Nonderivatization Analytical Method of Fatty Acids and<i>cis</i>-Pinonic Acid and Its Application in Ambient PM<sub>2.5</sub>Aerosols in the Greater Vancouver Area in Canada
Bibliographic record
Abstract
A nonderivatization analytical method has been developed to analyze C6-C20 fatty acids and cis-pinonic acid on a GC/ FID and a GC/MSD using a polar DB-FFAP capillary column. On the GC/FID, the response was highly linear over concentration ranges >2 orders of magnitude (R2 = 1.00). Using a mixed solvent of dichloromethane (DCM): methanol (3:1, v/v) and an extraction temperature of 40 degrees C, the method recoveries of the acids from spiked filters were 81-115% based on deuterated surrogates, and the relative standard deviations were <12%. The recoveries were mainly controlled by the extraction temperature. At 40 degrees C the acids in sample extracts were stable for at least 12 months, while at 80 degrees C, unintended esterization of the acids by methanol was found that reduces their stability in the sample extracts. The analysis of the acids in PM2.5 samples from NIST using this nonderivatization method showed that the efficiency and accuracy were comparable to the derivatization method. Compared with existing derivatization methods, the method is accurate and sensitive, yet simple to use. This method was applied to PM2.5 ambient aerosols collected from a forest site and at a traffic tunnel outlet in the greater Vancouver area in Canada. Total fatty acids (sum of C6-C20) in the aerosols were measured as 20.2-138.7 ng/m3 at the forest site and 100.2-264.6 ng/m3 at the tunnel site. The cis-pinonic acid concentrations were 1.6-44.2 ng/m3 in the forest and from below detection to 6.5 ng/m3 at the tunnel outlet.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".