SIMULATING THREE-DIMENSIONAL GAS HYDRATE GROWTH AND INHIBITION
Bibliographic record
Abstract
The economic and safety hazards associated with the ability of gas hydrates to form in pipelines have prompted our interest in the inhibition of hydrate growth. Antifreeze proteins (AFPs) adsorb to ice surfaces and certain AFPs can also inhibit the growth of hydrates formed from water molecules organized in cage-like formations around a central small gas molecule. A Monte Carlo computational method for simulating the growth of ice crystals has been developed and it has proved useful in the understanding of the inhibition mechanism of these proteins. We have modified this crystal growth software in order to simulate the growth of large structure II gas hydrates, consisting of millions of water and gas molecules. This represents a first step towards investigating the effectiveness of novel compounds to inhibit hydrate growth in silico. Here, we describe these software modifications, and our efforts to incorporate type I AFP molecules into the hydrate growth simulations. Because both the docking interaction and inhibition mechanism for AFP towards hydrates remains unknown, we have set up a number of inhibitor screens to investigate possible AFP-hydrate docking models. Our goal is to reproduce the changes to gas hydrate morphology that have been observed in the presence of AFP, which will guide our choices for the binding alignment between AFPs and hydrates. This alignment will be instrumental for determining the AFPI-inhibition mechanism and should prove invaluable for the development of novel, hyperactive hydrate inhibitors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".