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Record W2019819717 · doi:10.1039/c002187c

Synthesis and characterization of clathrate hydrates containing carbon dioxide and ethanol

2010· article· en· W2019819717 on OpenAlexaff
Takeshi Makiya, Takashi Murakami, Satoshi Takeya, Amadeu K. Sum, Saman Alavi, Ryo Ohmura

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsUniversity of OttawaSteacie Institute for Molecular Sciences
Fundersnot available
KeywordsClathrate hydrateCarbon dioxideCharacterization (materials science)EthanolChemistryCarbon fibersChemical engineeringNanotechnologyMaterials scienceHydrateOrganic chemistryEngineeringComposite material

Abstract

fetched live from OpenAlex

This paper reports an experimental study on the formation of a clathrate hydrate containing ethanol as a guest substance together with carbon dioxide. Phase equilibrium measurements in the system of CO(2) + ethanol + water have been performed in the temperature range from 254 K to 268 K. The measured equilibrium pressure at a given temperature below 264 K was lower by 20 kPa to 30 kPa than the corresponding equilibrium pressure of a simple CO(2) hydrate in the system without ethanol. Powder X-ray diffraction measurements indicated that the hydrate formed is structure I. This crystallographic structure is the same as that of simple CO(2) hydrate. However, the lattice constant of the hydrate formed in the system with ethanol was determined to be 11.8577(5) A at 113 K, which is larger than the corresponding lattice constant (11.8434(8) A) of simple CO(2) hydrate at the same temperature. These experimental results strongly suggest the formation of a new hydrate containing both ethanol and CO(2) as guest substances, thus contributing toward future applications considering CO(2) hydrates. To further understand the inclusion of ethanol molecules in the structure I hydrate and the measured increase in the crystal lattice constant, molecular dynamics simulations were performed to support the experimental results.

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 score0.006
Threshold uncertainty score0.659

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 head0.006
GPT teacher head0.201
Teacher spread0.195 · 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

Citations51
Published2010
Admission routes1
Has abstractyes

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