Results 181 to 190 of about 6,441,961 (279)

Organic Electrochemical Transistors for Energy‐Autonomous Bioelectronics: Materials, Devices, and System Integration

open access: yesAdvanced Electronic Materials, EarlyView.
This review surveys organic electrochemical transistors as core building blocks for energy‐autonomous bio‐integrated electronics. We outline device physics, OMIEC materials, and electrolyte effects that enable sub‐volt, high‐gain operation, and discuss direct coupling with mechanical, thermal, and optical energy harvesters to realize battery‐free ...
Jonggeun Park   +3 more
wiley   +1 more source

Atomic‐Scale Mechanisms of Anisotropic Thermal Decomposition in GeSn Alloys With Stepwise Pinning

open access: yesAdvanced Electronic Materials, EarlyView.
Combining in situ TEM with DFT calculations, this work elucidates atomic‐scale anisotropic thermal decomposition in GeSn films, identifying laminar receding in defect‐free regions and stepwise pinning at stacking faults. Driven by crystallographic anisotropy and defect‐mediated energetic penalties, these findings establish a physical framework for ...
YiXin Wang   +6 more
wiley   +1 more source

Strategies and Challenges in Memristor‐Selector Integration for High‐Density Memory and Compute‐in‐Memory Systems

open access: yesAdvanced Electronic Materials, EarlyView.
Selector integration enables scalable memristor crossbar arrays by suppressing sneak‐path currents and improving array selectivity. This review summarizes integration strategies, device requirements, challenges, and opportunities for high‐density memory, compute‐in‐memory, and neuromorphic computing systems.
Zohreh Hajiabadi   +3 more
wiley   +1 more source

Smart Exploration of Perovskite Photovoltaics: From AI Driven Discovery to Autonomous Laboratories

open access: yesAdvanced Energy Materials, EarlyView.
In this review, we summarize the fundamentals of AI in automated materials science, and review AI applications in perovskite solar cells. Then, we sum up recent progress in AI‐guided manufacturing optimization, and highlight AI‐driven high‐throughput and autonomous laboratories.
Wenning Chen   +4 more
wiley   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

open access: yesAdvanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park   +19 more
wiley   +1 more source

Home - About - Disclaimer - Privacy