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Record W4284961088 · doi:10.1039/d2nr02712g

Dynamic viscosity of methane hydrate systems from non-Einsteinian, plasma-functionalized carbon nanotube nanofluids

2022· article· en· W4284961088 on OpenAlexafffund
Adam McElligott, André Guerra, Chong Du, Alejandro D. Rey, Jean‐Luc Meunier, Phillip Servio

Bibliographic record

VenueNanoscale · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsNanofluidCarbon nanotubeMethaneViscosityHydrateMaterials scienceChemical engineeringPlasmaNanotechnologyNanotubeChemistryOrganic chemistryNanoparticleComposite materialPhysics

Abstract

fetched live from OpenAlex

The viscosity of oxygen-functionalized multi-walled carbon nanotube (O-MWCNT) nanofluids was measured for concentrations from 0.1 to 10 ppm under conditions of 0 to 30 MPag pressures and 0 to 10 °C temperatures. The presence of O-MWCNTs did not affect the temperature dependence of viscosity but did reduce the effective viscosity of solution due to cumulative hydrogen bond-disrupting surface effects, which overcame internal drag forces. O-MWCNTs added a weak pressure dependence to the viscosity of solution because of their ability to align more with the flow direction as pressure increased. In the liquid to hydrate phase transition, the times to reach the maximum viscosity were faster in O-MWCNT systems compared to the pure water baseline. However, the presence of O-MWCNTs limited the conditions at which hydrates formed as increased nanoparticle collisions in those systems inhibited the formation of critical clusters of hydrate nuclei. The times to viscosity values most relevant to technological applications were minimally 28.02% (200 mPa s) and 21.08% (500 mPa s) slower than the baseline, both in the 1 ppm system, even though all systems were faster to the final viscosity. This was attributed to O-MWCNT entanglement, which resulted in a hydrate slurry occurring at lower viscosity values.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.704
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.206
Teacher spread0.199 · 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.

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

Citations11
Published2022
Admission routes2
Has abstractyes

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