Lessons From Drug Discovery for Cryoprotective Agent Design: An AI‐Oriented Perspective
Advanced Science, EarlyView.Cryoprotectant design is reframed through the lens of drug discovery as a multiparameter optimization problem. This perspective highlights how AI and systematic design strategies could enable safer, more effective cryoprotectants, while identifying key limitations that currently constrain predictive progress in cryobiology. ABSTRACT Cryopreservation is
Dominika Wilczok +4 more
wiley +1 more source
Crystallization of Water Mediated by Carbon
Geophysical Monograph Series, Page 77-86., 2020 This book is Open Access. A digital copy can be downloaded for free from Wiley Online Library.
Explores the behavior of carbon in minerals, melts, and fluids under extreme conditions
Carbon trapped in diamonds and carbonate-bearing rocks in subduction zones are examples of the continuing exchange of substantial carbon ...
Tianshu Li, Yuanfei Bi, Boxiao Cao
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Biodegradable Zn‐Based Implants: Progress, Challenges, and Pathways toward Clinical Translation
Advanced Science, EarlyView.Exploring biodegradable Zn‐based implants offers a promising pathway to next‐generation biomedical devices with balanced degradation and biocompatibility. A comprehensive overview of biodegradable Zn‐based implants, covering their biological significance, material design principles, and advanced engineering strategies is provided.
Panfeng Zhao +10 more
wiley +1 more source
Advanced Science, EarlyView.To decouple mechanical robustness and energy storage capacity in phase change materials (PCMs), a series of polyurethane‐based PCMs bearing crystallizable alkyl side chains are synthesized via a hard‐segment‐anchored/side‐chain‐storage molecular design.
Huizhou Luo +6 more
wiley +1 more source
Experimental Study of Ice Slurry Production Characteristics by Vacuum Stirring Method
Zhileng xuebao, 2015 The process of ice slurry preparation is observed with the ice slurry preparation equipment based on vacuum stirring method, and the effects of ethylene glycol additive, solution volume and stirring speed on the supercooling degree of ice slurry ...
Liu Xi +4 more
doaj
Dependence of the lamellar thickness of an extended-chain single crystal of polyethylene on the degree of supercooling and the pressure [PDF]
Journal of the Chemical Society, Faraday Transactions, 1995 The dependence of the lamellar thickness (I) of an extended-chain single crystal (ECSC) of polyethylene (PE) crystallized at various pressures below or at the triple-point pressure (Ptri= 0.5 GPa) on the degree of super-cooling (ΔT) and the pressure have been studied. The value of I increased with the decrease in ΔT[i.e. increase in the crystallization
Hikosaka, M +4 more
openaire +1 more source
Advanced Science, EarlyView.Adult stem cell therapy requires more than high in vitro potency. This review proposes a systems framework in which cell‐intrinsic programs, instructive microenvironmental cues, and pre‐/post‐delivery engineering are co‐designed under standardized translational rules.
Soo‐Rim Kim +2 more
wiley +1 more source
Advanced Energy Materials, EarlyView.Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park +19 more
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Fabrication of a novel nano phase change material emulsion with low supercooling and enhanced thermal conductivity [PDF]
, 2020 This is the author accepted manuscript.A novel nano phase change material emulsion (NPCE) with low supercooling and high thermal conductivity was prepared by sonication method.
Zhang, G +5 more
core +1 more source
Harnessing Machine Learning to Understand and Design Disordered Solids
Advanced Intelligent Discovery, EarlyView.This review maps the dynamic evolution of machine learning in disordered solids, from structural representations to generative modeling. It explores how deep learning and model explainability transform property prediction into profound physical insight.
Muchen Wang, Yue Fan
wiley +1 more source