Results 121 to 130 of about 6,853 (246)

Ergonomic Sponge Electrodes From Recycled PEDOT:PSS

open access: yesAdvanced Electronic Materials, EarlyView.
ABSTRACT Emerging technologies in human–machine interfacing increasingly aim to develop solutions that naturally conform to the body's unique characteristics. Ergonomics and electrical performance in cutaneous sensing are crucial for accurate and reliable translation of biosignals.
Matías Ceballos   +3 more
wiley   +1 more source

Beyond Descriptor‐Based AI Design: Sp2‐Hybridized Branched Side Chains Enable Pre‐Aggregation–Driven Seeding Effects in Green‐Solvent‐Processed Organic Solar Cells

open access: yesAdvanced Energy Materials, EarlyView.
sp2‐hybridized branched side chains are introduced as a new molecular design for NFAs, YBOV, inducing strong solution‐state pre‐aggregation. This pre‐aggregation enables universal seeding motifs, highly ordered film growth, and overcoming the intrinsic current–voltage trade‐off, achieving 19.67% efficiency via green‐solvent processing beyond descriptor‐
Seokhwan Jeong   +14 more
wiley   +1 more source

Asymmetric Multi‐Site Ion Exchange in Porous Carbon Electrodes

open access: yesAdvanced Energy Materials, EarlyView.
A lognormal‐distribution 2D EXSY NMR framework uncovers the full spectrum of ion dynamics in hierarchical porous carbons, bridging fast surface exchange and confined in‐pore transport—establishing structure–transport relationships for next‐generation energy storage, gas storage, and ion removal materials.
Henry R. N. B. Enninful   +5 more
wiley   +1 more source

Verification and Mitigation of Proton‐Induced Non‐Ionizing Damage in Perovskite Solar Cells for Space Applications

open access: yesAdvanced Energy Materials, EarlyView.
Metal‐halide perovskite solar cells offer high‐efficiency power for orbital missions but suffer from permanent proton‐induced degradation. This work identifies non‐ionizing energy loss (NIEL) as the primary driver of irreversible failure, inducing atomic displacements and microcracks in the high‐fluence regime. Through energy‐tuned mapping and scalable
Jangwon Byun   +10 more
wiley   +1 more source

Deep Learning Prediction of Surface Roughness in Multi‐Stage Microneedle Fabrication: A Long Short‐Term Memory‐Recurrent Neural Network Approach

open access: yesAdvanced Intelligent Discovery, EarlyView.
A sequential deep learning framework is developed to model surface roughness progression in multi‐stage microneedle fabrication. Using real‐world experimental data from 3D printing, molding, and casting stages, an long short‐term memory‐based recurrent neural network captures the cumulative influence of geometric parameters and intermediate outputs ...
Abdollah Ahmadpour   +5 more
wiley   +1 more source

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