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Record W2326215116 · doi:10.1021/cg400767u

Measuring the Effect of Multi-Wall Carbon Nanotubes on Tetrahydrofuran–Water Hydrate Front Velocities Using Thermal Imaging

2013· article· en· W2326215116 on OpenAlexafffund
James Pasieka, Nathan Hordy, Sylvain Coulombe, Phillip Servio

Bibliographic record

VenueCrystal Growth & Design · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsTetrahydrofuranHydrateCarbon nanotubeClathrate hydrateMaterials scienceThermalFront (military)NanotechnologyChemical engineeringCarbon fibersMineralogyChemistryGeologyComposite materialOrganic chemistryThermodynamicsPhysics

Abstract

fetched live from OpenAlex

Clathrate hydrates are currently being studied for their applications in many areas such as natural gas storage and transportation, component separation, and carbon dioxide sequestration. The ability to increase hydrate production is integral in the success of these innovative technologies. It has been found that the addition of multi-wall carbon nanotubes (MWNTs) to hydrate systems promotes clathrate formation. In order to better understand how this occurs, an analysis of the heat transfer during the formation of tetrahydrofuran (THF) hydrates was performed. Two concentrations of both conventional (hydrophobic) and plasma-functionalized (hydrophilic) MWNTs were added to a THF–water hydrate-forming solution. With the use of infrared imaging, the velocity and temperature of the thermal front during hydrate formation was measured. It was found that in both cases, the presence of MWNTs elevated the velocity of the front for a given system sub-cooling. Furthermore, as the MWNT concentration increased, so did the velocities. The presence of the MWNTs also decreased the sub-cooling required for nucleation.

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)0.001
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 score0.129
Threshold uncertainty score0.690

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.200
Teacher spread0.182 · 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

Citations16
Published2013
Admission routes2
Has abstractyes

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