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Record W3034438133 · doi:10.1039/d0an00409j

Evaluation of the electroanalytical performance of carbon-on-gold films prepared by electron-beam evaporation

2020· article· en· W3034438133 on OpenAlexafffund
Thuy P. Nguyen, Richard L. McCreery, Mark T. McDermott

Bibliographic record

VenueThe Analyst · 2020
Typearticle
Languageen
FieldEngineering
TopicElectrochemical sensors and biosensors
Canadian institutionsAlberta Hospital EdmontonUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta InnovatesUniversity of Alberta
KeywordsGlassy carbonCarbon fibersEvaporationAdsorptionMaterials scienceAmorphous carbonElectron beam physical vapor depositionElectrodeElectron transferElectrochemistryAnalytical Chemistry (journal)FabricationNanotechnologyChemical engineeringThin filmAmorphous solidChemistryComposite materialCyclic voltammetryOrganic chemistryPhysical chemistry

Abstract

fetched live from OpenAlex

Carbon film electrodes can often be used without pretreatment, and their fabrication allows for flexibility in size and shape and for mass production. In this work, we are exploring layered structures comprised of thin films of carbon on gold (eC/Au) prepared by electron-beam evaporation. These extremely flat films are not pyrolyzed and are comprised of mainly amorphous carbon but still exhibit reasonable conductivity due to the underlying gold layer. eC/Au electrodes, without any pretreatment, yield similar heterogeneous electron-transfer rates for benchmark redox systems and significantly lower background current in comparison with polished glassy carbon. Interestingly, they show insignificant adsorption of quinones, which is uncommon for most carbon electrode materials. However, eC/Au is still prone to adsorption of airborne hydrocarbons when exposed to ambient air like most graphitic materials. With reproducibly fast electron transfer kinetics, low background current, negligible adsorption, and ultraflat surface, eC/Au films are a promising candidate for electrochemical and sensor applications.

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 score0.382
Threshold uncertainty score0.261

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 head0.012
GPT teacher head0.217
Teacher spread0.205 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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