Low Frequency Noise in Electrochemical Sensors for Water Quality Monitoring
Bibliographic record
Abstract
In the past several decades, water pollution has increased drastically due to rapid industrialization and population growth.Water contamination with pharmaceuticals are becoming an emerging problem as even a very low concentration may pose risks to human health and aquatic lifeforms.Since the safe limit of some chemicals such as painkillers and hormones in drinking water are in the range of ppm, the requirements for low-level detection of pharmaceuticals in water are demanding.Therefore, the development of new water quality monitoring sensors with improved limit-of-detection and sensitivity are critical.State-ofthe-art water quality monitoring systems include sensors for pH, free chlorine and dissolved oxygen.As opposed to conventional techniques, many novel sensors are based on electrochemical redox reactions, which are described by the Nernst equation.However, one important practical problem in realizing a highly sensitive and wide dynamic range sensor is that few ppm changes of activity or concentration results in a signal of only several microvolts.This results in a poor signal-to-noise (SNR) ratio, making the signal indistinguishable from the low frequency noise (LFN).Therefore, characterization of the sensor performance is indispensable.In this paper, we present our approach to the fabrication and results on the noise and sensitivity of several sensors (acetaminophen and estrogen, pH, free chlorine and temperature) for water quality monitoring.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".