Results 191 to 200 of about 433,421 (301)

Cryptographic transistor for true random number generator with low power consumption. [PDF]

open access: yesSci Adv
Kim SI   +7 more
europepmc   +1 more source

Influence of Surface Finish on the Tribological Performance of AlTiBN Coatings Deposited on Forming Tools

open access: yesAdvanced Engineering Materials, EarlyView.
Aluminum and nitride coatings are used in industry because they are hard, resist wear, and protect against oxidation. Adding boron can improve friction behavior and other properties. This study tests coatings on surfaces with different finishes. Results show smoother surfaces perform better, while rough ones wear faster, although coatings can reduce ...
Adrián Claver   +9 more
wiley   +1 more source

Enhancing Bubble Removal in Geometry‐Optimized Electrodes

open access: yesAdvanced Engineering Materials, EarlyView.
3D‐printed lattice electrodes outperform stochastic foams in alkaline water electrolysis despite 20%–25% lower surface area. Straight flow channels generate Venturi‐like bubble entrainment, suppressing gas accumulation that renders foam interiors electrochemically inactive.
Florian Wiesner   +5 more
wiley   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

open access: yesAdvanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran   +6 more
wiley   +1 more source

Fast-speed and low-power-consumption optical phased array based on lithium niobate waveguides. [PDF]

open access: yesNanophotonics
Wang Z   +11 more
europepmc   +1 more source

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

open access: yesAdvanced 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   +4 more
wiley   +1 more source

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