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Record W3207258447 · doi:10.1016/j.elecom.2021.107141

Electrochemical applications of printed circuit boards: Electrocatalysis and internal reference electrodes

2021· article· en· W3207258447 on OpenAlexafffund
Tianyu Li

Bibliographic record

VenueElectrochemistry Communications · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaVictoria UniversityUniversity of Victoria
KeywordsMaterials scienceElectrodeElectrocatalystElectrochemistryPrinted circuit boardSubstrate (aquarium)Reference electrodeNanotechnologyPlatinumElectrochemical cellChemistryComputer scienceCatalysis

Abstract

fetched live from OpenAlex

As a chemically resistant hard substrate with surface-mounted metal pads, Printed Circuit Boards (PCBs) have the potential to be utilized as substrates for electrochemical applications such as electrochemical microfluidic devices. Compared with regular electrochemical substrates made with glass slides or filter papers, PCB-based substrates have electrode pads with customized size, shape and position, and more wiring layers. Previous works have shown that the PCB-based electrochemical devices can be used as biosensors, electrochemical impedance cytometers, and electrochemical imaging platforms, while their workability as a platform for electrocatalysis has not yet been presented. This work presents a method to develop PCBs into electrochemical substrates for alcohol electrocatalysis and internal reference electrodes. The copper pads were pretreated and electrodeposited with nickel (Ni), silver (Ag), palladium (Pd), and platinum (Pt) for various electrocatalytic purposes. To show the ability of making PCB-based electrochemical devices, two types of PCB-based reference electrodes were made.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.576
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.223
Teacher spread0.214 · 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.

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

Citations0
Published2021
Admission routes2
Has abstractyes

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