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Record W2608089509 · doi:10.1021/acs.analchem.7b00932

Comparing the Electrochemical Response of Nanostructured Electrode Arrays

2017· article· en· W2608089509 on OpenAlexafffund
Mahdieh Atighilorestani, Alexandre G. Brolo

Bibliographic record

VenueAnalytical Chemistry · 2017
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaWestern Canada Research GridCompute Canada
KeywordsElectrodeElectrochemistryChemistryCyclic voltammetryNanotechnologyNanosensorRedoxBiosensorNanostructureCurrent densityMicroelectrodeNanosphere lithographyAnalytical Chemistry (journal)OptoelectronicsMaterials scienceInorganic chemistry

Abstract

fetched live from OpenAlex

The electrochemical responses from periodic 6 × 6 arrays of recessed gold nanorings were compared to the 6 × 6 recessed gold nanodiscs arrays. The nanostructured arrays were fabricated by focused ion beam milling and their electrochemical response from a reversible redox pair was obtained. Three-dimensional cyclic voltammetry simulations using COMSOL were performed on 6 × 6 periodic arrays of both recessed nanodiscs and nanorings to elucidate the differences in mass transport between these geometries. Specific mass transport properties near the electroactive surface of the electrodes were elucidated by analyzing the calculated concentration profiles of the redox species. Relative contributions from radial diffusion regimes inside the nanoholes play an important role on the electrochemical response of the recessed nanorings. Arrays of nanodiscs are common in different types of applications, particularly in biosensors. The results presented here suggest that the performance and sensitivity of electrochemical nanosensors can be simply improved by implementing electrodes with a geometry which offer greater current density while keeping the overall footprint of the sensor element constant.

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 score0.024
Threshold uncertainty score0.709

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.264
Teacher spread0.250 · 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
Published2017
Admission routes2
Has abstractyes

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