Photocatalytic chloride to chlorine conversion by ionic iron in aqueous aerosols: A combined experimental, quantum chemical and chemical equilibrium model study
Bibliographic record
Abstract
Aerosol chamber experiments show that the ligand-to-metal charge transfer absorption in iron(III) chlorides can lead to the production of chlorine. Based on this mechanism, the photocatalytic oxidation of chloride in mineral dust-sea spray aerosols was recently shown to be the largest source of chlorine over the North Atlantic. However, there has not been a detailed analysis of the mechanism including the aqueous formation equilibria and the absorption spectra of the iron chlorides; neither has there been an analysis of which iron chloride is the main chromophore. Here we present the results of experiments of photolysis FeCl_3 · 6H_2O in specific wavelength bands, an analysis of the absorption spectra of the title compounds from n=1..4 made using density functional theory, and the results of an aqueous phase model that predicts the abundance of the iron chlorides with changes in pH and ion concentrations. Transition state analysis is used to determine the energy thresholds of the dissociations of the species. Based on a speciation model with conditions extending from dilute water droplet to acidic seawater droplet to brine to salty crust, and the absorption rates and dissociation thresholds, we find that FeCl_2^+ is the most important species for chlorine production for a wide range of conditions. The mechanism was found to be active in the range of 400 to 530 nm with a maximum around 440 nm. We conclude that iron chlorides will form in atmospheric aerosols from the combination of iron(III) cations with chloride and that they will be activated by sunlight, generating chlorine (Cl_2/Cl) from chloride (Cl-), in a process that is catalytic in both chlorine and iron.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".