arXiv (All)
open access
Development and application of a machine learning potential for diamond surfaces and their sliding interfaces with silicon nitride and silica: effect of passivation, orientation and load
Friction and wear of diamond are controlled by its surface chemistry, which determines whether covalent bonds form across the sliding interface. First-principles simulations have explored the role of surface orientation and passivation, but are limited to a few hundred atoms and tens of picoseconds.
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Aging and configurational memories at rough frictional interfaces#1157249
- Diamond Formation at Superlubric Sliding Interface.#396005
- Persistence of touch deposits on a range of substrates assessed using an optimised application of Diamond Dye™.#483465
- Switching Friction via Sliding Ferroelectricity#398590
- Rolling Contact Fatigue Life Prediction of Traction Drives Based on Observation of Surface Crack Growth#69791
Record · ID 1490007
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive