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Record W2894648828 · doi:10.1039/c8ra06678g

Investigation of dynamical properties of methane in slit-like quartz pores using molecular simulation

2018· article· en· W2894648828 on OpenAlexaff
Lilong Yang, Xiang Zhou, Kewei Zhang, Fanhua Zeng, Zhouhua Wang

Bibliographic record

VenueRSC Advances · 2018
Typearticle
Languageen
FieldEngineering
TopicHydrocarbon exploration and reservoir analysis
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsAdsorptionMethaneMicroscale chemistryQuartzDiffusionChemistryMolecular dynamicsThermodynamicsThermal diffusivityAtmospheric temperature rangeChemical physicsMoleculeChemical engineeringMaterials sciencePhysical chemistryOrganic chemistryComputational chemistryComposite material

Abstract

fetched live from OpenAlex

. Therefore, the gas adsorption behavior is classed as physical adsorption because the heat of adsorption is significantly lower than the minimum heat of gas adsorption for chemisorption. The increase in the total adsorption entropy is caused by an increase in temperature due to an increase in internal energy, which brings about a reduction in the interactions between gas molecules and walls of quartz. However, with an increase in pore size the total adsorption entropy increases, for which an explanation may be that in pores of a larger size methane molecules are adsorbed at higher-energy sites and generate a higher isosteric heat, which causes a reduction in interactions between the adsorbate and adsorbent. Regarding the influence of different water contents on the diffusion of methane, it was demonstrated that with an increase in moisture the mobility of methane molecules initially increases and then decreases, which is related to the distance between gas molecules.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.030
Threshold uncertainty score0.244

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 head0.026
GPT teacher head0.266
Teacher spread0.240 · 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 designSimulation or modeling
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

Citations30
Published2018
Admission routes1
Has abstractyes

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