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Record W2169691700 · doi:10.1115/1.4007540

Driving Forces and Transportation Efficiency in Water Transportation Through Single-Walled Carbon Nanotubes

2012· article· en· W2169691700 on OpenAlexaff
Meng Sun, Wenhui Duan, Quan Wang, Martin Dowman, Jayantha Kodikara

Bibliographic record

VenueJournal of Nanotechnology in Engineering and Medicine · 2012
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsUniversity of Manitoba
FundersAustralian Research Council
KeywordsCarbon nanotubeMolecular dynamicsWater transportMoleculeMaterials scienceChemical physicsChannel (broadcasting)NanotechnologyPotential of mean forceWater channelBrownian motionNanofluidicsCarbon fibersMechanicsChemical engineeringComposite materialChemistryWater flowComputational chemistryEnvironmental engineeringEnvironmental sciencePhysicsMechanical engineering

Abstract

fetched live from OpenAlex

Based on the concept of an energy pump, water transportation in a carbon nanotube (CNT) is studied by molecular dynamics simulations. The influences of CNT pretwist angle, water mass, environmental temperature, CNT diameter, CNT channel length, and CNT channel restrain condition on driving force and transportation efficiency are investigated. It is found that in order to initiate the transportation, the pretwist angle must be larger than certain threshold, 80 deg, for the case of one water molecule in a restrained (8,0) CNT. Furthermore, driving force decreases with increasing water mass and it is more efficient to transport multiple water molecules than one water molecules. The water molecule is found to have higher degrees of collisions in a (8,0) CNT in elevated environmental temperature. By comparing three CNT channel lengths, the channel length of 19.80 nm is identified as a faster and more efficient transporter in an unrestrained (8,8) CNT. Finally, molecular dynamics (MD) simulation indicates that a water molecule can only be transported below 300 K in an unrestrained (8,8) CNT due to the large friction caused by severely deformed channel and the Brownian motion.

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 score0.119
Threshold uncertainty score0.526

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 head0.008
GPT teacher head0.208
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.

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

Citations1
Published2012
Admission routes1
Has abstractyes

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