Polylactic acid/nano chitosan composite fibers and their morphological, physical characterization for the removal of cadmium(II) from water
Bibliographic record
Abstract
Abstract This work discusses the fabrication of polylactic acid (PLA)/nano chitosan (nCHS) composite fibers by electrospinning method for Cd2+ metal ion adsorption from water. Here nCHS was synthesized by ionic gelation method and which is used as a reinforcement for PLA. The scanning electron microscopic analysis revealed that the addition 0.1 wt% nCHS has decreased the fiber diameter as well as the secondary pore size and hence imparted unique properties to electrospun composite fibers. The positive zeta potential values for the composites indicated their higher stability, though; the inclusion of nCHS reduced the crystallinity of the neat membranes. The contact angle measurements showed that the hydrophilicity of the composite was increased up to 0.1 wt% nCHS, and hence the surface energy was increased. Inverse gas chromatography results suggested that the basic character of the composites has intensified with the increase in nCHS addition. The adsorption capacity of the neat electrospun PLA and PLA–nCHS composites for Cd2+ ions were investigated and studies revealed that adsorption capacity of the composite was two times faster (approximately 70%) in comparison with neat PLA fibers. The increase in surface area as well as presence nCHS improved the adsorption capacity of the electrospun membrane.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".