CE-UV/LIF Analysis of Organic Fluorescent Dyes for Detection of Nanoplastics in Water for Quality Control
Bibliographic record
Abstract
Nanoplastics are a type of plastic that forms because of the degradation of bulk plastics due to several natural factors, and they are everywhere around us. Nanoplastics are a global concern due to their diverse composition. Additionally, detecting and analyzing these particles in water samples is challenging, as they also often bind with organic pollutants. Current water treatment methods are ineffective against nanoplastics, and these plastics can take years to degrade completely. One of the promising methods to detect nanoplastics is using organic fluorescent dyes that can bind to the nanoplastics by interactions with their surfaces. Capillary electrophoresis with UV detection or laser-induced fluorescence are feasible techniques for this, especially laser-induced fluorescence, as it can give much more sensitivity and selectivity for the fluorescent dyes. The research aimed to validate capillary electrophoresis (CE) with UV spectrophotometer/laser-induced fluorescence detection (LIF) for the quantitative analysis of micro/nanoplastics in lake/ground/well/tap water samples using organic fluorescent dyes. CE was used with a blue laser, a photodetector, and a UV detector. A 50%/50% mixture of R6G & DCM (Rhody dye) was used as it showed the most promising results. It was found that the CE-UV/LIF method, especially CE-LIF, has shown potential for analyzing the nanoplastics contents of real-world water samples. Rhody dye mixtures showed good binding to the polystyrene nanoplastics, especially at lower concentrations. However, with the rise of cheminformatics and artificial intelligence-machine learning, fluorescent dye-based chemosensors will be better designed for future applications of CE-UV/LIF.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".