Results 221 to 230 of about 29,335 (305)

Lithium‐Ion Battery Safety Evaluation by Quantifying Lithium Plating Degree Based on Pressure Signals

open access: yesAdvanced Science, EarlyView.
Low N/P cells enable controllable lithium plating, allowing the lithium plating degree (DLi) to be quantified and linked to thermal safety. Pressure signals during calibration provide a non‐destructive route to diagnose DLi, while thermal response tests define safety levels and management strategies. The framework remains applicable in commercial 12 Ah
Haonan Liu   +10 more
wiley   +1 more source

Programmed Interfacial Phase Separation for Spatiotemporal Fungicide Delivery via Tunable Micro/Nano Architectures

open access: yesAdvanced Science, EarlyView.
ABSTRACT Translating dynamic liquid‐liquid phase separation into robust solid‐state architectures remains a challenge in materials engineering. Here, we report a composition‐driven interfacial assembly strategy that couples supramolecular coacervation with metal‐phenolic coordination to engineer tunable fungicide carriers.
Rui Zhang   +7 more
wiley   +1 more source

Polymer Concepts in Cellular Function

open access: yes
Advanced Science, EarlyView.
Miao Yu   +7 more
wiley   +1 more source

Fusing Direct and Indirect Measurements Through Multi‐Fidelity Learning For Accelerated Electrocaloric Materials Discovery

open access: yesAdvanced Science, EarlyView.
A multi‐fidelity framework integrates sparse direct and abundant indirect electrocaloric measurements. Multi‐objective active learning accelerates BaTiO3‐based electrocaloric materials discovery at –70∘C$^{\circ }{\rm C}$. A diffuse transition enables an electrocaloric strength of 0.06×$\times$10−6 Km/V at –70℃ with an operational temperature span of ...
Bo Wang   +8 more
wiley   +1 more source

Learning Work Function via Implicit Reasoning on Electrostatic Potential Landscapes

open access: yesAdvanced Science, EarlyView.
StructPot‐CLR establishes a cross‐modal contrastive learning framework that aligns the crystal structures of 2D materials with plane‐averaged electrostatic potential landscapes for physically informed work‐function prediction. The model achieves an MAE of 0.265 eV and an R2 of 0.902 on the held‐out test set while accurately preserving key morphological
Haoyu Wan, Yue Wu, Tianhao Su, Deng Pan
wiley   +1 more source

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