Results 201 to 210 of about 146,303 (307)

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

Insights into Native Single-Atom Electrocatalyst Site Structures. [PDF]

ACS Nano
Zachman MJ, Hafiz H, O'Leary CM, Lim C, Chung DY, Li S, Park S, Kim J, Cullen DA, Holby EF, Stamenkovic VR.   +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

Universal logical operations in a silicon quantum processor. [PDF]

Nat Nanotechnol
Zhang C, Xu F, Zhang S, Duan M, Zhong D, Bai X, Wang H, Huang C, Deng Y, Gao M, Zhang YN, Liu J, Li C, Jiang Y, Zhao B, Shu H, Wu K, Shi K, Ding Q, Tian Z, Wang G, Yuan X, Xin T, Hu G, Liu S, Pan T, Huang P, He Y, Yu D.   +28 more
europepmc   +1 more source

Influence of Si Content and Milling Duration on the Microstructure and Mechanical–Tribological Properties of AlCoCrFeNiSi High‐Entropy Alloys

Advanced Engineering Materials, EarlyView.
Si‐doped AlCoCrFeNi high‐entropy alloys are synthesized by mechanical alloying to reveal the effect of Si content and milling time on phase evolution, microstructural refinement, and tribological behavior. A transition from FCC to BCC structure, significant grain refinement, and enhanced hardness and wear resistance are achieved, with the 4 at% Si ...
Mustafa Okumuş, Mehmet Emin Demir, Sercan Özpınar   +2 more
wiley   +1 more source

Formic Acid Stabilization on Supported Ionic Liquid Phases: Insights from Solid-State NMR Spectroscopy. [PDF]

Chemistry
Wu Y, Zhang Y, Leitner W, Bordet A, Wiegand T.   +4 more
europepmc   +1 more source

Microstructure, Thermal Transport, and Dry‐Sliding Tribology of Powder‐Metallurgy Al7075 Composites Reinforced With Sol–Gel‐Derived ZnO–rGO Hybrid Nanoparticles

Advanced Engineering Materials, EarlyView.
Sol–gel‐derived ZnO–rGO hybrid nanoparticles enable Al7075 powder‐metallurgy composites to achieve concurrent gains in hardness and thermal conductivity while markedly lowering friction and wear. The hybrid architecture couples ZnO‐based load support with rGO‐assisted lamellar sliding and heat spreading, revealing a promising route toward lightweight ...
Bunyamin Aksakal, Cevher Kursat Macit, Merve Ayık, Hakan Tutumlu   +3 more
wiley   +1 more source

Electrified Damage in Motion Systems

Advanced Engineering Materials, EarlyView.
The electrified damage in motion systems is a fundamental framework presenting the degradation pathway arising from the coupling of electrical energy transport with mechanical contact and interfacial chemistry. The framework positions electrified damage as a distinct degradation regime with unique characteristic surface morphologies and failures of ...
M. Humaun Kabir, Angelina Nguyen, Hong Liang   +2 more
wiley   +1 more source

NIS-Centered Reporter Gene Imaging and Radionuclide-Integrated Nanoplatforms for Quantitative Tracking of Immune Cell Therapy in Oncology and Inflammatory Disease Models. [PDF]

Pharmaceuticals (Basel)
Lee SB.
europepmc   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source