Results 191 to 200 of about 4,422,154 (275)

When Poor Exciton Dissociation Limits Photocurrents in Organic Solar Cells: Why Low Offset Non‐Fullerene Acceptor Blends Can't Be Efficient

open access: yesAdvanced Materials, EarlyView.
The energetic offset between the donor and the acceptor components in organic photoactive layers is central to the tradeoff between photovoltage and photocurrent losses. This Perspective covers the most important issues surrounding this topic in non‐fullerene acceptor blends, from the difficulty of accurately determining state energies and driving ...
Dieter Neher, Manasi Pranav
wiley   +1 more source

High‐Performance Transparent, Deformable, and Recoverable Biomimetic Stevia–PVA Hydrogel Triboelectric Nanogenerator with Machine Learning‐Assisted Motion Recognition

open access: yesAdvanced Materials, EarlyView.
A transparent, deformable stevia–PVA hydrogel triboelectric nanogenerator delivers significantly enhanced mechanical strength and electrical output through biomimetic hydrogen‐bonded networks. Coupled with machine learning–assisted signal recognition, the self‐powered hydrogel enables accurate human‐motion sensing for intelligent wearable and IoT ...
Thien Trung Luu   +5 more
wiley   +1 more source

Plasma‐Sequence‐Engineered Atomic Layer Deposition of Ultra‐Thin SiNx for Enhanced Etching Resistance in Extreme Ultraviolet Pellicles

open access: yesAdvanced Materials Interfaces, EarlyView.
Plasma‐sequence‐engineered ALD (PSE‐ALD), based on sequential NH3 and N2 plasma pulses, enables ultrathin, dense SiNx films. ToF‐MS analysis confirms ligand removal via HCl evolution, while increased film density indicates network densification. The resulting SiNx coating provides robust protection of graphite under H2 plasma exposure.
Hye‐Young Kim   +7 more
wiley   +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

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