Size‐controlled synthesis of Ni‐B nanoparticles by applying statistical experimental design in reverse micelles technique
Bibliographic record
Abstract
This work focuses on the size‐controlled preparation of Ni‐B nanoparticles by chemical reduction of nickel acetate with sodium borohydride in the ternary reverse micelles of cetyl trimethyl ammonium bromide (CTAB)/n‐hexanol/water. A response surface methodology (RSM) with four factors: CTAB/water (0.5–3.5 g/g); CTAB/n‐hexanol (0.2–0.8 g/g); B/Ni molar ratio (1–4); and nickel salt concentration (0.075–0.525 mol/L) was used to study the mean size of nanoparticles. Based on the central composite design (CCD), a total of 32 experimental tests were performed to correlate both reagent concentrations (Ni2+ and BH4−) and microemulsion compositions (surfactant/oil/water) to the size of to‐be‐obtained nanoparticles. The Ni‐B nanoparticles were obtained with a narrow size distribution of average diameter in the range of 4.5–30.6 nm. The quadratic model developed explained adequately the non‐linear nature of the modelled response (R2 = 0.97, precision = 33.19). Also the influence of effective variables on the mean size of Ni‐B nanoparticles was examined simultaneously and discussed theoretically by using 3D‐surface and 2D‐contour plots. Finally optimization of CCD based on desirability function was performed. The optimal conditions were found to be at the surfactant/oil/water mass ratio of 34/55/11, nickel salt concentration of 0.24 mol/L, and B/Ni molar ratio of 2.23 with desirability factor of 0.98. Moreover, validation of the optimization showed that the model predictions were very close to the experimental results with slight errors (7–10 %). Finally, TEM micrographs of Ni‐B nanoparticles at optimum conditions showed that by applying statistical experimental design in reverse micelle technique, not only particle size but also agglomeration could be effectively controlled.
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".