Modelling the effect of water and zinc acetate concentrations on the size and morphology of <scp>ZnO</scp> nanoparticles obtained via the precipitation method
Bibliographic record
Abstract
Abstract In a previous study, a model was proposed to explore the thermodynamic equilibrium involved in forming zinc oxide nanoparticles at specific conditions via precipitation, using zinc acetate and potassium hydroxide as precursors. In this current study, those parameters derived from the model that are theoretically affecting the particle size itself, such as water, zinc acetate, and potassium hydroxide concentrations, have been altered. Using data extracted from the model—including [Zn +2 ] concentration and pH—the trajectories of each reaction were plotted to ascertain the sizes of stable particles in equilibrium throughout the reaction's progression. Zinc oxide nanoparticles were experimentally obtained by varying reactant concentrations to validate the simulation outcomes. The resulting zinc oxide underwent morphological and structural characterization using transmission microscopy (TEM) and X‐ray diffraction (DRX). A strong correlation was observed between the sizes predicted by the model and those observed in the micrographs, showcasing nanoparticles ranging between 15 and 40 nm. Increasing the water concentration from 1.5 to 12 M resulted in an increase in particle size from 15 to 30 nm. In contrast, there was no change in particle size due to the rise in zinc acetate concentration from 0.081 to 0.81 M. Furthermore, the rapid addition of KOH led to the production of smaller particles on the order of 3 nm, likely attributed to the reaction occurring away from equilibrium. Reactant concentrations also influenced morphology alterations, allowing for the formation of faceted spheres or rods under specific conditions.
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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) | 0.000 | 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".