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Record W2990043327 · doi:10.1021/acs.analchem.9b03907

Effect of Substrate Permeability on Scanning Ion Conductance Microscopy: Uncertainty in Tip–Substrate Separation and Determination of Ionic Conductivity

2019· article· en· W2990043327 on OpenAlexafffund
Nicholas A. Payne, Jeremy I. G. Dawkins, Steen B. Schougaard, Janine Mauzeroll

Bibliographic record

VenueAnalytical Chemistry · 2019
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversité du Québec à MontréalMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMitacs
KeywordsChemistryIonic conductivityConductivityElectrodePorosityComposite numberSurface conductivityIonSubstrate (aquarium)MicroscopyComposite materialAnalytical Chemistry (journal)NanotechnologyMaterials scienceOpticsChromatography

Abstract

fetched live from OpenAlex

Composite electrodes can significantly improve the performance of an electrochemical device by maximizing surface area and active material loading. Typically, additives such as carbon are used to improve conductivity and a polymer is used as a binder, leading to a heterogeneous surface film with thickness on the order of 10s of micrometers. For such composite electrodes, good ionic conduction within the film is critical to capitalize on the increased loading of active material and surface area. Ionic conductivity within a film can be tricky to measure directly, and homogenization models based on porosity are often used as a proxy. SICM has traditionally been a topography-mapping microscopy method for which we here outline a new function and demonstrate its capacity for measuring ion conductivity within a lithium-ion battery film.

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 score0.025
Threshold uncertainty score0.662

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 head0.009
GPT teacher head0.301
Teacher spread0.292 · 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

Citations16
Published2019
Admission routes2
Has abstractyes

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