Characterization and integration of a new oxidative flow reactor for use in in vitro and human exposure systems with diesel exhaust and other aerosol
Bibliographic record
Abstract
Freshly emitted air pollutants may not represent real-world exposure conditions in human studies, especially for communities exposed to aged air pollutants. This study presents the design and characterization of a new oxidative flow reactor (OFR), named the Fast-oxidation Box (FoxBox, volume of 1019 L). Our aim is to simulate atmospheric aging of diesel exhaust (DE) with this system for cellular (in vitro) and controlled human studies, a unique capability globally. We measured: (a) residence time distribution (RTD) for DE-derived CO2, SO2, and particles, (b) DE particle transmission efficiency, (c) low-volatility organic compounds (LVOC) losses, and (d) particle size distribution, secondary organic aerosol (SOA) formation, and aerosol mass spectra of DE during photochemical oxidation (from OH exposure of (1.9 to 9.5)×1011 molec cm-3 s). Our results demonstrate turbulent flow-like conditions in FoxBox with narrower RTD for particles than gases. The particle transmission efficiency was nearly 100% for mobility diameters between 15 and 615 nm. LVOC losses to FoxBox walls were minimal. The changes in particle size distributions (e.g., formation of ultrafine particles) and chemical composition (e.g., SOA formation, increased O:C, etc.) during photochemical oxidation in FoxBox were like those observed in the atmosphere and other OFRs. Our preliminary study on cell viability found that photochemical oxidation significantly reduced cell viability, supporting observations that communities distant from air pollution sources are affected and vulnerable. The controlled human exposures with more realistic aerosol characteristics, such as those produced by FoxBox, may provide critical insight in this regard that has been lacking to date.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".