Promotion of hydrate formation by multi‐walled carbon nanotubes in ultrasonic compounding system
Bibliographic record
Abstract
Abstract Multi‐walled carbon nanotubes (MWCNTs) are an excellent hydrate promoter, with their own Brownian motion of nanoparticles effectively shortening hydrate nucleation and accelerating hydrate formation. In this work, the properties of methane hydrate formation in a complex system of MWCNTs, sodium dodecyl sulphate (SDS) and NaCl were investigated. It was shown that the compounding system effectively enhanced the kinetics of methane hydrate formation, and the gas consumption of the reaction reached 0.38 MPa at 100 ppm MWCNTs, an increase of 865.8% compared to the pure water system, effectively promoting methane hydrate. In the complexed system, NaCl significantly enhanced the dispersion of MWCNTs, with 1000 ppm NaCl showing the best kinetic promotion effect. SDS not only increases the gas–liquid contact area through the wall attachment effect, but also enhances the dispersion of MWCNTs by adsorbing on the surface of carbon nanotubes and forming an electronic layer with NaCl. MWCNTs not only improve the mass transfer of the system through Brownian motion, but their large heat transfer coefficients can also effectively conduct the heat generated by the system. However, MWCNTs become agglomerated with increasing concentration, making the kinetic promotion effect weaker and the solution less stable, resulting in shorter shelf life. This study confirmed the effective promotion of hydrate formation by MWCNTs under the ultrasonic compounding system, and also provided a reference for related studies on the compounding of MWCNTs with NaCl.
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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".