Reactivity of aminophenols in forming nitrogen-containing brown carbon from iron-catalyzed reactions
Bibliographic record
Abstract
Nitrogen-containing organic carbon (NOC) in atmospheric particles is an important class of brown carbon (BrC). Redox active NOC like aminophenols received little attention in their ability to form BrC. Here we show that iron can catalyze dark oxidative oligomerization of o- and p-aminophenols under simulated aerosol and cloud conditions (pH 1-7, and ionic strength 0.01-1 M). Homogeneous aqueous phase reactions were conducted using soluble Fe(III), where particle growth/agglomeration were monitored using dynamic light scattering. Mass yield experiments of insoluble soot-like dark brown to black particles were as high as 40%. Hygroscopicity growth factors (κ) of these insoluble products under sub- and super-saturated conditions ranged from 0.4-0.6, higher than that of levoglucosan, a prominent proxy for biomass burning organic aerosol (BBOA). Soluble products analyzed using chromatography and mass spectrometry revealed the formation of ring coupling products of o- and p-aminophenols and their primary oxidation products. Heterogeneous reactions of aminophenol were also conducted using Arizona Test Dust (AZTD) under simulated aging conditions, and showed clear changes to optical properties, morphology, mixing state, and chemical composition. These results highlight the important role of iron redox chemistry in BrC formation under atmospherically relevant conditions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".