Ratiometric Sensing of Glyphosate in Water Using Dual Fluorescent Carbon Dots
Bibliographic record
Abstract
Glyphosate is a broad-spectrum pesticide used in crops and is found in many products used by industry and consumers. Unfortunately, glyphosate has been shown to have some toxicity toward many organisms found in our ecosystems and has been reported to have carcinogenic effects on humans. Hence, there is a need to develop novel nanosensors that are more sensitive and facile and permit rapid detection. Current optical-based assays are limited as they rely on changes in signal intensity, which can be affected by multiple factors in the sample. Herein, we report the development of a dual emissive carbon dot (CD) system that can be used to optically detect glyphosate pesticides in water at different pH levels. The fluorescent CDs emit blue and red fluorescence, which we exploit as a ratiometric self-referencing assay. We observe red fluorescence quenching with increasing concentrations of glyphosate in the solution, ascribed to the interaction of the glyphosate pesticide with the CD surface. The blue fluorescence remains unaffected and serves as a reference in this ratiometric approach. Using fluorescence quenching assays, a ratiometric response is observed in the ppm range with detection limits as low as 0.03 ppm. Our CDs can be used to detect other pesticides and contaminants in water, as cost-effective and simple environmental nanosensors.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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 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".