Interfacial Interactions and Tribological Behaviour of Metal-Oxide/2D-Material Contacts
Bibliographic record
Abstract
Abstract This work combines experimental atomic force microscopy (AFM) and DFT simulations to study oxidized metal (oxidized copper & titanium) and 2D material (graphene & MoS2) interfaces. Combining AFM and DFT allowed identifying the interfacial interaction and established a correlation between tribological behavior, interfacial charge distribution, and variations in the potential energy profile with sliding along the metal/2D-materials interfaces. The TiO2 (rutile) and CuO (cupric oxide) metal oxides were mostly found to chemisorb along the interface with the 2D-materials. Both the metal-oxide counter-surfaces (TiO2 and CuO) exhibited higher friction force and adhesion on graphene than on MoS2. The CuO surface was inferred to be copper rich based on comparison with DFT simulations. The interfacial electronic charge distribution and relative energy change were identified to strongly influence sliding and adhesive behavior between oxidized-metal/2D-material contacts when considering only electronic effects in the DFT simulations. More homogenous interfacial charge distribution/sharing and lower surface energy variation, as found on the MoS2 surfaces, were identified to lower friction and adhesion. Non-electronic effects not captured by simulations were found to likely dominate interfacial shear strength measurements experimentally. Therefore, MoS2 should be used in interfacial applications involving TiO2 and copper rich CuO surfaces requiring lower adhesion and friction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".