Utilizing nature-based adsorbents for removal of microplastics and nanoplastics in controlled polluted aqueous systems: A systematic review of sources, properties, adsorption characteristics, and performance
Bibliographic record
Abstract
The pervasive distribution of microplastics and nanoplastics in water systems has raised significant concerns about their ecological and human health impacts. Traditional methods to mitigate plastic pollution are often inadequate, prompting the need for innovative and sustainable solutions. Agricultural waste or by-products (AWBP) are underutilized sources of adsorbents for environmental pollution control, particularly for microplastic and nanoplastic removal. Despite their low cost and high adsorption capacities, AWBP are frequently burned, dumped, or placed in landfills. Most importantly, there remains a notable gap in research, i.e., a systematic review of AWBP-based adsorbents for the removal of microplastics and nanoplastics, which is the novelty of this review. Therefore, using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) method, this study reviewed the use of hydrochar, biochar, and activated carbon (collectively termed nature-based adsorbents) for the removal of microplastics and nanoplastics, focusing on their sources, properties, adsorption characteristics, and performance. The principal findings and major conclusions indicate that these adsorbents can achieve up to 100 % adsorption efficiency for the removal of microplastics and nanoplastics. Environmental factors such as pH, temperature, and co-existing species can influence adsorption performance. Functional groups like -COOH, -OH, and -C O enhanced adsorption efficiency. Multiple mechanisms, including physisorption and chemisorption, contributed to high adsorption capacities. The pseudo-second-order kinetic model best described the adsorption processes, with the Langmuir isotherm providing the best fitting. Additionally, these adsorbents offer long-term carbon sequestration and align with several Sustainable Development Goals. This review highlights current knowledge gaps and provides recommendations for future research to further improve this technology. • Nature-based adsorbents achieve up to 100 % removal efficiency for microplastics and nanoplastics. • Adsorption performance varies with pH, temperature, functional groups, and competing ions. • The Langmuir isotherm best fits the adsorption process for microplastic and nanoplastic removal. • Adsorbent properties influence the removal efficiency of microplastics and nanoplastics in polluted aqueous systems. • Using nature-based adsorbents aligns with multiple Sustainable Development Goals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 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) | 0.000 | 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 teacher head, 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".