Efficient Formation of Light-Absorbing Polymeric Nanoparticles from the Reaction of Soluble Fe(III) with C4 and C6 Dicarboxylic Acids
Bibliographic record
Abstract
The role of transition metals in the formation and aging of secondary organic aerosol (SOA) from aliphatic and aromatic precursors in heterogeneous/multiphase reactions is not well understood. The reactivity of soluble Fe(III) toward known benzene photooxidation products that include fumaric (trans-butenedioic) and muconic (trans,trans-2,4-hexadienedioic) acids was investigated. Efficient formation of brightly colored nanoparticles was observed that are mostly rod- or irregular-shaped depending on the structure of the organic precursor. The particles were characterized for their optical properties, growth rate, elemental composition, iron content, and oxidation state. Results indicate that these particles have mass absorption coefficients on the same order as black carbon and larger than that of biomass burning aerosols. The particles are also amorphous in nature and consist of polymeric chains of Fe centers complexed to carboxylate groups. The oxidation state of Fe was found to be in between Fe(III) and Fe(II) in standard compounds. The organic reactant to iron molar ratio and pH were found to affect the particle growth rate. Control experiments using maleic acid (cis-butenedioic acid) and succinic acid (butanedioic acid) produced no particles. The formation of particles reported herein could account for new pathways that lead to SOA and brown carbon formation mediated by transition metals. In addition, the multiple chemically active components in these particles (iron, organics, and acidic groups) may have an effect on their chemical reactivity (enhanced uptake of trace gases, catalysis, and production of reactive oxygen species) and their likely poor cloud/ice nucleation properties.
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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) | 0.000 | 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".