Aerogels Fabricated from Wood-Derived Functional Cellulose Nanofibrils for Highly Efficient Separation of Microplastics
Bibliographic record
Abstract
Microplastics pollution in the aquatic environment has been considered as a particular concern for global ecosystems in recent years. Herein, bleached eucalyptus pulp was chemically modified by 2,2,6,6-tetramethylpiperidine-1-oxyl and 2,3-epoxypropyltrimethylammonium chloride to produce modified cellulose nanofibrils (CNFs). Then, porous CNF-based aerogels were prepared by the freeze-drying process and selected as a matrix filter for highly efficient separation of polystyrene microplastics (PSMPs). Results showed that the modified CNF aerogels could efficiently separate different types of PSMPs with filtration efficiencies of 100 and 75% for carboxylate-modified polystyrene (PS–COOH) and amine-modified polystyrene (PS–NH 2 ), respectively. Meanwhile, the hydrophilicity and porous structure of these materials endowed aerogels with high filtration flux. The excellent filtration performance of materials mainly relied on their microstructure and high surface charge, which could effectively capture and separate PSMPs from aqueous solution through the synergistic actions of physical entrapment, electrostatic interaction, and hydrogen bonding. In addition, the modified aerogels had good stability and reusability, and the filtration efficiency of PS–COOH remained at 100% after eight cycles. These findings provide a green and promising method for designing functional aerogel filters from sustainable resources to be used for applications in microplastics separation and water purification.
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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 | 0.000 | 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".