MétaCan
Menu
Back to cohort

Development of a Clark Microsensor for Low Concentration Dissolved Oxygen Monitoring

2020· article· en· W3038265090 on OpenAlexaff
Mehdi Nosrati, Daniela Vieira, Edward J. Harvey, Géraldine Merle, Sharmistha Bhadra

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsMcGill University
Fundersnot available
KeywordsElectrodeReference electrodeAuxiliary electrodeMaterials scienceOxygen sensorWorking electrodeMicrotechnologyAnalytical Chemistry (journal)Current (fluid)OxygenOptoelectronicsElectrochemistryChemistryElectrical engineeringNanotechnologyElectrolyteChromatography

Abstract

fetched live from OpenAlex

This paper reports on a geometrically optimized implantable three-electrode Clark microsensor for measuring dissolved oxygen concentration. The sensor is based on a conventional concentric three electrode structure. In order to maximize the current between the working and counter electrode, sensor structures with different reference electrode sizes and shapes are simulated. Results show that the highest current is achieved for a half crescent electrode with a surface area half of the conventional full crescent reference electrode. The optimized microsensor structure is fabricated using silicon-based microtechnology. Electrochemical measurements performed with the sensor show that the sensor responds to different concentration of dissolved oxygen (DO2). A measured current of 0.87 µA between working and counter electrodes is reported for 96% of DO2 concentrations. The sensor shows a sensitivity of −0.0094 μA/%DO2 and an accuracy of 1.98% DO2 in measurement. A measurable output current at 2% DO2 of the microsensor demonstrates that the microsensor can have potential for low DO2 concentration measurement.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: Methods · Consensus signal: Methods
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.001

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 head0.032
GPT teacher head0.253
Teacher spread0.222 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreMethods

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
Published2020
Admission routes1
Has abstractyes

Explore more

Same topicAnalytical Chemistry and SensorsFrench-language works237,207