Effect of Oxalate and Sulfate on Iron-Catalyzed Secondary Brown Carbon Formation
Bibliographic record
Abstract
Oxalate and sulfate are ubiquitous components of ambient aerosols with a high complexation affinity to iron. However, their effect on iron-driven secondary brown carbon formation in solution from soluble aromatic and aliphatic reagents was not studied. We report masses and hydrodynamic particle sizes of insoluble particles formed from the dark aqueous phase reaction of catechol, guaiacol, fumaric, and muconic acids with Fe(III) in the presence of oxalate or sulfate. Results show that oxalate decreases particle yield in solution from the reaction of Fe(III), with a stronger effect for guaiacol than catechol. For both compounds, the addition of sulfate results in the formation of more polydisperse (0.1-5 μm) and heavier particles than those from control experiments. Reactions with fumaric and muconic acids show that oxalate (not sulfate) and pH are determining factors in the efficiency of particle formation in solution. Polymerization reactions occur readily in the presence of sulfate in solution producing particles with iron-coordinated and/or pore-trapped sulfate anions. The addition of oxalate to the reactions of Fe(III) with all organics, except guaiacol, produced fewer and larger polymeric particles (>0.5 μm). These results imply that even in the presence of competing ligands, the formation of insoluble and colored particles from soluble organic precursors still dominates over the formation of soluble iron complexes.
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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".