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Record W4388573487 · doi:10.18280/i2m.220501

A Device for Measuring the Electrical Conductivity of Liquids Using Phase Sensitive Detection Technique

2023· article· en· W4388573487 on OpenAlexvenueno aff
Salim Kerai, Youcef Hamoudi

Bibliographic record

VenueInstrumentation Mesure Métrologie · 2023
Typearticle
Languageen
FieldEngineering
TopicThermography and Photoacoustic Techniques
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceElectrical resistivity and conductivityPhase (matter)ConductivityAnalytical Chemistry (journal)OptoelectronicsElectrical engineeringChromatographyPhysicsChemistryEngineering

Abstract

fetched live from OpenAlex

The quantification of electrical conductivity in fluids is integral to various applications, including water engineering, biomedical, and industrial sectors.This study introduces an innovative methodology harnessing phase-sensitive detection to assess conductance, thereby nullifying the coupling capacitances' interference between the probe cell's metallic electrodes.The devised electronic conditioning circuit incorporates a 1 kHz sinusoidal voltage source, an admittance-to-voltage converter, a lock-in amplifier, and a microcontroller/LCD interface.Calibrations were performed over two ranges, 1 mS/cm and 20 mS/cm, utilizing precise combinations of resistors, capacitors, and adjustable resistors.The experimental findings were juxtaposed with a standard commercial conductometer across various solutions -calibration solutions, electrolyte solutions (NaCl, KCl, CaCl2, MgSO4), and different water treatment room solutions at a hemodialysis center (Carbon filter, water softener, Reverse osmosis, Dialysate).The relative error in the measured conductivity was derived, with a maximum value of 1.45% noted.This error margin is inferior to those reported by many commercial conductivity meters, suggesting improved accuracy of our method.

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)0.001
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: Empirical
Teacher disagreement score0.315
Threshold uncertainty score0.427

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.000
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 head0.056
GPT teacher head0.324
Teacher spread0.269 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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