Results 201 to 210 of about 6,060,553 (281)

Impact of Rapid Thermal Annealing on the Structural and Piezoelectric Properties of AlN Thin Films

open access: yesAdvanced Electronic Materials, EarlyView.
Aluminum nitride (AlN) is a biocompatible piezoelectric material suitable for energy harvesting in MEMS resonators. Through rapid thermal annealing, a threshold for CMOS‐compatible processes is established. AlN improved its crystallinity and enhanced the piezoelectric coefficient by ∼160%. Devices show an improvement in the electromechanical properties
Laura Mazón‐Maldonado   +7 more
wiley   +1 more source

Large-scale energy budget of impulsive magnetic reconnection: Theory and simulation. [PDF]

open access: yesJ Geophys Res Space Phys, 2017
Kiehas SA   +5 more
europepmc   +1 more source

Decoupled Dielectric Optimization and YbOx Contact Engineering in All‐Spray‐Processed Oxide Thin‐Film Transistors

open access: yesAdvanced Electronic Materials, EarlyView.
Fully spray‐processed oxide thin‐film transistors are advanced by decoupling dielectric quality from contact energetics. Thermochemically densified MgO suppresses defect‐mediated leakage and improves electrostatic control, while an ultrathin YbOx interlayer induces a 1.67 eV work‐function shift that boosts mobility from ∼40 to ∼94 cm2 V−1 s−1.
Ahmed Mohamed   +5 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

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