Results 191 to 200 of about 1,104,809 (345)

Grain Size

, 2016
openaire   +2 more sources

Unexpected Enhancement of High-Cycle Fatigue Property in Hot-Rolled DP600 Steel via Grain Size Tailoring. [PDF]

Materials (Basel)
Song Y, Zhang C, Chen YA, Yang M, Zhang C, Lu B, Huang Y, Lu J, Wang S.   +8 more
europepmc   +1 more source

THE INVESTIGATION OF THE EFFECT OF GRAIN SIZE ON RAMAN AND 29Si MAS NMR SPECTRA OF BENTONITES FROM TURKEY

, 2016
Özgür Alver
openalex   +2 more sources

Evolution of bed load grain size distribution with increasing flow strength and the effect of flow duration on the caliber of bed load sediment yield in ephemeral gravel bed rivers [PDF]

, 2001
D. Mark Powell, Ian Reid, Jonathan B. Laronne   +2 more
openalex   +1 more source

Universal In Situ Isotope Exchange Raman Spectroscopy (IERS) Methodology for Measuring Oxygen Surface Exchange Dynamics Using a Probe Layer

Advanced Functional Materials, EarlyView.
A bespoke multilayer thin film configuration has been designed, which overcomes the material dependency of conventional isotope exchange Raman spectroscopy (IERS). This universal IERS methodology is efficient, non‐destructive and provides additional structural information and time resolution, which can be further extended to various isotopic elements ...
Zonghao Shen, Francesco Chiabrera, Alexander Stangl, Laetitia Rapenne, Federico Baiutti, Carmen Jiménez, Albert Tarancón, Mónica Burriel   +7 more
wiley   +1 more source

Sediment grain size and bedform morphology and dynamics shape benthic community heterogeneity in submarine dune fields. [PDF]

Sci Rep
Chauvel N, Dufresne T, Le Bot S, Weill P, Raoux A, Pezy JP.   +5 more
europepmc   +1 more source

Effect of Grain Size on Residual Loss of Mn-Zn Ferrites

, 1998
K. Takadate, Yutaka Yamamoto, A. Makino
openalex   +2 more sources

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, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Grain Size and Electrochemical Surface Modification Effects on Corrosion, Biological, and Technological Properties of CP Titanium Implants. [PDF]

J Funct Biomater
Hlinka J, Cvejn D, Dluhos L, Babuska V, Cabanova K, Dvorakova J, Volodarskaja A, Valiev RZ, Faisal NH, Peterek Dedkova K, Palupcikova R, Vodarek V.   +11 more
europepmc   +1 more source

Complex Cryptographic and User‐Centric Physically Unclonable Functions Enabled by Strain‐Sensitive Nanocrystals via Selective Ligand Exchange

Advanced Functional Materials, EarlyView.
This study investigates electromechanical PUFs that improve on traditional electric PUFs. The electron transport materials are coated randomly through selective ligand exchange. It produces multiple keys and a key with motion dependent on percolation and strain, and approaches almost ideal inter‐ and intra‐hamming distances.
Seungshin Lim, Hanseok Seo, Taesung Park, Byung Ku Jung, Junhyeok Park, Young Kyun Choi, Dae Yang Oh, Soong Ju Oh   +7 more
wiley   +1 more source