Visual Sensing Platform for Quantitative Analysis of Water Contaminants
Bibliographic record
Abstract
There is a growing, unmet demand for real-time, frequent monitoring of water quality across various sectors, including drinking water, wastewater, agriculture, and aquaculture. To address this need, we developed a rapid, image-based water quality sensor that shifts traditional, time-consuming, and costly laboratory analysis to on-site detection of contaminants. In this first-of-its-kind study, we introduced a solid-phase fluorescence sensing platform for detecting chemical contaminants in water. The sensor integrates zinc oxide (ZnO) quantum dots (QDs) with molecularly imprinted polymers (MIPs) to create a fluorescence-responsive material. Upon UV activation, the fluorescence emission from the sensing layer is quenched in the presence of target contaminants, which absorb the excited-state energy of the emissive electrons. Contaminant concentrations were estimated by analyzing the color intensity of captured images using a portable fluorescence detector. The platform was tested with 2,4-dichlorophenoxyacetic acid (2,4-D) and the algal toxin Microcystin-LR (MCLR) as model contaminants, demonstrating high sensitivity and adaptability to different targets. We further investigated the sensor's working mechanism, identifying key parameters for expanding its use to a broader range of contaminants. Results confirmed the sensor's strong sensitivity and selectivity, highlighting its potential for developing portable, UV-LED-activated detection systems. The platform's high sensitivity, specificity, target flexibility, and user-friendly design lay the foundation for future lab-on-a-chip, image-based sensing technologies for environmental monitoring.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".