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Record W4405220995 · doi:10.1016/j.mex.2024.103096

Fabrication and optimization of a multielectrode microfluidic electrochemical flow cell for fast and dynamic detection of reaction products

2024· article· en· W4405220995 on OpenAlexafffund
Espen Vinge Fanavoll, David A. Harrington, Svein Sunde, Frode Seland

Bibliographic record

VenueMethodsX · 2024
Typearticle
Languageen
FieldEngineering
TopicMicrofluidic and Capillary Electrophoresis Applications
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaNorges Forskningsråd
KeywordsMicrofluidicsFabricationMultielectrode arrayNanotechnologyElectrochemistryFlow (mathematics)Materials scienceBiomedical engineeringProcess engineeringMicroelectrodeChemistryEngineeringElectrodeMedicineMathematics

Abstract

fetched live from OpenAlex

Construction and experimental validation of electrochemical cells with multiple electrodes in a microfluidic channel is described. Details of the fabrication of the electrodes and polydimethylsiloxane channel using soft lithography methods are given. Calibration of the collection efficiencies and transit times between electrodes validate the use of these cells for fast electrochemical detection of soluble species. Mass transit times between two electrodes down to 3 ms with a flow rate of 200 µL min -1 are demonstrated. We demonstrate that there is an upper limit in the electrode width to channel height ratio depending on the electrolyte conductivity. A recommendation for the maximum electrode width to channel height ratio is presented. The electrode width is recommended to not exceed four times the height of the channel in 0.1 M H 2 SO 4 . We also demonstrate operating strategies to minimize the impact of oxygen in air, optimization of stepping motor syringe pump parameters, electrolyte switching, and show how to deposit catalyst particles on a channel electrode. • Fabrication methods are given for all components of microfluidic flow cells with multiple electrodes • Conditions are given for improved operation, including geometry, pumping, and electrical parameters.

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: none
Teacher disagreement score0.710
Threshold uncertainty score0.434

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 head0.006
GPT teacher head0.231
Teacher spread0.225 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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