Surface Water Enhances the Uptake and Photoreactivity of Gaseous Catechol on Solid Iron(III) Chloride
Bibliographic record
Abstract
Uptake and photoreactivity of catechol-Fe complexes are investigated at the gas/solid interface under humid and dry conditions, along with the nature of the hydrogen-bonding network of adsorbed water. Catechol was chosen as a simple model for organics in aerosols. Iron chloride was used to distinguish ionic mobility from binding to coordinated iron(III) in hematite. Studies were conducted using diffuse reflectance infrared Fourier transform spectroscopy as a function of irradiation time. Results show that adsorbed water at 30% relative humidity (RH), not light, increases the concentration of adsorbed catechol by a factor of 3 over 60 min relative to dry conditions. Also, our data show that, at 30% RH and under light and dark conditions, growth factors describing the concentration of adsorbed catechol are very similar suggesting that light does not significantly enhance the uptake of catechol vapor on FeCl3. Surface water also enhances the initial photodecay kinetics of catechol-Fe complexes at 30% RH by a factor of 10 relative to control experiments (RH < 1%, or no FeCl3 under humid conditions). Absorptions assigned to carbonyl groups were not observed with irradiation time, which was explained by the dominance of FeCl(2+) species relative to FeOH(2+) in the highly acidic "quasi-liquid" phase at 30% RH. Clear differences in the hydrogen-bonding network upon gaseous catechol uptake are observed in the dark and light and during the photodecay of adsorbed catechol. The implications of these results on our understanding of interfacial processes in aged iron-containing surfaces 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".