Results 171 to 180 of about 508,796 (300)

Thermochemical Micro‐Explosion for Prompt Thrombolysis via Proximal Injection of Liquid Alkali Metal

open access: yesAdvanced Science, EarlyView.
We report a micro‐explosive thermochemical thrombolysis (METCT) therapy via injectable liquid alkali metal encapsulated in dimethyl silicone (LAM@oil). METCT enables prompt and safe vascular recanalization within 90 s. Critically, the LAM@oil system demonstrates significantly higher thrombolytic efficacy compared to clinically available thrombolytic ...
Xin Liao   +7 more
wiley   +1 more source

Bending‐Resistant Intimate 3D Graphene–Metal Heterojunctions for Highly Sensitive and Robust Flexible Sensors

open access: yesAdvanced Science, EarlyView.
A reservoir‐guided silver‐nanoparticle soldering strategy is developed for bending‐resistant 3D graphene–metal heterojunctions. Controlled hot‐plate evaporation confines conductive ink within the metal reservoir, suppressing spreading and protecting the LIG interface from deformation‐induced fracture.
Saeyoung Park   +5 more
wiley   +1 more source

Beech latewood density as a proxy for temperature reconstruction. [PDF]

open access: yesSci Adv
Verschuren L   +15 more
europepmc   +1 more source

Intra-seasonal development of radial increment of Picea abies in Latvia

open access: yesResearch for rural development, 2017
Jēkabs Dzenis   +3 more
openaire   +1 more source

Machine Learning‐Assisted KCl‐CaCl2‐LiCl Electrolyte Design for Low‐Temperature, High‐Performance Calcium‐Based Liquid Metal Batteries

open access: yesAdvanced Science, EarlyView.
A machine learning‐assisted framework optimizes the KCl‐CaCl2‐LiCl ternary electrolyte. The optimized 13:35:52 mol% composition enables Ca‐based liquid metal batteries to operate stably at 480 °C, with >99.5% coulombic efficiency, ultralow self‐discharge, and excellent cycling stability, advancing low‐temperature large‐scale energy storage.
Xinglin Zhou   +3 more
wiley   +1 more source

Polarization Dynamics in Ferroelectrics: Insights Enabled by Machine Learning Molecular Dynamics

open access: yesAdvanced Science, EarlyView.
Machine learning molecular dynamics is presented as a route to capture polarization switching, domain wall kinetics, topological polar textures, and polar mechanical coupling beyond the limits of conventional atomistic methods. This Perspective surveys recent progress and identifies key methodological directions, including long‐range electrostatics ...
Dongyu Bai   +3 more
wiley   +1 more source

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