Effects of Poly(styrene/Pentafluorostyrene-block-vinylpyrrolidone) Amphiphilic Kinetic Hydrate Inhibitors on the Dynamic Viscosity of Methane Hydrate Systems at High-Pressure Driving Forces
Bibliographic record
Abstract
Inhibiting viscosity increases from hydrate formation has been a persistent flow assurance challenge for decades. In this study, two low-dosage kinetic hydrate inhibitors with 10 wt % hydrophobic content, amphiphilic block copolymers poly(styrene- b -vinylpyrrolidone) and poly(pentafluorostyrene- b -vinylpyrrolidone), were synthesized using reversible addition–fragmentation chain-transfer polymerization with a switchable chain-transfer agent. Aqueous solutions of these copolymers at 700 and 7000 ppm were loaded in a high-pressure rheometer and tested at pressures up to 15 MPag and temperatures from 0 to 6 °C. The dynamic viscosity profiles of the methane hydrates slurries were recorded, and the 700 ppm system reached 200 mPa·s 2.2–2.4 times slower than pure water. This value was 1.3 for the poly(vinylpyrrolidone) homopolymer, suggesting a reduced tendency for hydrate particle adhesion in block copolymer solutions. At 7000 ppm, the relative time did not change substantially, achieving 2.6–2.7 times slower. However, a block copolymer with 5 wt % poly(pentafluorostyrene) at 7000 ppm reached 3.5 times slower, which indicates that the optimal hydrophobic content might differ for each amphiphilic polymer solution. No significant effects of molecular weight and dispersity on hydrate growth were observed for copolymers with similar composition.
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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".