A Low-Cost Sensor for Assessment of Aqueous Solutions Properties
Bibliographic record
Abstract
Monitoring of aqueous solution parameters such as pH and solute concentration is of utmost importance in various industries such as oceanic and agriculture. Herein, we report an innovative, low-cost interdigitated electrode (IDE) sensor built on a printed circuit board (PCB) for assessment of aqueous solutions properties through electrochemical impedance spectroscopy (EIS). We designed and fabricated two variants of the proposed sensor with two different electrode widths. The sensors were characterized in “dry” mode (with no electrolyte) using an equivalent electric circuit, and the associated model parameters were derived. In the “wet” mode, we used saline solutions with a concentration varying from 0.03% to 0.9%. Three different equivalent electrical circuit models were fit to the EIS data acquired using the sensors. Our observations showed that the series resistance of the electrochemical cell decreases as the solution concentration increases, which was used to calibrate the sensors. The results demonstrated that the developed sensors could reliably distinguish between samples with slight differences (0.03%) in their concentration. We also studied the effects of pH within the range of 4–7.6. From the models fit to the acquired EIS data, we extracted two appropriate circuit parameters to calibrate the sensor for pH measurement: one exhibits an almost linear response with an absolute sensitivity of$91.802~\Omega $/pH over the entire pH measurement range (relative sensitivity ranging from 56.35% at low pH to 70.36% at high pH), and the other demonstrates a nonlinear response with a relative sensitivity ranging from 248.4% at low pH to 471.96% at high pH.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".