Methane conversion rate into structure H hydrate crystals from ice
Bibliographic record
Abstract
Abstract The methane uptake and conversion rate to structure H (sH) hydrates was measured and compared to crystallization kinetics models. Three large molecule guest substances (LMGS) were used as sH hydrate formers: neohexane (NH), methylcyclohexane (MCH), and tert‐butyl methyl ether ( TBME). The initial crystallization occurred quickly at the LMGS liquid‐ice interface until ∼20–30% of ice was converted into hydrate (hydrate growth stage I). Slower hydrate crystal growth was observed after a hydrate film covered the ice surface at a rate of 3–400 nm2/h (hydrate growth stage II). The TBME system showed the fastest kinetics at the beginning of the reaction followed by NH and MCH system. However the trend changed when the temperature was increased (“reaction” stage III). Surprisingly, the conversion rate achieved with the TBME system upon melting the ice was the smallest. This was attributed to the strong interaction of TBME with water molecules that increased the energy barrier for water molecules to form hydrate cages. The conversion rates were well correlated with the Avrami equation and the shrinking core model. Finally, NH was found to be the best LMGS in this study to obtain full conversion within a short reaction time and achieving high methane gas storage in the hydrate. © 2007 American Institute of Chemical Engineers AIChE J, 2007
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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) | 0.002 | 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".