assembly of structure in space. This paper
Bement, L. J.
core   +2 more sources

A Generalized Framework for Data‐Efficient and Extrapolative Materials Discovery for Gas Separation

open access: yesAdvanced Intelligent Discovery, EarlyView.
This study introduces an iterative supervised machine learning framework for metal‐organic framework (MOF) discovery. The approach identifies over 97% of the best performing candidates while using less than 10% of available data. It generalizes across diverse MOF databases and gas separation scenarios.
Varad Daoo, Jayant K. Singh
wiley   +1 more source

Effect of preheating temperature on post-weld residual stress of dissimilar steel plates

open access: yesMetalurgija, 2020
Based on the numerical simulation software Visual-Environment, the numerical calculation and analysis of residual stress field under different preheating temperatures for Q345/2Cr13 dissimilar plate welding were carried out in this paper.
H. Fu   +6 more
doaj  

Analysis of the plugging of the systems autonomy demonstration project brassboard filters [PDF]

open access: yes
A fine gray powder was clogging the brassboard filters. The powder appeared to be residue from a galvanic corrosive attack by ammonia of the aluminum and stainless steel components in the system.
Clay, John C.
core   +1 more source

Automated Alignment Powered by Computer Vision Streamlines the Two‐Photon Polymerization‐Based Micro 3D Printing of Multiscale and Multimaterial Structures

open access: yesAdvanced Intelligent Discovery, EarlyView.
Two‐photon polymerization enables high‐resolution microfabrication, but performing alignment when printing multiple structures is difficult. Here, we present a fast, robust, and open‐source protocol for automated alignment on Nanoscribe systems. Achieving ≈0.4 μm accuracy in under 5 s, our protocol reduces time and error in multimaterial printing. This
Daniel Maher   +4 more
wiley   +1 more source

AI‐Guided Co‐Optimization of Advanced Field‐Effect Transistors: Bridging Material, Device, and Fabrication Design

open access: yesAdvanced Intelligent Discovery, EarlyView.
This article outlines how artificial intelligence could reshape the design of next‐generation transistors as traditional scaling reaches its limits. It discusses emerging roles of machine learning across materials selection, device modeling, and fabrication processes, and highlights hierarchical reinforcement learning as a promising framework for ...
Shoubhanik Nath   +4 more
wiley   +1 more source

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