Bamboo Fiber Paper-Based Filter Material for Fast and Efficient Capture of Microplastics
Bibliographic record
Abstract
Aiming at the hot issue of global microplastics (MPs) pollution, an ecofriendly paper-based filter from bamboo-derived cellulose was developed through mechanical processing and traditional papermaking formation technology. The paper-based filter exhibited a stable capture efficiency of 98% and exceptional filtration flux of 21167 L m –2 h –1 for amine-modified polystyrene (PS-NH 2, 5 μm). Remarkably, filtration kinetics followed the intermediate blocking model ( R 2 = 0.99), and the filter demonstrated excellent reusability, maintaining 99% efficiency after 10 cycles. Furthermore, it demonstrated remarkable universal adaptability, achieving >95% removal for polypropylene (PP), polyethylene (PE), and polyethylene terephthalate (PET), and also exhibited exceptional purification of three natural water samples. The superior capture performance stemmed from synergistic multiscale interactions between the microstructure and MPs, including physical interception, MP self-sedimentation effect, electrostatic interaction, hydrogen bonding, and π-π interactions. Life cycle assessment (LCA) confirmed a 48.8% reduction in global warming potential (GWP) unit energy consumption compared to conventional polymeric filters, with electricity and water consumption identified as primary environmental impacts via sensitivity and contribution analysis. Furthermore, a sustainable strategy was proposed to achieve MPs recovery and utilization. This work provides an efficient and environmentally friendly solution for microplastic remediation with significant potential for drinking water purification and MP separation in complex aquatic environments.
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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 | 0.000 | 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 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".