Results 251 to 260 of about 128,560 (335)
Effect of Pass Strain on Grain Refinement in 7475 Al Alloy during Hot Multidirectional Forging
, 2004 Oleg Sitdikov +4 more
openalex +2 more sources
Hydroxylated Ionic Liquids as Functional Additives for Stable Aqueous Zn Batteries
Advanced Functional Materials, EarlyView.A hydroxyl‐functionalized ionic liquid additive (HO‐EMImTfO) regulates Zn2+ solvation and electrodeposition by forming a stable ion‐diversion dam at the Zn interface. This design mitigates Zn pulverization, suppresses dendrite growth, and reduces side reactions, enabling Zn||Cu and Zn||V2O5 cells to achieve exceptional cycling stability and efficiency.
Qiang Yan +6 more
wiley +1 more source
Advanced Functional Materials, EarlyView.An ultra‐robust memristor based on SrTiO3‐CeO2 (S‐C) vertically aligned nanocomposite (VAN) achieving exceptional endurance of 1012 switching cycles via interface engineering. Artificial neural networks (ANNs) integrated with S‐C VAN memristors exhibit high training accuracy across multiple datasets.
Zedong Hu +12 more
wiley +1 more source
Uptake of iron and its effect on grain refinement of pure magnesium by zirconium
, 2004 Peng Cao +3 more
openalex +2 more sources
Effect of Grain Refinement and Dispersion of Particles and Reinforcements on Mechanical Properties of Metals and Metal Matrix Composites through High-Ratio Differential Speed Rolling. [PDF]
Materials (Basel), 2020 Bahmani A, Kim WJ.
europepmc +1 more source
Advanced Functional Materials, EarlyView.Phase‐field modeling reveals the mechanisms behind short‐term ferroelectric imprint in Hf0.5Zr0.5O2 polycrystalline thin films. Combined with a charge trapping model, the proposed framework accurately reproduces coercive field shifts with pause time and their recovery through field cycling in polarization‐voltage measurements, offering valuable ...
Kévin Alhada‐Lahbabi +10 more
wiley +1 more source
Unleashing the Power of Machine Learning in Nanomedicine Formulation Development
Advanced Functional Materials, EarlyView.A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore +7 more
wiley +1 more source