Effective Removal of Microplastic Particles from Wastewater Using Hydrophobic Bio-Substrates
Bibliographic record
Abstract
The rapid increase in soil and water pollution is primarily attributed to anthropogenic factors, notably the mismanagement of post-consumer plastics on a global scale. This exploratory research design evaluated the effectiveness of natural hydrophobic cattail (Typha Latifolia) fibres (CFs) as bio-adsorbents of microplastic particles (MPPs) from wastewater. The study investigates how the composition of the adsorption environment affects the adsorption rate. Straightforward batch adsorption tests were conducted to evaluate the “spontaneous” sorption of MPPs onto CFs. Five MPP materials (PVC, PP, LDPE, HDPE, and Nylon 6) were evaluated. Industrial wastewater (PW) and Type II Distilled Water (DW) were employed as adsorption environments. The batch test results show that CFs are effective in removing five MPP materials from DW and PW. However, a higher removal percentage of MPPs was observed 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 increase of 50%. These findings indicate that hydrophobic interactions drive the “spontaneous and instantaneous” adsorption process and that adjusting the adsorption environment can effectively enhance the MPP removal rate. This research highlights the significant role that bio-substrates can play in mitigating environmental pollution, serving as efficient, sustainable, non-toxic, biodegradable, 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) | 0.000 | 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 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".