Unveiling Phase-Dependent Genotoxicity of Organic Pollutants in Gaseous and Aqueous Forms
Bibliographic record
Abstract
Organic pollutants exist in various physical states within the natural environment, yet it remains unclear how their physical states influence their toxicity characteristics. This study investigated the phase-dependent genotoxicity and combined effects of two organic compounds, tert -butyl hydroperoxide (TBHP) and dimethyl sulfate (DES), in both gaseous and aqueous phases. Given the substantial differences in concentrations for the same compound in gaseous and aqueous environments, we constructed the complete multitoxic and dose–response curves for gene induction in both phases, covering environmentally relevant concentrations. Under the same stress conditions, the genotoxicity of gaseous TBHP was 158.21 ± 33.17% of that of its aqueous form, while gaseous DES exhibited 260.56 ± 12.63% of the genotoxicity of its aqueous form. Notably, while no formation of new reaction products were observed, aqueous-phase mixtures exhibited greater complexity and higher toxicity compared to their gaseous counterparts. These differences were attributed to variations in molecular energy states, free radical generation and diffusion, molecular interaction pathways, and chemical reactivity between the two phases. By elucidating the mechanisms underlying these disparities, this study highlights the critical role of physical states in evaluating the toxicity and risks associated with gaseous organic chemicals.
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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".