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Record W2335293179 · doi:10.1021/ef500524m

Inhibition Activity of Antifreeze Proteins with Natural Gas Hydrates in Saline and the Light Crude Oil Mimic, Heptane

2014· article· en· W2335293179 on OpenAlexafffund
Hassan Sharifi, Virginia K. Walker, John A. Ripmeester, Peter Englezos

Bibliographic record

VenueEnergy & Fuels · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular SciencesQueen's UniversityUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaUniversity of British Columbia
KeywordsChemistryAntifreeze proteinClathrate hydrateHydrateDifferential scanning calorimetryNatural gasAntifreezeChromatographyChemical engineeringOrganic chemistryThermodynamicsBiochemistry

Abstract

fetched live from OpenAlex

For practical purposes, kinetic hydrate inhibitors must perform predictably in the presence of oil as well as saline and high driving forces, but such deterministic behavior is rarely achieved. Here, we evaluated two biological inhibitors, type I and type III antifreeze proteins (AFPs I and III), under these exacting conditions using a double high-pressure crystallizer apparatus and additionally assayed using high-pressure micro-differential scanning calorimetry. The two AFP types behaved somewhat differently under these environmental conditions. The addition of AFP I reduced natural gas hydrate induction time, whereas AFP III had no impact on hydrate crystal nucleation. Nonetheless, for both AFPs, gas hydrate growth was significantly inhibited to ∼50% of that found in control experiments. Once hydrate had formed, decomposition was slower and started later. Thus, gas hydrates formed in the presence of APF I and III appeared to remain stable outside the hydrate stable zone, an observation that has also been noted for other inhibitors. Our observations have potential implications for the use of biological inhibitors under subsea pipeline conditions.

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 score0.014
Threshold uncertainty score0.308

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 head0.004
GPT teacher head0.178
Teacher spread0.174 · 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

Citations28
Published2014
Admission routes2
Has abstractyes

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