Compressible cellulose nanofibril (CNF) based aerogels produced via a bio-inspired strategy for heavy metal ion and dye removal
Bibliographic record
Abstract
A sustainable nanomaterial, cellulose nanofibril (CNF) was used to prepare aerogel sorbents to remove various contaminants in wastewater. A mussel-inspired coating strategy was used to introduce polydopamine onto the surface of CNFs, which were cross-linked with polyethylenimine (PEI) to form the aerogels. The synthetic procedure was optimized to achieve a minimal consumption of raw materials to produce a robust porous structure. The aerogels possessed a low density (25.0 mg/cm3), high porosity (98.5%) and shape recovery in air and water. Adsorption studies were conducted on two representative contaminants, Cu (II) and methyl orange (MO). The kinetic data obeyed the pseudo 2nd order kinetic model and the mechanism of adsorption could be described by the intra-particle diffusion model. The Langmuir model fitting yielded a maximum adsorption capacity of 103.5 mg/g and 265.9 mg/g for Cu (II) and MO, respectively. The effects of pH on the adsorption performance were evaluated, confirming that the aerogels can maintain a high adsorption capacity over a wide pH range.
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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".