Research on Machine Learning Models for Maize Hardness Prediction Based on Indentation Test [PDF]
Haipeng Lin +5 more
openalex +1 more source
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]
Qi X +10 more
europepmc +1 more source
Characterization of Indentation Impressions on Human Enamel for Hardness Measurement
Zhang Guangming +3 more
openalex +1 more source
Comment on ‘The concept of differential hardness in depth sensing indentation’
Jürgen Malzbender
openalex +1 more source
Dynamic hardness and formation of Portevin-Le Chatelier bands during impact indentation
А. А. Шибков +5 more
openalex +2 more sources
Copper Contact for Perovskite Solar Cells: Properties, Interfaces, and Scalable Integration
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
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]
A. Franco +4 more
openalex +1 more source
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

