Multicomponent Mass Transfer in Dissolved Gas Analysis: The Impacts of Headspace Pressurization on Reliable Measurement
Bibliographic record
Abstract
Measuring dissolved gas concentrations such as methane (CH 4 ), carbon dioxide (CO 2 ), and hydrogen (H 2 ) (e.g., groundwater or surface water samples) is important for ensuring safe and effective subsurface energy development and storage. A common method is to collect water samples in fixed-volume sealed vessels and then use static headspace equilibrium techniques to quantify the dissolved gas concentrations by gas chromatography. Previously, the presence of multiple gas components was not considered during the analysis of water samples but is necessary. A mass balance approach considering multicomponent mass transfer was developed and validated, and the impact on dissolved gas measurements was quantified. It was found that mass transfer occurring in fixed-volume vessels leads to pressurization of sample headspace during analysis, causing error. Higher solubility gases (e.g., CO 2 ) exhibit higher headspace pressurization and larger errors than lower solubility gases (e.g., CH 4 ). In addition, it was found that the volume of the headspace induced and the co-occurrence of multiple dissolved gas species in a sample can exacerbate headspace pressurization and error. Overall, caution must be taken when using static headspace equilibrium techniques; if multicomponent mass transfer is not considered, error and potential under reporting of dissolved concentrations is possible.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".