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Record W2896673025 · doi:10.1039/c8nr06204h

Supercritical CO<sub>2</sub>-induced atomistic lubrication for water flow in a rough hydrophilic nanochannel

2018· article· en· W2896673025 on OpenAlexaff
Tuan A. Ho, Yifeng Wang, Anastasia Ilgen, Louise Criscenti, Craig M. Tenney

Bibliographic record

VenueNanoscale · 2018
Typearticle
Languageen
FieldEngineering
TopicNanopore and Nanochannel Transport Studies
Canadian institutionsResponse Biomedical (Canada)
FundersBasic Energy SciencesNational Nuclear Security AdministrationOffice of ScienceNational Energy Technology LaboratoryRoyal Society of ChemistryRoyal SocietySandia National LaboratoriesU.S. Department of Energy
KeywordsLubricationLubricantSupercritical fluidMaterials scienceFlow (mathematics)NanofluidicsAtomic force microscopyRough surfaceNanotechnologyMolecular dynamicsFlow propertiesChemical engineeringChemical physicsMechanicsComposite materialChemistryComputational chemistryOrganic chemistryPhysicsEngineering

Abstract

fetched live from OpenAlex

A fluid flow in a nanochannel highly depends on the wettability of the channel surface to the fluid. The permeability of the nanochannel is usually very low, largely due to the adhesion of fluid at the solid interfaces. Using molecular dynamics (MD) simulations, we demonstrate that the flow of water in a nanochannel with rough hydrophilic surfaces can be significantly enhanced by the presence of a thin layer of supercritical carbon dioxide (scCO2) at the water-solid interfaces. The thin scCO2 layer acts like an atomistic lubricant that transforms a hydrophilic interface into a super-hydrophobic one and triggers a transition from a stick- to- a slip boundary condition for a nanoscale flow. This work provides an atomistic insight into multicomponent interactions in nanochannels and illustrates that such interactions can be manipulated, if needed, to increase the throughput and energy efficiency of nanofluidic systems.

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 score0.014
Threshold uncertainty score0.991

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 head0.016
GPT teacher head0.236
Teacher spread0.220 · 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

Citations63
Published2018
Admission routes1
Has abstractyes

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