Scalable Bamboo Fiber/Microfibrillated Cellulose Foam via Solvent‐Exchange‐Assisted Ambient Drying for Highly Efficient Microplastics Capture
Bibliographic record
Abstract
Abstract The pervasive contamination of microplastics (MPs) in aquatic systems demands sustainable and high‐performance purification technologies. However, conventional methods face challenges of energy‐intensive fabrication, low flux, and secondary pollution. Here, a scalable strategy to fabricate bamboo fiber/microfibrillated cellulose (BF/MFC) foam through solvent‐exchange‐assisted ambient drying, circumventing high‐energy consumption drying and toxic crosslinkers, is proposed. The synergistic assembly of bamboo fibers and MFC via hydrogen bonding and electrostatic interactions constructs a hierarchical porous architecture with a positively charged surface, abundant active sites, and mechanical robustness. The optimized BF/MFC foam conforms to the standard pore‐blocking filtration model, achieving high filtration efficiency (99.4%) and flux (7257.4 L m −2 h −1 ), and high adsorption capacity (720.4 mg g −1 ) through synergistic interactions of physical interception, electrostatic attraction, and hydrogen bonding. This capture system also demonstrates excellent reusability and good purification ability for various plastics and actual water bodies. Furthermore, a viable concept is proposed for value‐added products through the efficient recycling of microplastics. The multiscale self‐densification assembly strategy establishes a sustainable and scalable framework for microplastic remediation in aquatic environments.
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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) | 0.001 | 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".