Molecular Insights into the Concentration Dependent Promotion Effect of Tetrabutylammonium Bromide on Hydrate Growth: A Molecular Dynamics Simulation Study
Bibliographic record
Abstract
Tetrabutylammonium bromide (TBAB) has been proven to improve the growth of hydrate via experimental methods, which may be attributed to its different concentrations. In this study, the molecular dynamics (MD) simulation is employed to investigate the concentration dependent promotion effect of TBAB on the growth of CO 2 hydrate. The tetrahedral order parameter, number of cages, hydrate crystal growth trajectories, significant microconfigurations, and distribution of CO 2 and TBAB are analyzed in detail. It is found that the promotion effect of TBAB is more prominent at low concentrations (5 and 10 wt %) than that at high concentrations (15 and 20 wt %). During the growth of hydrate crystal, tetrabutylammonium (TBA + ) ions adsorb on the hydrate crystal and serve as guest molecules to form TBA + semiclathrate hydrate cages. Then, the TBA + semiclathrate hydrate cages undergo a self-adjustment process and induce the generation of CO 2 hydrate cages. At high concentrations, the great accumulation of TBA + at the hydrate–liquid interface disturbs the effective adsorption and self-adjustment processes, and the tightly packed arrangement of TBA + at the gas–liquid interface partially inhibits the mass transfer of CO 2 . This study provides visible mechanisms of the concentration dependent promotion effect of TBAB from the microscopic level, which complements the vacancy in experimental studies.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".