Results 201 to 210 of about 25,940 (295)

Exponential stability of non-autonomous stochastic partial differential equations with finite memory

open access: yes
The exponential stability, in both mean square and almost sure senses, for energy solutions to a nonlinear and non-autonomous stochastic partial differential equations with finite memory is investigated. Various criteria for stability are obtained.
Wan, Li, Duan, Jinqiao
core  

A New Customizable Surfactant LLPS Strategy for Sustainable and Highly Efficient Radioactive Metal Ion Separation

open access: yesAdvanced Science, EarlyView.
A diluent‐free, customizable liquid–liquid phase separation (LLPS) platform enables highly efficient radioactive metal ion extraction and stripping. By encapsulating hydrophobic extractants into an ionic surfactant‐rich condensed phase, this LLPS system leverages synergistic electrostatic and coordination interactions.
Ruihan Yan   +7 more
wiley   +1 more source

Sticky Yet Slippery: Molecular Ordering Reconciles Bubble‐Surface Affinity With Ultralow Friction at the Nanoscale

open access: yesAdvanced Science, EarlyView.
By engineering the molecular order and thickness of PDMS layers, we reconcile the stickiness and slipperiness during bubble transport. AFM measurements and MD simulations further reveal how these nanoscale architectures tune hydrophobic interaction FHB and friction force f.
Shishuang Zhang   +7 more
wiley   +1 more source

Physics‐Informed Machine Learning for Sustainable Alloy Design: Toward a Recyclable Unified Q&P Steel

open access: yesAdvanced Science, EarlyView.
A physics‐informed property‐bridging framework links high‐throughput hardness screening to tensile performance in quenching and partitioning steels. By transferring metallurgically guided representations across properties, a single alloy composition is designed to achieve multiple strength grades through heat‐treatment tuning alone, offering a ...
Xiaolu Wei   +7 more
wiley   +1 more source

STAID: A Self‐Refining Deep Learning Framework for Spatial Cell‐Type Deconvolution with Biologically Informed Modeling

open access: yesAdvanced Science, EarlyView.
STAID is a unified deep learning framework that couples iterative pseudo‐spot refinement with neural network training through a feedback loop and exploits gene co‐expression information to model higher‐order interactions, achieving accurate and robust cell‐type deconvolution in spatial transcriptomics.
Jixin Liu   +5 more
wiley   +1 more source

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