Unraveling Water-Based Lubrication with Carbon Dots of Asphaltene Origin
Bibliographic record
Abstract
Despite the lower toxicity of water-based lubricants over nonrenewable petroleum-based analogues, they face challenges in achieving widespread adoption due to low stability and inadequate friction-reduction performance. To address this, a cost-effective nanoadditive is synthesized by expansive oxidation of asphaltenes to create biocompatible asphaltene-derived carbon dots [(ACDs); 5 nm]. These ACDs exhibit excellent water redispersibility, promoting long-term friction reduction and marking the first use of an asphaltene-based system for friction reduction in water or oil. Even at low loadings (0.2-4.0 wt %), ACDs significantly reduce friction on steel surfaces (>54%) with tribofilm stability surpassing pristine carbon dots, typical carbon-based graphene quantum dots, and inorganic nanomaterials (commercial 5 and 20 nm silica). The ACDs' attributes include high negative zeta potential, considerable water uptake, varied functional groups, biocompatibility, and a nanodisc shape conducive to stable tribofilm formation through effective particle stacking. The scalable synthesis, high yield, and impressive water redispersibility of ACDs position them favorably for commercial water-based lubrication.
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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".