A Device for Measuring the Electrical Conductivity of Liquids Using Phase Sensitive Detection Technique
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".