One pot synthesis of nano Ag in calcium alginate beads and its catalytic application in <scp><i>p</i>‐Nitrophenol</scp> reduction with kinetic parameter estimation and model fitting
Bibliographic record
Abstract
Abstract In this work, one step process of synthesis of silver nanoparticles (Agnp) embedded in insitu formed calcium alginate (CA) beads is stated. CA, formed from the reaction between sodium alginate and calcium hydroxide, acts as reducing and stabilizing agent as well as support for nanoparticles. The reaction mechanism for the formation and stabilization of Agnp is proposed where the vicinal dihydroxy groups of alginate are assumed to act as the reducing agent for Ag+ to Ag°. Transmission electron microscopy (TEM), x‐ray diffraction (XRD), UV‐vis spectroscopy, field emission scanning electron microscopy (FESEM), and atomic absorption spectroscopy (AAS) were used to characterize the Agnp. The formation of spherical nanoparticles with average size range of 4‐5 nm was confirmed by TEM. Catalytic activity of this nano silver‐calcium alginate (Agnp‐CA) composite was evaluated in the reduction of p‐nitrophenol. Concentrations of sodium alginate, calcium hydroxide, and AgNO3 are found to be the parameters that critically affect the synthesis of Agnp. The efficacy of the catalyst is expressed on the basis of suitable reaction parameters. Both pseudo‐homogeneous and heterogeneous kinetic models are proposed for the reaction to find the best model and the Eley‐Riedel model is found to fit well with the experimental data. The novelty of this work is that the tandem process of CA bead formation, Agnp formation, and Agnp entrapment in CA have been transformed into a single‐step process. Moreover, elaborations of each step of the ionic mechanisms of Agnp formation and p‐NP reduction with Agnp and the establishment of a heterogeneous kinetic model for the reaction are reported for the first time here.
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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 | 0.001 |
| 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 | 0.001 | 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".