Electrospun nanofibers of chitosan/polyvinyl alcohol/UiO-66/nanodiamond: Versatile adsorbents for wastewater remediation and organic dye removal
Bibliographic record
Abstract
The appeal of metal–organic frameworks (MOFs) in wastewater treatment is tempered by their polycrystalline, powdery state, and challenges associated with their deployment. In the case of UiO-66, one of the most stable and widely-used MOFs, a low tendency for removing some organic contaminants has been observed on top of the mentioned issues. To address these challenges, herein, we take two complementary steps, i.e., hybridization of UiO-66 with organic nanodiamond (ND) followed by the integration of the hybrid nanoparticles in electrospun polymeric nanofibers based on chitosan/polyvinyl alcohol (PVA). We present the electrospinning of polymer/MOFs as a promising technique to fabricate highly efficient adsorbents for water remediation. We use the electrospun chitosan/PVA nanofibers (ECPN) as a versatile host for MOF nanoparticles that remove cationic methylene blue and anionic Congo red dyes. Four nanofiber composites containing thermally oxidized nanodiamond (TOND), ND, UiO-66, and [email protected] are utilized to unravel the effect of nanoparticles type and loading on dye adsorption capacity. It is shown that incorporation of a small loading of nanoparticles in ECPN significantly enhaces the maximum dye adsorption capacity. More importantly, the rationally engineered hybrid [email protected] nanoparticles exhibit the best performance in dye adsorption; for instance, an 80 % increase in maximum dye adsorption capacity, from 769 to 1429 mg/g, is recorded for ECPN loaded with [email protected] compared to the unfilled ECPN. On top of that, the designed adsorbent showed appreciable regeneration ability after 6 adsorption–desorption cycles. All in all, this study offers a new generation of engineered advanced materials to remove emerging contaminants from water streams.
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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".