Functional group analysis by H NMR/chemical derivatization for the characterization of organic aerosol from the SMOCC field campaign
Bibliographic record
Abstract
Abstract. Water soluble organic compounds (WSOC) in aerosol samples collected in the Amazon Basin in a period encompassing the middle/late dry season and the beginning of the wet season, were investigated by H NMR spectroscopy. HiVol filter samples (PM2.5 and PM>2.5) and size-segregated samples from multistage impactor were subjected to H NMR characterization. The H NMR methodology, recently developed for the analysis of organic aerosol samples, has been improved by exploiting chemical methylation of carboxylic groups with diazomethane, which allows the direct determination of the carboxylic acid content of WSOC. The content of carboxylic carbons for the different periods and sizes ranged from 12% to 20% of total measured carbon depending on the season and aerosol size, with higher contents for the fine particles in the transition and wet periods with respect to the dry period. A comprehensive picture is presented of WSOC functional groups in aerosol samples representative of the biomass burning period, as well as of transition and semi-clean atmospheric conditions. A difference in composition between fine (PM2.5) and coarse (PM>2.5) size fractions emerged from the NMR data, the former showing higher alkylic content, the latter being largely dominated by R-O-H (or R-O-R') functional groups. Very small particles (<0.14 μm), however, present higher alkyl-chain content and less oxygenated carbons than larger fine particles (0.42–1.2 μm). More limited variations were found between the average compositions in the different periods of the campaign.
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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 | 0.001 | 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".