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 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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".