Melanized-Cationic Cellulose Nanofiber Foams for Bioinspired Removal of Cationic Dyes
Bibliographic record
Abstract
In recent years, water pollution has developed into a severe environmental and public health problem due to rapid urbanization and industrialization, especially in some developing countries. Finding solutions to tackle water pollution is urgently required and is of global importance. Currently, a range of water treatment methods are available; however, a water remediation process that is simple, inexpensive, eco-friendly, and effective for the removal of pollutants down to ppm/ppb concentrations has long been sought after. Herein, we describe a novel approach using fungal melanin for developing melanized-cationic cellulose nanofiber (melanized-C-CNF) foams that can successfully remove pollutants in water systems. The foam can be recycled several times while retaining its adsorption/desorption property, indicating high practicability for adsorbing the cationic dye crystal violet. This work highlights the opportunity to combine both the advanced features of sustainable polymers such as cellulose and the unique properties of fungal melanin to manufacture biohybrid composites for 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) | 0.001 | 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".