Synergistic effect of alcohol polyoxyethylene ether sodium sulphate and copper foam on methane hydrate formation
Bibliographic record
Abstract
Abstract Natural gas is the cleanest fossil energy source and its consumption is increasing rapidly, so an efficient natural gas way of storing and transporting is urgently needed. Solidified natural gas (SNG) technology is gaining traction because of its higher safety, lower cost, and flexible storage and transportation modes. To improve the methane uptake rate in SNG technology, this work investigated the growth of methane hydrate in fatty alcohol polyoxyethylene ether sodium sulphate (AES) solution with the addition of three different pores per inch (PPI) of copper foam (CF). The results showed that the addition of AES caused the hydrate to grow upwards along the wall, and the methane uptake in the 300 ppm AES solution was increased by 623% compared to pure water. CF not only provided more nucleation sites for hydrate but also transferred the heat generated during hydration. Moreover, there was a synergistic effect between AES and CF and the solution could continuously transport upward along the continuous metal skeleton to increase the gas–liquid contact area. Thus, the formation rate and induction time of methane hydrate improve. Hydrate had the highest methane uptake in the 20 PPI CF system and the lower the pressure, the greater the ability of CF to promote hydrate formation. The methane uptake improved by 27.6% and the induction time was reduced by 59.7% compared to the pure AES system at 6 MPa. This work is aimed at advancing SNG technology (especially at low pressure) and informs the theoretical foundation.
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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".