An Enhanced Measurement for Inorganics in Water Based on a Novel Planar Three-Electrode Sensor
Bibliographic record
Abstract
Although conductivity is prevalently used in water quality detection for inorganic ions, its utility could be weakened when various kinds of ions are involved as it merely embodies water bulk resistance indiscriminately. Aiming at detectability enhancement, the article proposes a novel measurement method utilizing interfacial impedance for further exploration of ions. Based on theoretical analysis and measuring model comparison, a current-controlled method with difference measurement was derived, as well as the equivalent circuit. Then, a novel three-electrode sensor with a planar structure was accordingly designed and fabricated. After measurement parameters optimizing by experiments of frequency response and amplitude response, the sensor was tested with a traditional two-electrode sensor and conductivity sensor. Experimental results not only testified the performance of the proposed one in interfacial impedance measurement but also revealed a reduction process of interfacial impedance with increasing conductivity. The influence of water temperature was tested, too. Impedance differences between anion and cation inspired further experiments involving more ion species, which demonstrated that diverse positive ions trended to have similar relationships between conductivity and interfacial impedance while the relationships differed due to types of negative ions. The relationships make the measurement a promising tool for ion detection in certain applications.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 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".