Clathrate nucleation and inhibition from a molecular perspective
Bibliographic record
Abstract
Identifying the molecular processes that lead to clathrate-hydrate nucleation has been an active area of research for more than a decade. The question has a number of important ramifications, spanning applications in geology (formation and stability of natural methane-hydrate deposits), environmental science (CO2 sequestration), and industry (prevention of hydrate blockages). The drive to develop more active and robust hydrate inhibitors for the oil industry that work at very low dosages, in particular, has been slowed down because our understanding of the molecular mechanisms by which such inhibitors work is still largely conjectural. In this paper, we present results from the first direct molecular-dynamics simulations of the inhibition of nucleation in methane hydrate. Molecular-dynamics simulations have been used to simulate the behaviour of a thin film of water under a methane atmosphere with and without poly(vinylpyrrolidone) (PVP). Simulations in the absence of PVP show clear evidence of the nucleation and growth of methane hydrate; this behaviour is completely suppressed, however, when PVP is included in the simulation. We conclude that these simulations provide an excellent basis for understanding the way in which PVP inhibits hydrate nucleation. PACS Nos.: 81.10Aj, 81.10Dn, 81.10Fq
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".