Rapid Analysis of Dissolved Methane, Ethylene, Acetylene and Ethane using Partition Coefficients and Headspace-Gas Chromatography
Bibliographic record
Abstract
Analysis of dissolved methane, ethylene, acetylene, and ethane in water is crucial in evaluating anaerobic activity and investigating the sources of hydrocarbon contamination in aquatic environments. A rapid chromatographic method based on phase equilibrium between water and its headspace is developed for these analytes. The new method requires minimal sample preparation and no special apparatus except those associated with gas chromatography. Instead of Henry's Law used in similar previous studies, partition coefficients are used for the first time to calculate concentrations of dissolved hydrocarbon gases, which considerably simplifies the calculation involved. Partition coefficients are determined to be 128, 27.9, 1.28, and 96.3 at 30°C for methane, ethylene, acetylene, and ethane, respectively. It was discovered that the volume ratio of gas-to-liquid phase is critical to the accuracy of the measurements. The method performance can be readily improved by reducing the volume ratio of the two phases. Method validation shows less than 6% variation in accuracy and precision except at low levels of methane where interferences occur in ambient air. Method detection limits are determined to be in the low ng/L range for all analytes. The performance of the method is further tested using environmental samples collected from various sites in Nova Scotia.
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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 | 0.002 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".