Needle trap extraction for <scp>GC</scp> analysis of formic and acetic acids in aqueous solution
Bibliographic record
Abstract
Formic and acetic acids are ubiquitous in the environment, food, and most of the natural products. Extraction of the acids from aqueous solution is required for their isotope analysis by the gas chromatography-isotope ratio mass spectrometry. To this objective, we have previously developed a purge-and-trap technique using the dynamic solid-phase microextraction technology, the NeedlEX. The extraction efficiency, however, remains unexamined. Here, we address this question using the flame ionization detector and isotope ratio mass spectrometer while comparing it with that of the CAR/PDMS fiber. The results show that the NeedlEX is applicable at a wide range of concentration through coordination of purge volume given the minimum amount 3.7 ng and 1.8 ng of formic and acetic, respectively, is extracted. The efficiency of NeedlEX was 6-7 times lower than the fiber at 1000 μg/mL depending on the analyte. It is, however, superior to the latter at 10 μg/mL or less owing to its lower detection limit. The extraction efficiency of both acids is equivalent in molar amount. This is, however, disguised by the different response of the flame ionization detector. The isotope ratio mass spectrometor overcomes this problem but is compromised by relatively large errors. These results are particularly useful for isotopic analysis of carboxylic acids.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".