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Record W3215004871 · doi:10.3390/methane1010002

Mechanisms, Growth Rates, and Morphologies of Gas Hydrates of Carbon Dioxide, Methane, and Their Mixtures

2021· article· en· W3215004871 on OpenAlexafffund
Camilo Martinez, Juan F. Sandoval, Nathalia Ortiz, Sebastian Ovalle, Juan G. Beltrán

Bibliographic record

VenueMethane · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsRoyal Military College of Canada
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsMethaneCarbon dioxideClathrate hydrateDissociation (chemistry)HydrateChemistryCrystal growthSubcoolingGrowth rateChemical engineeringChemical physicsCrystallographyPhysical chemistryOrganic chemistryBoiling

Abstract

fetched live from OpenAlex

Mechanisms of growth and dissociation, growth rates, and morphology of gas hydrates of methane, carbon dioxide, and two CH4:CO2 mixtures (80:20 and 30:70 nominal concentration) were studied using using high resolution images and very precise temperature control. Subcooling and a recently proposed mass transfer-based driving force were used to analyze the results. When crystal growth rates did not exceed 0.01 mm/s, all systems showed faceted, euhedral crystal habits at low driving forces. At higher driving forces and growth rates, morphologies were different for all systems. These results solve apparent contradictions in literature about the morphology of hydrates of methane, carbon dioxide, and their mixtures. Differences in the growth mechanism of methane-rich and carbon dioxide-rich hydrates were elucidated. It was also shown that hydrate growth of methane, carbon dioxide, and their mixtures proceed via partial dissociation of the growing crystal. Temperature gradients were used to dissociate hydrates at specific locations, which revealed a most interesting phenomenon: On dissociation, carbon dioxide-rich hydrates propagated onto the bare substrate while drawing water from the opposite side of the sample. Furthermore, it was shown that an abrupt change in morphology common to all systems could be correlated to a change in the slope of growth rate data. This change in morphology was explained by a shift in the crystal growth mechanism.

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)0.001
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 score0.024
Threshold uncertainty score0.663

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.010
GPT teacher head0.216
Teacher spread0.205 · 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

Citations21
Published2021
Admission routes2
Has abstractyes

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