Fully Integrated, Simple, and Low-Cost Electrochemical Sensor Array for in Situ Water Quality Monitoring
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Rapid, accurate and inexpensive monitoring of water quality parameters is indispensable for continued water safety, especially in resource-limited areas. Most conventional sensing systems either can only monitor one parameter at a time or lack user-friendly on-site monitoring capabilities. A fully integrated electrochemical sensor array is an excellent solution to this barrier. Electrochemical sensing methods involve transduction of water quality parameters where chemical interactions are converted to electrical signals. The challenge remains in designing low-cost, easy-to-use, and highly sensitive sensor array that can continuously monitor major water quality parameters such as pH, free chlorine, temperature along with emerging pharmaceutical contaminants, and heavy metal without the use of expensive laboratory-based techniques and trained personnel. Here, we overcame this challenge through realizing a fully integrated electrochemical sensing system that offers simultaneous monitoring of pH (57.5 mV/pH), free chlorine (186 nA/ppm), and temperature (16.9 mV/°C) and on-demand monitoring of acetaminophen and 17β-estradiol (<10 nM) and heavy metal (<10 ppb), bridging the technological gap between signal transduction, processing, wireless transmission, and smartphone interfacing. This was achieved by merging nanomaterials and carbon nanotube-based sensors fabricated on microscopic glass slides controlled by a custom-designed readout circuit, a potentiostat, and an Android app. The sensing system can be easily modified and programmed to integrate other sensors, a capability that can be exploited to monitor a range of water quality parameters. We demonstrate the integrated system for monitoring tap, swimming pool, and lake water. This system opens the possibility for a wide range of low-cost and ubiquitous environmental monitoring applications.
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.
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it