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Record W4235718555 · doi:10.1002/ange.201403638

Antifreezes Act as Catalysts for Methane Hydrate Formation from Ice

2014· article· en· W4235718555 on OpenAlexaff
G. E. McLaurin, Kyuchul Shin, Saman Alavi, John A. Ripmeester

Bibliographic record

VenueAngewandte Chemie · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMethaneMethanolHydrateChemistryAqueous solutionClathrate hydrateCatalysisInorganic chemistryAdsorptionRaman spectroscopyChemical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Contrary to the thermodynamic inhibiting effect of methanol on methane hydrate formation from aqueous phases, hydrate forms quickly at high yield by exposing frozen water–methanol mixtures with methanol concentrations ranging from 0.6–10 wt % to methane gas at pressures from 125 bars at 253 K. Formation rates are some two orders of magnitude greater than those obtained for samples without methanol and conversion of ice is essentially complete. Ammonia has a similar catalytic effect when used in concentrations of 0.3–2.7 wt %. The structure I methane hydrate formed in this manner was characterized by powder X‐ray diffraction and Raman spectroscopy. Steps in the possible mechanism of action of methanol were studied with molecular dynamics simulations of the Ih (0001) basal plane exposed to methanol and methane gas. Simulations show that methanol from a surface aqueous layer slowly migrates into the ice lattice. Methane gas is preferentially adsorbed into the aqueous methanol surface layer. Possible consequences of the catalytic methane hydrate formation on hydrate plug formation in gas pipelines, on large scale energy‐efficient gas hydrate formation, and in planetary science are discussed.

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 categoriesInsufficient payload (model declined to judge)
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 score0.061
Threshold uncertainty score1.000

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)0.0010.001

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 head0.013
GPT teacher head0.234
Teacher spread0.221 · 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.

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

Citations29
Published2014
Admission routes1
Has abstractyes

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