Preparation and functionalization of zinc oxide nanoparticles with polymer microgels for potential catalytic applications
Bibliographic record
Abstract
The preparation, functionalization and catalytic application of ZnO nanoparticles have been systematically carried out. In the first step, the virgin ZnO nanoparticles were prepared from Zn(CH3COO)2·2H2O solution in an alcoholic medium using the liquid precipitation approach. To form a new type of (zinc oxide)-(chitosan)-poly(N-isopropylacrylamide-acrylic acid), abbreviated as [ZnO-CS-P(NIPAAm-AA)], hybrid polymer microgels; ZnO particles were loaded to the chitosan based polymer microgels. Both the pure and hybrid ZnO materials, so obtained, were characterized by Fourier transform infrared spectroscopy (FTIR), scanning electron microscope (SEM), UV–visible spectroscopy and dynamic light scattering (DLS). For the hybridization purpose, the CS-P(NIPAAm-AA) polymeric gels were prepared by free-radical polymerization. Physicochemical behavior of these hybrid microgels was found responsive to external stimuli such as temperature and pH. In order to trace the hydrodynamic size of the ZnO, microgel, and ZnO-microgel hybrid system, DLS were also performed. From the overall results, the formation of ZnO nanoparticles with an average particle size of ≈ 35-42 nm having some irregularity in structure was confirmed. Likewise, SEM results show that pure CS-P(NIPAAm-AA) microgel particles are spheroid in shape with a smooth surface while the ZnO-CS-P(NIPAAm-AA) hybrid microgel particles look somewhat rough and having irregularity in their overall shape/structure. The fine particles of virgin ZnO and their hybrid with microgel was used for the catalytic reduction of 4-nitrophenol into 4-aminophenol and the results of both materials were compared. In addition, the kinetics of this catalytic reduction has also been summarized. The factors those influence the reduction rate of 4-nitrophenol in the presence of inorganic-organic hybrid materials have also been discussed and it was also found that the ZnO-microgel hybrid samples acted as a better catalytic agent compared to the ZnO or microgel alone.
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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".