DRIFTS Studies on the Role of Surface Water in Stabilizing Catechol–Iron(III) Complexes at the Gas/Solid Interface
Bibliographic record
Abstract
Surface water plays a crucial role in facilitating or inhibiting surface reactions in atmospheric aerosols. However, little is known about the role of surface water in the complexation of organic acid molecules to transition metals in multicomponent aerosol systems. We report herein results from real time DRIFTS experiments that show in situ complexation of catechol to Fe(III) under humid conditions. Catechol was schosen as a simple model for humic-like substances (HULIS) in aerosols and aged polyaromatic hydrocarbons (PAH). It was also detected in secondary organic aerosols (SOA) formed from the reaction of hydroxyl radicals with benzene. Given the importance of the iron content in aerosols and its biogeochemistry, our studies were conducted using FeCl3. For comparison, these surface-sensitive studies were complemented with bulk aqueous ATR-FTIR, UV-vis, and HPLC measurements for structural, quantitative, and qualitative information about complexes in the bulk, and potential degradation products in the dark. Under dry conditions, DRIFTS spectra show that gas phase catechol adsorbs molecularly and is fully protonated on samples containing FeCl3 with no evidence of complexation to Fe(III). Upon increasing the relative humidity to a value below the deliquescence of FeCl3, surface water facilitates ionic mobility resulting in the formation of monodentate catechol-Fe complexes. These complexes are stable at the gas/solid interface and do not undergo any further degradation in the dark as shown from bulk UV-vis and HPLC experiments. The implications of our studies on understanding interfacial and condensed phase chemistry relevant to multicomponent aerosols, water thin films on buildings, and ocean surfaces containing transition metals are discussed.
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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".