Results 201 to 210 of about 51,743 (307)

Phase Diagrams Enable Solid‐State Battery Design

open access: yesAdvanced Materials Interfaces, EarlyView.
Batteries are non‐equilibrium devices with inherent thermodynamic driving forces to react at interfaces, regardless of kinetics or operating conditions. Chemical potential mismatches across interfaces are dissipated via interfacial reactions. In this work, it is illustrated how phase diagrams and chemical potential maps predict degradation pathways but
Nathaniel L. Skeele, Matthias T. Agne
wiley   +1 more source

A Family of Sodium Solid‐State Electrolytes Based on the NaGaxAl1‐xCl4 Solid Solution

open access: yesAdvanced Materials Interfaces, EarlyView.
ABSTRACT Sodium‐based metal chloride solid electrolytes are promising for sodium solid‐state batteries due to their excellent oxidation stability, which, as shown for Li halides, can coexist with high ionic conductivity. To explore cationic substitution effects, we synthesized NaGaxAl1‐xCl4 (0 ≤ x ≤ 1) via ball milling and investigated structural and ...
Hao Guo, Matteo Bianchini
wiley   +1 more source

The Role of STEM Teaching in Education: An Empirical Study to Enhance Creativity and Computational Thinking. [PDF]

open access: yesJ Intell
Suherman S   +5 more
europepmc   +1 more source

Advanced Design for Weakly Coupled Resonators by Automatic Active Optimization

open access: yesAdvanced Materials Technologies, EarlyView.
An Automatic Active Optimization (AAO) strategy integrates machine learning predictors and genetic algorithms in a closed‐loop workflow. By iteratively expanding its dataset with new discoveries, AAO overcomes the limits of conventional methods. This approach finds superior microstructural designs beyond the initial sample space. We demonstrate this on
Wei Yue   +8 more
wiley   +1 more source

Characterization of Droplet Formation in Ultrasonic Spray Coating: Influence of Ink Formulation Using Phase Doppler Anemometry and Machine Learning

open access: yesAdvanced Materials Technologies, EarlyView.
This study explores how machine learning models, trained on small experimental datasets obtained via Phase Doppler Anemometry (PDA), can accurately predict droplet size (D32) in ultrasonic spray coating (USSC). By capturing the influence of ink complexity (solvent, polymer, nanoparticles), power, and flow rate, the model enables precise droplet control
Pieter Verding   +5 more
wiley   +1 more source

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