Utilizing Electrosorption for Efficient Removal of Polyethylene Microplastics from Water: Critical Factors and Mechanistic Insights
Bibliographic record
Abstract
Microplastics (MPs) produced by human activities can enter the environment through wastewater systems. A significant quantity of MPs still reaches the environment via wastewater treatment plant (WWTP) effluent because the techniques commonly used in WWTPs are not effective at removing MPs, especially smaller particles. To address this, an electrosorption (ES) method was developed in this study to separate MPs (3–5 µm polyethylene particles) from water using graphite felt electrodes. Electrosorption experiments were conducted using a static water cell and a flow-through cell to examine the influence of hydrodynamic forces. Increasing voltage (up to 12 V) enhanced electrostatic attraction, accelerating removal. Higher flow rates improved MP transport to the electrode, boosting efficiency. The highest removal (96.9%) occurred at 80 mL/min, 12 V, and 20 mM KNO3 after 150 minutes. By analyzing the influence of various parameters on MP removal efficiency and exploring the underlying mechanisms through DLVO theory, this study research establishes a foundation for future advancements in ES for MP removal. Future studies could focus on investigating the removal of MPs using ES in more complex, real-world environments.
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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 | 0.001 |
| 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 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".