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Effects of As-Produced and Amine-Functionalized Multi-Wall Carbon Nanotubes on Carbon Dioxide Hydrate Formation

2015· article· en· W2513498820 on OpenAlexafffund
James Pasieka, Larissa Jorge, Sylvain Coulombe, Phillip Servio

Bibliographic record

VenueEnergy & Fuels · 2015
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMcGill University
FundersFonds de recherche du Québec – Nature et technologiesNatural Sciences and Engineering Research Council of CanadaHydro-Québec
KeywordsCarbon dioxideHydrateDissolutionCarbon nanotubeAmine gas treatingNucleationChemical engineeringClathrate hydrateChemistryMaterials scienceCarbon fibersMethaneNanotechnologyOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

In an attempt to mitigate carbon dioxide emissions, new technologies are being proposed that can aid in greenhouse gas sequestration. One such technology involves trapping carbon dioxide in hydrate form. To optimize this process, it is of interest to investigate how the hydrate system behaves in the presence of promoting agents. In this work, the effect of as-produced and amine-functionalized multi-wall carbon nanotubes (MWNTs) on carbon dioxide dissolution and hydrate growth rates was studied. For either type of the MWNTs, the effect on saturation values and dissolution rates was negligible. Under lower concentrations, it was found that both forms of MWNTs enhanced growth, with the functionalized MWNTs performing slightly better. Under higher concentrations, both forms of MWNTs decreased growth rates, because the initial nucleation event was more pronounced and led to heat- and mass-transfer limitations.

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 score0.013
Threshold uncertainty score0.575

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 head0.012
GPT teacher head0.212
Teacher spread0.200 · 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

Citations27
Published2015
Admission routes2
Has abstractyes

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