Alginate cryogel beads for effectively aggregating nanoplastics for water remediation
Bibliographic record
Abstract
Nanoplastic (NP) pollution, consisting of particles smaller than 1 µm, poses a significant threat to both global ecosystems and human health. However, effective removal remains challenging due to their sub-micron size and low environmental concentrations. In this research, we discovered that an alginate cryogel can rapidly aggregate NPs (50–200 nm) into micrometer-sized clusters, enabling efficient removal via conventional membrane filtration. This cryogel-enhanced filtration achieved over 99% NP removal within 2 minutes. We attribute the observed aggregation to the combined effects of weakly bound alginate leaching upon cryogel rehydration and localized Ca²⁺ release from cryogels on driving NP aggregation. This mechanism enables effective NP removal across a pH range of 4–8 for both spherical and irregularly shaped NPs. While surface adsorption plays a role in NP removal, aggregation predominantly resulted into the effective filtration of NPs, with minimal influence from cryogel’s internal porosity. By leveraging NP aggregation into the microscale rather than relying on size-dependent direct filtration, this strategy presents a promising scalable solution for wastewater treatment and broader environmental applications. Nanoplastic pollution poses a significant threat to both global ecosystems and human health, but its effective remediation is challenging due to the size and low environmental concentrations of nanoplastics. Here, the authors show that an alginate cryogel can rapidly aggregate 50–200 nm particles into micrometer-sized clusters, enabling efficient removal through conventional membrane filtration due to a combination of weakly bound alginate leaching upon cryogel rehydration and localized Ca²⁺ release from the cryogel.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".