Results 171 to 180 of about 96,343 (317)

From Composition to Ionic Conductivity: Machine Learning-Guided Discovery and Experimental Validation of Argyrodite-Type Lithium-Ion Electrolytes. [PDF]

Small
Kong S, Yu Z, Matsui N, Kamiya M, Iwamizu Y, Tanaka Y, Kubota N, Nomoto K, Hori S, Hirayama M, Suzuki K, Kanno R.   +11 more
europepmc   +1 more source

Order-disorder and ionic conductivity in calcium nitride-hydride. [PDF]

Nat Commun, 2023
Irvine GJ, Smith RI, Jones MO, Irvine JTS.   +3 more
europepmc   +1 more source

Designing Polymer Nanocomposites for X‐Ray Shielding: Mechanisms, Architectures, and Scalable Processing

Advanced Engineering Materials, EarlyView.
This review highlights advances in lightweight, lead‐free polymer nanocomposites for diagnostic X‐ray shielding. By linking filler chemistry, dispersion, architecture, and photon interaction mechanisms, it establishes structure–performance relationships guiding material design.
Aklilu G. Messele, Hossein Ipakchi, Tizazu H. Mekonnen   +2 more
wiley   +1 more source

Ionic Conductivity of Composite Polymer Electrolyte: Clarifying the Role of the Interface with Nonconductive Particles. [PDF]

ChemSusChem
Navallon G, Monaco F, Märker K, Cloetens P, Drnec J, Atkins D, Picard L, Lyonnard S.   +7 more
europepmc   +1 more source

A Dislocation Perspective on Strength and Toughness in Ceramics

Advanced Engineering Materials, EarlyView.
Dislocations in ceramics enjoy a long but yet under‐appreciated history. The three research waves for dislocations in ceramics highlight the topic evolution over the last 90 years. This review focuses on the impact of dislocation on strength and toughness in ceramics.
Xufei Fang
wiley   +1 more source

Disorder-Mediated Ionic Conductivity in Irreducible Solid Electrolytes. [PDF]

J Am Chem Soc
Landgraf V, Tu M, Zhao W, Lavrinenko AK, Cheng Z, Canals J, de Leeuw J, Ganapathy S, Vasileiadis A, Wagemaker M, Famprikis T.   +10 more
europepmc   +1 more source

Machine Learning‐Supported Analysis for Predicting and Visualizing Nonlinear Relationships Between Material Properties in Electroplated Chromium Layers

Advanced Engineering Materials, EarlyView.
This study applies machine learning regression to predict chromium layer thickness in decorative trivalent chromium electroplating, using 441 experiments from laboratory‐scale (1L) and pilot‐scale (14L) setups. Tree‐based models, particularly CatBoost, outperformed linear regression by capturing nonlinear parameter interactions (R2$R^2$ up to 0.77 ...
Christoph Baumer, Christopher Mai, Luca Eisentraut, Andreas Bund, Ricardo Buettner   +4 more
wiley   +1 more source

Temperature Evolution of Crystal Structure, Ferroelectricity and Ionic Conductivity of Ca₉La(VO₄)₇. [PDF]

Molecules
Baryshnikova OV, Lazoryak BI, Morozov VA, Stefanovich SY, Mosunov AV, Gallyamov EM, Aksenov SM, Deyneko DV.   +7 more
europepmc   +1 more source

Triple Junctions as Dislocation‐Like Defects: The Role of Grain Boundary Crystallography Revealed by Experiment and Atomistic Simulation

Advanced Engineering Materials, EarlyView.
Grain boundary triple junctions are an essential ingredient of the microstructure of polycrystalline materials. In this study, a triple junction is observed using atomic‐resolution scanning transmission electron microscopy and characterized. Computer simulations reveal that the junction has a dislocation character that is determined by the joining ...
Tobias Brink, Saba Saood, Peter Schweizer, Jörg Neugebauer, Gerhard Dehm   +4 more
wiley   +1 more source

Tunable band gap and ionic conductivity in PMMA and PANI blend with WO₃ nanocomposites for optoelectronics and energy storage devices. [PDF]

Sci Rep
Al-Sulami AI, Elamin NY, Aldosari E, Farea MO, Alzahrani SS, Al-Harthi AM, Alharbi EM, Rajeh A.   +7 more
europepmc   +1 more source