Raman Spectroscopy Study on the Competition between CH<sub>4</sub> and N<sub>2</sub> to Form Mixed Gas Hydrate
Bibliographic record
Abstract
The mechanism of CH 4 /N 2 mixed hydrate formation was investigated by using in situ Raman Spectroscopy. The experiments were carried out in tetrahydrofuran (THF) aqueous solutions in the presence of sodium dodecyl sulfate (SDS) at concentrations varying from 0 to 1000 ppm. This work provides information that is expected to be useful for the design of an improved process to recover methane from low-concentration coalbed methane (LCCBM). It was found that during the hydrate formation process, CH 4 molecules preferentially enter the small hydrate cages and then N 2 molecules compete to fill the remaining empty cages. The presence of SDS in the THF solutions accelerates the incorporation of CH 4 and N 2 into the hydrates. A concentration of 500 ppm of SDS was found to be favorable for enhancing the cage occupancy of CH 4 molecules. The ratio of integral Raman peak areas ( I ) obtained at this concentration is higher than that of the other SDS concentrations. The cage occupancy competition between CH 4 and N 2 molecules is also influenced by the temperature. The temperature of 282.15 K was found to be an optimal temperature to promote the cage occupancy of CH 4 molecules. To achieve a high CH 4 separation efficiency, it is necessary to monitor the ratio of integral Raman peak areas in addition to acquiring gas consumption, the CH 4 recovery rate, and the separation factor during the hydrate formation process.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".