Improving the Quantification of Secondary Organic Aerosol Using a Microflow Reactor Coupled to HPLC-MS and NMR to Manufacture Ad Hoc Calibration Standards
Bibliographic record
Abstract
Secondary organic aerosol (SOA) is a key uncertainty in quantifying the impact of humans on Earth's climate. SOA is a complex mixture of oxidized organic species, and a fundamental hurdle in determining its composition is the lack of authentic standards for comparison and quantification. Organic synthesis can be used to produce pure standards, but is limited to compounds for which there is a degree of confidence in the proposed structure and can be expensive and time-consuming. In this study, a flow reactor was developed to form SOA in sufficient quantities to be collected and pure compounds subsequently isolated from the mixture using semipreparative high performance liquid chromatography. The purity and yield of each isolated compound were obtained using proton nuclear magnetic resonance ((1)H NMR), whereas molecular formulas were confirmed by high resolution Fourier transform ion cyclotron mass spectrometry (FTICR-MS). The effectiveness of the methodology has been evaluated here by using α-pinene as the precursor because it is the monoterpene with the most well characterized SOA chemistry. Eleven individual α-pinene SOA compounds were produced from α-pinene oxidation experiments and used for quantitative analysis of SOA formed during chamber experiments carried out close to ambient conditions. These compounds represented 25% of the total SOA mass, a significant improvement in mass balance compared to previous studies. This relatively simple approach may be extended to produce other SOA components not available commercially to improve quantification of aerosol sources.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".