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Record W2130351065 · doi:10.1109/jsen.2011.2170563

Electrode Potential-Based Coupled Coil Sensor for Remote pH Monitoring

2011· article· en· W2130351065 on OpenAlexaff
Sharmistha Bhadra, Greg E. Bridges, D. J. Thomson, Michael S. Freund

Bibliographic record

VenueIEEE Sensors Journal · 2011
Typearticle
Languageen
FieldChemical Engineering
TopicAnalytical Chemistry and Sensors
Canadian institutionsUniversity of Manitoba
Fundersnot available
KeywordsElectromagnetic coilCapacitanceResonatorElectrodeElectrical impedanceCapacitorMaterials scienceInductive sensorResponse timeOptoelectronicsPotentiometric sensorVoltageElectrical engineeringAnalytical Chemistry (journal)ChemistryComputer scienceEngineering

Abstract

fetched live from OpenAlex

We present a coupled coil pH sensor for high-resolution remote pH monitoring. The sensor is based on a passive LC coil resonator whose resonant frequency is monitored remotely by measuring the change in impedance of an interrogator coil coupled to the sensor coil. The sensor resonator consists of an inductive coil connected in parallel with a voltage dependent capacitor and a pH combination electrode. When the pH of the contact solution changes, the electrode potential changes the capacitance, and therefore the resonant frequency of the sensor. A linear response with a 0.1 pH resolution is achieved over a 2-12 pH dynamic range at room temperature. The response time of the sensor is demonstrated to be less than 30 s and is limited by the response time of the pH combination electrode. Effects of varying separation distance and temperature change on the sensor's performance are shown. The described sensor technology has potential application for remote pH monitoring in numerous fields such as biomedical sensing, environmental monitoring, industrial and chemical processing, and structural health monitoring.

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: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.030
GPT teacher head0.250
Teacher spread0.220 · 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
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

Citations22
Published2011
Admission routes1
Has abstractyes

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