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Record W2528503471 · doi:10.1021/acs.analchem.6b02804

Limits of Detection and Quantification of Electrochemical Quartz-Crystal Nanobalance in Platinum Electrochemistry and Electrocatalysis Research

2016· article· en· W2528503471 on OpenAlexafffund
Jutae Kim, Andrew Munro, Diane Beauchemin, Gregory Jerkiewicz

Bibliographic record

VenueAnalytical Chemistry · 2016
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of CanadaQueen's UniversityCanada Foundation for Innovation
KeywordsChemistryElectrochemistryDetection limitElectrocatalystCyclic voltammetryPlatinumElectrodeElectrolyteVoltammetryAnalytical Chemistry (journal)QuartzSupporting electrolyteCrystal (programming language)Aqueous solutionCatalysisInorganic chemistryChromatographyPhysical chemistryMaterials scienceOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

The electrochemical quartz-crystal nanobalance has been used in electrochemistry research for over three decades. It provides an atomic/molecular level insight into the nature of interfacial electrochemical phenomena by measuring in situ mass changes on the nanogram scale. The sensitivity of this technique remains unknown because there have been no attempts to determine its limits of detection (LOD) or quantification (LOQ). We propose an experimental approach for determining the values of LOD and LOQ for Pt electrodes in aqueous H 2 SO 4 solutions that employs cyclic voltammetry and frequency variation measurements. However, this methodology is also appropriate to other electrode materials and electrolytes. The LOD and LOQ values depend on the electrolyte concentration and decrease (i.e., the sensitivity increases) as the concentration decreases. Knowledge of the LOD and LOQ values determines the applicability of this technique in research on the oxidation and degradation of Pt catalysts employed in fuel cells.

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 score0.004
Threshold uncertainty score0.763

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.019
GPT teacher head0.292
Teacher spread0.273 · 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

Citations4
Published2016
Admission routes2
Has abstractyes

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