Filterable Redox Cycling Activity: A Comparison between Diesel Exhaust Particles and Secondary Organic Aerosol Constituents
Bibliographic record
Abstract
The redox activity of diesel exhaust particles (DEP) collected from a light-duty diesel passenger car engine was examined using the dithiothreitol (DTT) assay. DEP was highly redox-active, causing DTT to decay at a rate of 23-61 pmol min(-1) μg(-1) of particle used in the assay, which was an order of magnitude higher than ambient coarse and fine particulate matter (PM) collected from downtown Toronto. Only 2-11% of the redox activity was in the water-soluble portion, while the remainder occurred at the black carbon surface. This is in contrast to redox-active secondary organic aerosol constituents, in which upward of 90% of the activity occurs in the water-soluble fraction. The redox activity of DEP is not extractable by moderately polar (methanol) and nonpolar (dichloromethane) organic solvents, and is hypothesized to arise from redox-active moieties contiguous with the black carbon portion of the particles. These measurements illustrate that "Filterable Redox Cycling Activity" may therefore be useful to distinguish black carbon-based oxidative capacity from water-soluble organic-based activity. The difference in chemical environment leading to redox activity highlights the need to further examine the relationship between activity in the DTT assay and toxicology measurements across particles of different origins and composition.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".