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Record W2028558234 · doi:10.1002/cjce.5450810508

Catalysis of Gas Hydrates by Biosurfactants in Seawater‐Saturated Sand/Clay

2003· article· en· W2028558234 on OpenAlexvenueno aff
Rudy Rogers, Chandra Kothapalli, May S. Lee, J. Robert Woolsey

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
Fundersnot available
KeywordsClathrate hydrateSeawaterHydrateChemistryMicelleChemical engineeringMethaneGeologyMineralogyOrganic chemistryAqueous solutionOceanography

Abstract

fetched live from OpenAlex

Abstract Biosurfactants catalyzed natural gas hydrate formation in sand/clay packs saturated with seawater. Representative samples from the five possible biosurfactant classifications enhanced hydrate formation rate and decreased hydrate induction time. Biosurfactants increased rates 96% to 288% and decreased induction times 20% to 71% relative to the control. Micellar‐forming rhamnolipid reached a critical micellar concentration at 13 ppm at hydrate‐forming conditions; these micelles migrated readily through a seawater‐saturated sand pack to catalyze hydrate formation in another zone. The type of biosurfactant, in conjunction with specific porous media, help determine massive, dispersed, nodular, or stratified forms of hydrates. Results suggested that minimal microbial activity in ocean‐floor sands can greatly influence gas hydrate formation.

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 score0.046
Threshold uncertainty score0.613

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 head0.165
Teacher spread0.160 · 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

Citations67
Published2003
Admission routes1
Has abstractyes

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