Measuring the Effect of Multi-Wall Carbon Nanotubes on Tetrahydrofuran–Water Hydrate Front Velocities Using Thermal Imaging
Bibliographic record
Abstract
Clathrate hydrates are currently being studied for their applications in many areas such as natural gas storage and transportation, component separation, and carbon dioxide sequestration. The ability to increase hydrate production is integral in the success of these innovative technologies. It has been found that the addition of multi-wall carbon nanotubes (MWNTs) to hydrate systems promotes clathrate formation. In order to better understand how this occurs, an analysis of the heat transfer during the formation of tetrahydrofuran (THF) hydrates was performed. Two concentrations of both conventional (hydrophobic) and plasma-functionalized (hydrophilic) MWNTs were added to a THF–water hydrate-forming solution. With the use of infrared imaging, the velocity and temperature of the thermal front during hydrate formation was measured. It was found that in both cases, the presence of MWNTs elevated the velocity of the front for a given system sub-cooling. Furthermore, as the MWNT concentration increased, so did the velocities. The presence of the MWNTs also decreased the sub-cooling required for nucleation.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".