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Record W3126731474 · doi:10.1002/celc.202001574

Impact of Protein Fouling on the Charge Injection Capacity, Impedance, and Effective Electrode Area of Platinum Electrodes for Bionic Devices

2021· article· en· W3126731474 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueChemElectroChem · 2021
Typearticle
Languageen
FieldNeuroscience
TopicNeuroscience and Neural Engineering
Canadian institutionsDiscovery Centre
FundersAustralian Research CouncilAustralian Government
KeywordsElectrodeDielectric spectroscopyMaterials scienceFoulingPlatinumAdsorptionCyclic voltammetryElectrical impedanceElectrochemistryAnalytical Chemistry (journal)DiffusionChemistryChromatographyElectrical engineeringMembraneThermodynamicsBiochemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The impact of protein fouling on platinum electrodes was assessed by electrochemical methods. Protein fouling affected the electrode potential and charge transfer through the electrode‐solution interface. Adsorbed proteins partially blocked the electrode, with no charge passing through blocked regions. The electrochemical theory and methodology for investigating partially blocked electrodes is fully presented, applied to protein adsorption, and the implications for bionics applications are discussed. The partially blocked electrode had a reduced admittance, and increased impedance and polarization resistance consistent with a smaller effective electrode area. The charge storage capacity and charge injection capacity decreased after protein adsorption. The effective electrode area was assessed by impedance and cyclic voltammetry. The diffusion profile towards the partially blocked electrode was mixed between linear and radial diffusion.

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.

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 score0.003
Threshold uncertainty score0.595

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 studies0.0000.000
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 head0.030
GPT teacher head0.275
Teacher spread0.245 · 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