The effects of multi-wall carbon nanotubes on hydrate formation
Bibliographic record
Abstract
Clathrate hydrates are crystalline compounds that form under suitable thermodynamic conditions when inclusion molecules become trapped in a water lattice.Aside from being a hindrance to the oil and gas industry by blocking pipelines, naturally-occurring gas hydrates account for over 50% of the global carbon-based fuel reserves.This virtually untapped resource holds large potential for future extractions.In addition, the high gas storage capacity of hydrates has inspired the development of novel technologies used for transporting natural gas as well as sequestering carbon dioxide.This thesis investigates the effects of multi-wall carbon nanotubes (MWNTs) on hydrate formation.In view of optimizing hydrate-based technologies for applications including the transportation and storage of gases, the promoting effects of the MWNTs were evaluated for several hydrate systems.In order to examine the heat effects associated with adding MWNTs, an infrared camera was set up to capture the heat propagation accompanying the formation of tetrahydrofuran (THF) hydrates.The study revealed that for a given sub-cooling, the presence of hydrophobic and hydrophilic MWNTs caused an increase in the velocity of the heat front.This can be attributed to the high thermal conductivity of the MWNTs.Investigations with methane were conducted in order to model natural gas behavior.Both the dissolution phase and growth rates of the methane hydrates were examined.As-produced (hydrophobic) and plasmafunctionalized (hydrophilic) MWNTs were added at various loading concentrations to the methane and water system.It was found that the hydrophobicity of the as-produced MWNTs provided a limited dissolution and growth rate improvement, whereas their plasma-functionalized counterpart provided an enhancement at both stages of formation.This result is attributed to optimized mass transfer effects associated with the nanoparticles.Experiments with carbon dioxide were conducted under similar conditions.Two forms of MWNTs were also investigated, namely as-produced and amine-functionalized MWNTs.Due to the high solubility of carbon dioxide in water, the presence of the MWNTs did not affect the rate of dissolution as they had with the sparingly soluble methane.For the growth rate experiments, it was found that both types of the MWNTs demonstrated an enhancement, with the amine-functionalized Abstract ii MWNTs performing slightly better.Under higher concentrations however, the presence of both forms of nanoparticles produced a considerable nucleation event which induced heat and mass transfer limitations on the system.In summary, the enhancements viewed with the addition of the MWNTs can be attributed to their heat and mass transfer aiding properties.
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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) | 0.001 | 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".