Low-level salinity monitoring in drinking water using an electrochemical microfluidic sensor with an ion-selective polymer membrane
Bibliographic record
Abstract
Sensitive methods for detecting low salt levels (<100 ppm) in drinking water are limited. Current techniques lack specificity or are too costly and complex for point-of-need use. This study presents an innovative electrochemical sensor for precise monitoring of low-level salt ions in drinking water. It integrates an in-situ prepared, UV-curable standalone Ion-Selective Polymer (ISP) membrane within a Polymethyl methacrylate-based microfluidic chip. The ISP membrane containing carboxylic and amide functional groups selectively interacts with target ions. This interaction alters the electrochemical signal, which is measured using two electrodes prepared on either side of the ISP membrane. Chronoamperometry was used to assess the sensor's performance in the presence of different salt ions (i.e., NaCl, KCl, NaNO 3 , and KNO 3 ) from 0 to 800 ppm concentrations. The ISP membrane exhibited significantly higher selectivity towards Na + ions. When compared with a membrane-less sensor, the ISP membrane-integrated device showcased a 87 % improvement in the limit of detection from 3.39 to 0.45 ppm, an 91.6 % reduction in the limit of quantification from 5.84 to 0.49 ppm, and a 28.7-fold increase in sensitivity from 0.7 to 20.1 nA/ppm. Furthermore, the sensor was validated using diluted municipal tap water, achieving recoveries between 84.7 % and 105 % with RSDs below 10 %. The ISP membrane composition can be tailor-made for a wide range of biological and chemical contaminants while the microfluidic chip design is suitable for scalable manufacturing and integration into Point-of-Need testing platforms.
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.
How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".