Effective Removal of Microplastics Particles from Wastewater using Bio-Substrates
Bibliographic record
Abstract
The rapid increasing rate of soil and water bodies pollution is the main anthropogenic effect caused by the mismanagement of post-consumer plastics. This research evaluated the effectiveness of cattail (Typha Latifolia) fibers (CFs) as bio-adsorbents of microplastic particles (MPPs) from wastewater. The effect of the adsorption environment composition on the adsorption rate was investigated. Batch tests were conducted to evaluate the “spontaneous” adsorption of MPs onto CFs. Five MPPs materials (PVC, PP, LDPE, HDPE, and Nylon 6) were evaluated. An industrial wastewater (PW) and Type II Distilled Water (DW) were employed as adsorption environments. The batch tests results show that CFs are effective in removing MPPs from DW and PW. However, higher removal percentage of MPPs were obtained in PW, ranging from 89% to 100% for PVC, PP, LDPE, and HDPE; while the adsorption of Nylon 6 increased to 29.9%, a removal increased of 50%. These observations indicate that hydrophobic interactions drive the “spontaneous and instantaneous” adsorption process and that adjusting the adsorption environment effectively enhances the MPPs removal rate. This research demonstrates the important role that bio-substrates can play in reducing the environmental pollution as efficient, sustainable, low cost, and reliable adsorbents for the removal of MPPs from wastewaters.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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 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".