Testing antifreeze protein from the longhorn beetle <i>Rhagium mordax</i> as a kinetic gas hydrate inhibitor using a high-pressure micro differential scanning calorimeter
Bibliographic record
Abstract
Low dosage kinetic hydrate inhibitors are employed as alternatives to expensive thermodynamic inhibitors to manage the risk of hydrate formation inside oil and gas pipelines. These chemicals need to be tested at appropriate conditions in the laboratory before deployment in the field. A high pressure micro differential scanning calorimeter HP-μDSC VII (Setaram Inc.) containing two 50 cc high pressure cells (maximum operating pressure 40 MPa; temperature range –40 to 120 °C) was employed to observe methane hydrate formation and decomposition in the presence of hyperactive antifreeze protein from Rhagium mordax (RmAFP) and biodegradable synthetic kinetic inhibitor Luvicap Bio. A systematic capillary dispersion method was used, and this method enhanced the ability to detect the effect of various inhibitors on hydrate formation with small quantities. The presence of RmAFP and Luvicap Bio influence (inhibit) the hydrate formation phenomena significantly. Luvicap Bio (relative strength compared to buffer: 13.3 °C) is stronger than RmAFP (9.8 °C) as a nucleation inhibitor. However, the presence RmAFP not only delays hydrate nucleation but also reduces the amount of hydrate formed (20%–30%) after nucleation significantly. Unlike RmAFP, Luvicap Bio promoted the amount of hydrate formed after nucleation. The superior hydrate growth inhibition capability and predictable hydrate melting behavior compared to complex, heterogeneous hydrate melting with Luvicap Bio shows that RmAFP can be a potential natural green kinetic inhibitor for hydrate formation in pipelines.
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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".