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Record W2064620841 · doi:10.1063/1.2141506

Molecular-dynamics simulations of binary structure II hydrogen and tetrahydrofurane clathrates

2005· article· en· W2064620841 on OpenAlexafffund
Saman Alavi, John A. Ripmeester, D. D. Klug

Bibliographic record

VenueThe Journal of Chemical Physics · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsSteacie Institute for Molecular Sciences
FundersNational Research Council Canada
KeywordsChemistryMoleculeClathrate hydrateMolecular dynamicsCrystallographyHydrogenHydrogen bondBinary numberChemical physicsThermodynamicsComputational chemistryPhysicsHydrateOrganic chemistry

Abstract

fetched live from OpenAlex

The binary structure II hydrogen and tetrahydrofurane (THF) clathrates are studied with molecular-dynamics simulations. Simulations are done at pressures of 120 and 1.013 bars for temperatures ranging from 100 to 273 K. For the small cages of the structure II unit cell, H2 guest molecule occupancies of 0, 16 (single occupancy), and 32 (double occupancy) are considered. THF occupancies of 0-8 in the large cages are studied. For cases in which THF does not occupy all large cages in a unit cell, the remaining large cages can be occupied with sets of four H2 guest molecules. The unit-cell volumes and configurational energies are compared in the different occupancy cases. Increasing the small cage occupancy leads to an increase in the unit-cell volume and thermal-expansion coefficient. Among simulations with the same small cage occupancy, those with the large cages containing 4H2 guests have the largest volumes. The THF guest molecules have a stabilizing effect on the clathrate and the configurational energy of the unit cell decreases linearly as the THF content increases. For binary THF + H2 clathrates, the substitution of the THF molecules in the large cages with sets of 4H2 molecules increases the configurational energy. For the binary clathrates, various combinations of THF and H2 occupancies have similar configurational energies.

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 score0.050
Threshold uncertainty score0.254

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 head0.005
GPT teacher head0.212
Teacher spread0.207 · 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

Citations127
Published2005
Admission routes2
Has abstractyes

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