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Record W2322735929 · doi:10.1103/physrevb.90.125415

Capacitance of graphene in aqueous electrolytes: The effects of dielectric saturation of water and finite size of ions

2014· article· en· W2322735929 on OpenAlexafffund
Pankaj Sharma, Z. L. Mišković

Bibliographic record

VenuePhysical Review B · 2014
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCapacitanceGrapheneElectrolyteMaterials scienceDielectricQuantum capacitanceIonAqueous solutionSaturation (graph theory)Condensed matter physicsChemical physicsAnalytical Chemistry (journal)PhysicsNanotechnologyOptoelectronicsPhysical chemistryChemistryQuantum mechanicsElectrodeOrganic chemistry

Abstract

fetched live from OpenAlex

We present a theoretical model for electrolytically top-gated graphene, in which we analyze the effects of dielectric saturation of water due to possibly strong electric fields near the surface of a highly charged graphene, as well as the steric effects due to the finite size of salt ions in an aqueous electrolyte. By combining two well-established analytical models for those two effects, we show that the total capacitance of the solution-gated graphene is dominated by its quantum capacitance for gating potentials $\ensuremath{\lesssim}1\phantom{\rule{0.28em}{0ex}}\mathrm{V}$, which is the range of primary interest for most sensor applications of graphene. On the other hand, at the potentials $\ensuremath{\gtrsim}1\phantom{\rule{0.28em}{0ex}}\mathrm{V}$ the total capacitance is dominated by a universal capacitance of the electric double layer in the electrolyte, which exhibits a dramatic decrease of capacitance with increasing gating potential due to the interplay of a fully saturated dielectric constant of water and ion crowding near graphene.

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)0.001
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 score0.009
Threshold uncertainty score0.137

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 head0.006
GPT teacher head0.261
Teacher spread0.255 · 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

Citations21
Published2014
Admission routes2
Has abstractyes

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