MétaCan
Menu
Back to cohort
Record W2314339149 · doi:10.1103/physreve.85.051508

Redefining electrical double layer thickness in narrow confinements: Effect of solvent polarization

2012· article· en· W2314339149 on OpenAlexaff
Siddhartha Das, Suman Chakraborty, Sushanta K. Mitra

Bibliographic record

VenuePhysical Review E · 2012
Typearticle
Languageen
FieldChemistry
TopicElectrostatics and Colloid Interactions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsElectric fieldElectrolyteMaterials sciencePolarization (electrochemistry)SolventChemical physicsPermittivityPolarElectrostaticsNanometreSubstrate (aquarium)Relative permittivityCondensed matter physicsNanotechnologyDielectricOptoelectronicsChemistryComposite materialPhysicsPhysical chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

In this paper we delineate the consequences of field-dependent solvent polarization in the electric double layer (EDL) electrostatic potential distribution, and the effective EDL thickness in narrow nanofluidic confinements with thick (or overlapping) EDLs. The EDL, formed at the interface between a charged substrate and an electrolyte solution, induces a large electric field spanning across few nanometer distances from the interface. As a result, a polar solvent like water gets polarized, making its relative permittivity a function of the EDL electric field. This affects the overall EDL electrostatic potential distribution and most importantly, leads to a significant reduction of the effective EDL thickness, with the extent of the reduction being dictated by the value of field independent EDL thickness, strength of the solvent polarization, and the substrate-liquid interfacial electrostatic potential. Such a finding will necessitate redefining the classical EDL thickness, which will be of overwhelming significance in nanofluidic transport.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.000
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

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 head0.017
GPT teacher head0.325
Teacher spread0.308 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
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

Citations87
Published2012
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Review ESame topicElectrostatics and Colloid InteractionsFrench-language works237,207