Phosphorylated cellulose/electrospun chitosan nanofibers media for removal of heavy metals from aqueous solutions
Bibliographic record
Abstract
Abstract Contamination of water resources by toxic heavy metals has significant impacts on environmental and human health. Their removal from aqueous media is essential to ensure water sustainability and to provide safe freshwater availability to population. Electrospun chitosan (CS) nonwoven mats are efficient at removing heavy metals from aqueous media. However, they suffer from low permeability and low‐mechanical strength. They are also unable to remove contaminants in a nonselective way. A bilayer sorbent media made of a porous phosphorylated cellulose substrate covered by electrospun CS nanofibers was developed to overcome those weaknesses. The hydrophilic composite shows good water permeability and mechanical strength with appropriate thermal and chemical characteristics. Adsorption tests with Cd(II) indicate that pseudo‐second order and Langmuir models best fitted experimental data, with a maximum adsorption capacity of 591 mg/g at 25°C. Adsorption with multielement samples containing Cr(VI), Cu(II), Cd(II), and Pb(II) also reveal their capability to remove them in a selective way. This mechanically resistant, hydrophilic, and permeable adsorbent media was able to capture both cationic and anionic metallic contaminants.
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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) | 0.001 | 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".