Results 131 to 140 of about 89,236 (296)
PREdicting LNP In Vivo Efficacy (PRELIVE) framework enables the prediction of lipid nanoparticle (LNPs) organ‐specific delivery through dual modeling approaches. Composition‐based models using formulation parameters and protein corona‐based models using biological fingerprints both achieve high predictive accuracy across multiple organs.
Belal I. Hanafy +3 more
wiley +1 more source
A One-Dimensional (1D) Computational Fluid Dynamics Study of Fontan-Associated Liver Disease (FALD). [PDF]
Li Y +4 more
europepmc +1 more source
Permanent magnets derive their extraordinary strength from deep, universal electronic‐structure principles that control magnetization, anisotropy, and intrinsic performance. This work uncovers those governing rules, examines modern modeling and AI‐driven discovery methods, identifies critical bottlenecks, and reveals electronic fingerprints shared ...
Prashant Singh
wiley +1 more source
Using Patient-Based Computational Fluid Dynamics for Abdominal Aortic Aneurysm Assessment. [PDF]
Kaewchoothong N +3 more
europepmc +1 more source
Computational fluid dynamics analysis on relationship between portal vein thrombosis and wall shear stress after hepatectomy [PDF]
Mamoru Takada +7 more
openalex +1 more source
Applying a high electric field to a doped organic semiconductor heats up the charge carrier distribution beyond the lattice temperature, enhancing conductivity. It is shown that the associated effective temperature can be used to extract the effective localization length, which is a characteristic length scale of charge transport and provides ...
Morteza Shokrani +4 more
wiley +1 more source

