Degradation of Dicarboxylic Acids (C<sub>2</sub>−C<sub>9</sub>) upon Liquid-Phase Reactions with O<sub>3</sub> and Its Atmospheric Implications
Bibliographic record
Abstract
Aerosols are considered major players in climate change and represent health hazards. Dicarboxylic acids are among a major class of components that form secondary organic atmospheric aerosols. To understand the atmospheric transformation of these compounds, kinetic studies on the ozonolysis and the photoinduced ozonolysis (lambda > or = 250 nm) of aqueous solutions of seven (C2-C9) dicarboxylic acids, which have been identified in atmospheric aerosols, were performed using Fourier transform infrared and ultraviolet-visible spectroscopy. The measured apparent rate constants for dicarboxylic acids in 0.1 mol L(-1) aqueous solutions at 298 +/- 2 K are as follows: oxalic, (2.7 +/- 0.1) x 10(-2); malonic, (5.5 +/- 0.1); succinic, (6.7 +/- 0.4) x 10(-4); glutaric, (1.3 +/- 0.2) x 10(-3); adipic, (1.7 +/- 0.1) x 10(-3); pimelic, (4.4 +/- 0.1) x 10(-3); and pinic, (2.5 +/- 0.1) x 10(-2) (L mol(-1) s(-1)). An empirical equation is provided to estimate the ozonolysis rate constant of dicarboxylic acids containing more than three carbon atoms for which no experimental data exists. A mechanism for malonic acid ozonolysis, which explains its fast ozonolysis rate constant, is also suggested. The implications of our results to atmospheric chemistry indicate that ozonolysis and photoinduced ozonolysis are not significant removal pathways for dicarboxylic acids.
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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) | 0.001 | 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 | 0.000 | 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".