Microtribology of Aqueous Carbon Nanotube Dispersions
Bibliographic record
Abstract
Abstract The tribological behavior of carbon nanotubes (CNTs) in aqueous humic acid (HA) solutions was studied using a surface forces apparatus (SFA) and shows promising lubricant additive properties. Adding CNTs to the solution changes the friction forces between two mica surfaces from “adhesion controlled” to “load controlled” friction. The coefficient of friction with either single‐walled (SW) or multi‐walled (MW) CNT dispersions is in the range 0.30–0.55 and is independent of the load and sliding velocity. More importantly, lateral sliding promotes a redistribution or accumulation, rather than squeezing out, of nanotubes between the surfaces. This accumulation reduced the adhesion between the surfaces (which generally causes wear/damage of the surfaces), and no wear or damage was observed during continuous shearing experiments that lasted several hours even under high loads (pressures ∼10 MPa). The frictional properties can be understood in terms of the Cobblestone Model where the friction force is related to the fraction of the adhesion energy dissipated during impacts of the nanoparticles. We also develop a simple generic model based on the van der Waals interactions between particles and surfaces to determine the relation between the dimensions of nanoparticles and their tribological properties when used as additives in oil‐ or water‐based lubricants.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".