Acryloyl piperidine/pyrrolidine statistical and block copolymers as hydrate inhibitors: effects of cloud point temperature and copolymer microstructure
Bibliographic record
Abstract
Ongoing investigations on the polymer chemistry and physical properties of kinetic hydrate inhibitors (KHIs) have led to several theories attempting to interpret their inhibition mechanisms during hydrate nucleation and growth, crucial for flow assurance in offshore facilities. Namely, numerous reports have studied the relationship between the hydrophobicity, cloud point temperatures (CPTs) and copolymer microstructures of KHIs and their abilities to reduce hydrate formation. To verify these theories, statistical and block copolymers with various compositions (0, 30, 50, 70, 100 mol.%) of acryloyl pyrrolidine (APy) with acryloyl piperidine (APi) were synthesized and sI methane hydrate growth kinetics were measured at 2 °C and 4646 kPa. All tested KHI samples reduced methane consumption to less than 25% of that of an uninhibited system. Poly(APi) with bigger amide rings and lower CPT acted as a poorer KHI than the more hydrophilic poly(APy). The statistical copolymers with a wide range of CPTs from 4 to 48 °C showed that KHI performance was not influenced by CPT, as the sI hydrate growth rate exhibited a nearly linear relationship with respect to copolymer composition. On the other hand, results of block copolymers suggest that the thermoresponsive behavior of these surfactant-like additives might play a role during hydrate growth inhibition. The block copolymer with 30 mol.% APi acted as the best KHI amongst all tested samples, while the block copolymer with 70 mol.% APi acted as the poorest. The results were significantly different from the statistical copolymers with similar composition; thus, copolymer microstructure is also a factor to consider during KHI design.
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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) | 0.000 | 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".