Bridging effects in THF clathrate hydrates
Bibliographic record
Abstract
Clathrate hydrates are crystalline solids composed of water and a guest molecule,which is often a volatile liquid or a gas. There are many potential applications for gashydrates, such as their use for the transport and storage of various gases, as well as for thesequestration of CO2 in deep waters. However, gas hydrates can also be problematic forthe oil and gas industry, where hydrate formation causes solid plugs that prevent flow inpipelines and damage equipment. For all these processes, hydrate formation consists oftwo stages: nucleation followed by propagation or growth. Full understanding of hydratepropagation mechanisms is still under development and several propagation mechanismshave been identified. The primary focus of this study is the investigation of bridgingphenomena in tetrahydrofuran (THF) hydrates. A combination of infrared and visiblelight cameras was used to investigate dendritic growth and bridge propagation betweenTHF-water solution droplets. Effects of the underlying materials on the bridge formationhave also been studied, and it was concluded that surfaces possessing low thermalconductivity and small contact angle tend to favor dendritic bridging. Finally, increase inroughness of the underlying surface can result in faster bridging between the droplets. Asecondary emphasis of this work was to model the solidification behavior of supercooledTHF hydrate films. Several approaches for modeling the solidification of supercooledliquids were identified and their implementation in the Matlab software package iscurrently underway. Better understanding of the bridging effect and hydrate filmformation will lead to greater knowledge of the hydrate crystallization spread, whichunderlies all hydrate applications.
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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".