Results 151 to 160 of about 9,866 (290)

Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test [PDF]

open access: gold
Haipeng Lin   +5 more
openalex   +1 more source

Safety of Sodium‐Ion Batteries: Evaluation and Perspective from Component Materials to Cells, Modules, and Packs

open access: yesAdvanced Energy Materials, EarlyView.
This review provides a bottom‐up evaluation of sodium‐ion battery safety, linking material degradation mechanisms, cell engineering parameters, and module/pack assembly. It emphasizes that understanding intrinsic material stability and establishing coordinated engineering control across hierarchical levels are vital for preventing degradation coupling ...
Won‐Gwang Lim   +5 more
wiley   +1 more source

Role of Ni Layer Thickness in Regulating Mechanical Properties and Deformation-Fracture Behavior of TiB<sub>2</sub>-Ni Multilayer Films. [PDF]

open access: yesNanomaterials (Basel)
Qi X   +10 more
europepmc   +1 more source

Dynamic hardness and formation of Portevin-Le Chatelier bands during impact indentation

open access: diamond, 2023
А. А. Шибков   +5 more
openalex   +2 more sources

Copper Contact for Perovskite Solar Cells: Properties, Interfaces, and Scalable Integration

open access: yesAdvanced Energy and Sustainability Research, EarlyView.
Copper electrodes, as low‐cost, scalable contacts for perovskite solar cells, offer several advantages over precious metals such as Au and Ag, including performance, cost, deposition methods, and interfacial engineering. Copper (Cu) electrodes are increasingly considered practical, sustainable alternatives to noble‐metal contacts in perovskite solar ...
Shuwei Cao   +4 more
wiley   +1 more source

Traceability for indentation measurements in Brinell-Vickers-Knoop hardness

open access: gold
Cihan Kuzu   +19 more
openalex   +1 more source

The use of a vickers indenter in depth sensing indentation for measuring elastic modulus and vickers hardness [PDF]

open access: diamond, 2004
A. Franco   +4 more
openalex   +1 more source

A Physics Constrained Machine Learning Pipeline for Young's Modulus Prediction in Multimaterial Hyperelastic Cylinders Guided by Contact Mechanics

open access: yesAdvanced Intelligent Discovery, EarlyView.
A physics‐guided machine learning framework estimates Young's modulus in multilayered multimaterial hyperelastic cylinders using contact mechanics. A semiempirical stiffness law is embedded into a custom neural network, ensuring physically consistent predictions. Validation against experimental and numerical data on C.
Christoforos Rekatsinas   +4 more
wiley   +1 more source

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