Determination of polystyrene nanospheres and other nanoplastics in water via binding with organic dyes by capillary electrophoresis with laser-induced fluorescence detection
Bibliographic record
Abstract
The purpose of this research work was to develop a new method for the quantitative analysis of water samples containing nanoplastics in the presence of microplastics and other colloidal particles. Our approach involved a mixture of fluorescent organic dyes that was added to each water sample for binding with the target nanoplastics. Binding was proven by zeta potential measurements that revealed the point of zero charge shifting from pH 4 for polystyrene nanoparticles, to pH 6.13 after binding with the dye mixture. Centrifugation effectively separated the free dyes from all dye-bound particles in the heterogeneous mixture, thus eliminating any potential interference. Electrokinetic injection of the free dyes in the supernatant allowed efficient separation by capillary electrophoresis (CE), for accurate quantitation individually with laser-induced fluorescence detection. A diode laser was operated at λ ex of 450 nm to induce fluorescence from the dyes, and an optical interference filter to collect only emission photons with λ em of 520 nm. The fluorescence peak intensity decreased for each dye, thereby determining the total binding activity of all plastics and other particles. This new method enables high-throughput screening of water samples for nanoplastics, based on their fast binding with organic dyes in 5 min, rapid analytical separation of dyes by capillary electrophoresis within 10 min, and instantaneous fluorescence intensity measurement of individual dye peaks. Binding percentages as high as 149(±2) %/μg of 9.5-nm polystyrene nanoparticles were attained when using a concentration of 125 μg/mL for each dye. The binding mechanism was mainly attributed to hydrophobic interaction and modified by electrostatic forces. Binding of the four dyes with polystyrene microparticles, casein micelles, and transition metal oxide nanoparticles was verified to demonstrate minimal interference. The method was successfully applied to rapid testing of water samples from various sources, ranging from drinking fountains and household faucets to flowing rivers. The method also applied to a decontamination study wherein a removal of 94 % polystyrene nanospheres (diameter = 80 nm) was achieved by adding only 20 mg of casein powder into 1.6 mL of water containing 36 mg of the nanoplastics initially. • Selective binding of fluorescent organic dyes with polystyrene nanoparticles. • Capillary electrophoresis with laser fluorescence determination of % bindings. • Detection limit as low as 0.10 μg of 90-nm polystyrene nanoparticles. • Detection limit at 0.04 μg of 9.5-nm polystyrene nanoparticles. • Time requirements of 5 min for binding and 10 min for CE-LIF analysis.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.000 |
| 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".