Enhancement of Diarylamine Antioxidant Activity by Molybdenum Dithiocarbamates
Bibliographic record
Abstract
Molybdenum dithiocarbamates (MDTCs) are indispensable lubricant additives. Although their role as antiwear agents is well established, they have also been attributed antioxidant properties that are not understood. MDTCs do not inhibit autoxidation, but they markedly enhance the capacity of diphenylamines (DPAs)─ubiquitous radical-trapping antioxidants (RTAs)─to do so. We find this synergy to be evident not only at elevated temperatures (160 °C in n -hexadecane) but also at moderate temperatures, where autoxidations can be continuously monitored and kinetics more easily interpreted (100 °C in squalane). Interestingly, the synergy disappeared in an unsaturated hydrocarbon ( n -hexadec-1-ene), where the RTA activity of the DPA is known to result from the diarylnitroxide derived therefrom. Autoxidations of squalane carried out in the presence of the diarylnitroxide─wherein it is a poor inhibitor─were much better inhibited in the presence of MDTC, suggesting that it converts the nitroxide to (a) more competent RTA(s). Indeed, preparative experiments revealed two species: DPA and a DPA dimer into which a single oxygen atom had been incorporated. This conversion is accelerated by the oxidation of MDTC to a dioxo molybdenum species. A mechanism is proposed to account for these observations, and the implications of our findings and their interpretation are discussed.
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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".