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Record W4281728245 · doi:10.1002/cjce.24493

Equilibrium properties and crystal growth of <scp> D <sub>2</sub> O </scp>  + cyclopentane hydrate for sustainable tritium separation

2022· article· en· W4281728245 on OpenAlexvenueno aff
Meku Maruyama, Ryo Ohmura

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsnot available
FundersJKA Foundation
KeywordsCyclopentaneHydrateClathrate hydrateCrystal growthCrystal (programming language)CrystallizationThermodynamic equilibriumTritiumChemistrySubstrate (aquarium)CrystallographyThermodynamicsAnalytical Chemistry (journal)StereochemistryOrganic chemistryGeologyNuclear physics

Abstract

fetched live from OpenAlex

Abstract This study reports thermodynamic properties and crystal growth observations of D 2 O + cyclopentane hydrate toward the development of a hydrate‐based tritium separation process. We employed D 2 O as a host of hydrate with respect to the investigation into the formation of D 2 O hydrate substrate, which is a core component of the tritium separation process. The hydrate phase equilibrium temperature in the liquid D 2 O + liquid cyclopentane system was 3.2°C higher than the corresponding H 2 O hydrate. Experiments on crystal growth were conducted at temperatures ranging from 5.1 to 8.5°C under atmospheric pressure. Under each thermodynamic condition, polygonal hydrate crystals appeared, growing along the D 2 O/cyclopentane interface. The geometric shape and size of the crystals varied depending on the temperature. The variation in crystal morphology was comparable to that of the H 2 O + cyclopentane hydrate. Implications based on the obtained results for the hydrate‐based isotopic water separation process design are discussed.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.176
Teacher spread0.168 · 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 source (direct Gemma or distilled Codex), 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

Citations15
Published2022
Admission routes1
Has abstractyes

Explore more

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