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Record W2049252822 · doi:10.1021/ef200812m

Natural Gas Hydrate Formation and Decomposition in the Presence of Kinetic Inhibitors. 1. High Pressure Calorimetry

2011· article· en· W2049252822 on OpenAlexaff
Nagu Daraboina, John A. Ripmeester, Virginia K. Walker, Peter Englezos

Bibliographic record

VenueEnergy & Fuels · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular SciencesQueen's UniversityUniversity of British Columbia
Fundersnot available
KeywordsHydrateDifferential scanning calorimetryChemistryClathrate hydrateMethaneNucleationCrystallizationAntifreeze proteinRecrystallization (geology)DecompositionChemical stabilityNatural gasOrganic chemistryThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

The effect of kinetic inhibitors, both chemical (PVP and H1W85281) and biological (type III antifreeze protein), on natural gas hydrate formation was investigated using high pressure differential scanning calorimetry (HP-DSC). The presence of inhibitors decreased the overall formation of methane/ethane/propane hydrate compared to systems without added inhibitors. As well, all of the inhibitors significantly delayed hydrate nucleation as compared to water controls. However, the two classes of inhibitors were distinguished by the formation of hydrates with different stabilities. A single hydrate melting peak was seen with the antifreeze protein (AFP), and this was consistent after recrystallization. In contrast, multiple hydrate melting events, some indicating the formation of hydrate structures with high stability, were observed in the presence of the chemical inhibitors, and these varied depending on the crystallization cycle. This heterogeneity suggests that the use of these chemical inhibitors (PVP and H1W85281) may present a special challenge to operators depending upon the gas mixture and environmental conditions and that AFPs may offer a more predictable, efficacious solution in these cases.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.135
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.200
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations94
Published2011
Admission routes1
Has abstractyes

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