Effects of Hydrophobic and Hydrophilic Graphene Nanoflakes on Methane Dissolution Rates in Water under Vapor–Liquid–Hydrate Equilibrium Conditions
Bibliographic record
Abstract
Several industries have steadily gained interest in gas hydrate technologies for their potential use in natural gas transport and storage applications. Additives which optimize the efficiencies of these technologies, particularly nanoparticles, have lately been subject to an increasing investigative focus. Graphene nanoflakes (GNFs) have previously been proven to enhance hydrate systems, particularly methane hydrate systems. In this study, the dissolution rates of methane and molar saturation values were measured in nanofluids containing both hydrophobic (as-produced) and hydrophilic (plasma-functionalized) GNFs at 2 °C and 3146 kPa. For both types of GNFs, the effect of loading in the aqueous phase was equally determined. Dissolution rate enhancement was limited at low concentrations of around 0.5 ppm for hydrophobic GNFs due to small-scale agglomeration while significantly increasing dissolution kinetics by about 18.84% at concentrations of 5 ppm. The performance eventually decreased at higher concentrations (10 ppm) due to large-scale agglomeration. Hydrophilic GNFs, which exhibited no agglomeration, enhanced dissolution rates further with each successive loading until a 44.45% plateau at 10 ppm. This plateau may have been a limit of the system or a result of mean free path limitations. Either type of GNFs nearly triples the dissolution rates of methane investigated in previous studies on multi-walled carbon nanotubes due to their higher specific surface area.
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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 | 0.001 |
| 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".