MétaCan
Menu
Back to cohort
Record W2040804766 · doi:10.1021/ef900543v

Recovery of Methane from Hydrate Formed in a Variable Volume Bed of Silica Sand Particles

2009· article· en· W2040804766 on OpenAlexafffund
Praveen Linga, Cef Haligva, Sung Chan Nam, John A. Ripmeester, Peter Englezos

Bibliographic record

VenueEnergy & Fuels · 2009
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsNational Research Council CanadaSteacie Institute for Molecular SciencesUniversity of British Columbia
FundersNatural Resources CanadaNatural Sciences and Engineering Research Council of CanadaMinistry of Education and Human Resources Development
KeywordsMethaneHydrateMicroporous materialClathrate hydrateVolume (thermodynamics)MineralogyChemical engineeringChemistryParticle sizeDissociation (chemistry)DecompositionThermodynamicsOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

The decomposition of methane hydrate crystals formed in sediment at 1.0, 4.0, and 7.0 °C was studied in a new apparatus designed to accommodate three different size volume beds of silica sand particles. The sand particles are microporous with a 0.9 nm pore diameter and have an average diameter equal to 329 μm. The hydrate was formed in the interstitial spaces between sand particles, and the hydrate crystal decomposition was driven by heating (thermal stimulation). The amount of methane released from the dissociating hydrate in each experiment (methane recovery curve) was determined, and it was shown that the release of gas proceeds in two stages in terms of rate. The rate of methane release (recovery) per mole of water depends on the bed size for the first stage of hydrate dissociation. The second stage rate does not depend on the bed size. This work suggests that the comparison of simulated data to experimental results from laboratory synthesized hydrate and possibly from natural samples should be done with more than one sample-size data.

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 score0.060
Threshold uncertainty score0.998

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)0.0020.000

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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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

Citations115
Published2009
Admission routes2
Has abstractyes

Explore more

Same venueEnergy & FuelsSame topicMethane Hydrates and Related PhenomenaFrench-language works237,207