Removing micro- and nanoplastics (MNPs) from water via novel composite adsorbents: A review
Bibliographic record
Abstract
This review critically examines recent advances in the development of novel composite adsorbents specifically carbon-based, magnetic-based, and metal-organic-framework-based materials (MOFs), for the efficient removal of Micro- and nanoplastics (MNPs) from water. While these composites demonstrate superior adsorption performance compared to traditional adsorbents (e.g., chitosan, carbon nanotube, biochar, and granular activated carbon), challenges such as scalability, synthesis complexity, and environmental safety remain. Carbon-based composites offer high surface area, diverse functional groups, and enhanced adsorption capacity but face challenges with recovery. Magnetic composites facilitate easy separation and reuse but are hindered by synthesis complexity. MOFs provide tunable porosity and selectivity, yet their stability and cost require improvement. Thus, future research should prioritize the development of composite adsorbents that are stable, sustainable, and scalable for practical water treatment applications. Integrating experimental data with computational modeling to optimize adsorption processes, and tailoring surface functionalities for specific MNP types will further boost removal efficiency and selectivity. This strategic focus aims to advance water treatment technologies capable of efficiently and safely mitigating the growing challenge of MNP pollution across various aquatic environments. • Sources and pathways of MNPs in aquatic environments are summarized. • Key adsorption mechanisms involved in MNP removal in water is highlighted. • The advancement of carbon-, magnetic-, and MOF-based composite adsorbents for the removal of MNPs in water are reviewed. • Challenges, limitations, and future prospects of composite adsorbents are discussed.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".