Innovating Ferro-sonication approach for extracting microplastics from wastewater
Bibliographic record
Abstract
For accurate and reliable analysis of microplastics (MPs) in wastewater (WW), it is imperative to comprehend the significance of pre-treating WW before analysis. The suspended solids (SS) in the matrix tend to adhere to the MPs during filtration, which interferes with the detection of the MPs. In this regard, the present study aims to develop and optimize a pretreatment method to improve the extraction efficiency of MPs from WW by reducing the SS. A combination of the Fenton reaction and ultrasonication, ferro-sonication (Fe-UlS), was proposed to digest and eliminate the SS from WW. This hybrid pretreatment, Fe-UlS, was optimized for ultrasonication amplitude, treatment time, and hydrogen peroxide dose using response surface methodology (RSM) with a Box-Behnken design, achieving a desirability of 0.984. The optimum conditions for the Fe-UlS, such as the (1:1) Fenton reagent ratio (0.05 M FeSO4: 30 % H2O2), ultrasonication amplitude (31 %), and total process time (30 min) were found to be statistically significant (p < 0.05). The developed method was then employed for the extraction of spiked polyethylene (PE), polypropylene (PP) and polyethylene terephthalate (PET) MPs in real WW and found efficient in removing 83 % of the TSS present in the primary influent were in 30 min at a temperature of 45 °C. Also, the method did not affect the physio-chemical characteristics of the MPs; however, the thermal analysis of PE and PP MPs showed a statistically significant decrease in the melting temperature, as proven by paired t-test analysis. Further, a non-targeted liquid chromatography-mass spectrometry (LC-MS) analysis proved that Fe-UlS is a stable process, as it did not cause any leaching of MPs under the optimum pretreatment conditions. Finally, Laser Direct-Infrared Imaging (LD-IR) analysis was conducted to validate the developed Fe-UlS pretreatment approach for MP analysis in real WW. About 3434 MPs were detected in 100 mL of WW primary influent, within the size range of 9 to 500 μm. This hybrid pretreatment approach not only streamlines WW sample processing but also reduces the required concentration of Fenton reagent and processing time, yielding accurate and reliable results for monitoring MPs in WW.
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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) | 0.000 | 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 | 0.001 | 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".