Online monitoring and mass transfer modelling of the growth of Ni‐B nanoparticles in a reverse micelle system
Bibliographic record
Abstract
Abstract The growth of Ni‐B nanoparticles by chemical reduction of nickel acetate tetrahydrate (0.3M) using sodium borohydride (B/Ni = 2.5 molar ratio) in CTAB/n‐hexanol/water ternary reverse micelles was investigated. SEM studies showed that the synthesized nanoparticles within the reverse micelle range are more favourable in terms of microstructure and morphology. Nanoparticle growth has been controlled in the range of 0.83–5.59 nm · h−1 by the precise adjustment of mass fraction of surfactant in the oil phase (0.22–0.47 wt%) and the overall mass fraction of the aqueous phase (0.1–0.3 wt%) values. Particle growth was measured in situ using time resolved UV‐vis absorbance spectroscopy. In addition, a new correlation based on characteristic absorbance peaks of Ni‐B nanoparticles at wavelengths of 285 and 230 nm has been derived for this purpose. An accuracy of about 9 % on the nanoparticle average sizes with respect to sizes measured by dynamic light scattering (DLS) was found (R2 = 0.98). A diffusion controlled growth model involving effective diffusivity of reverse micelles (Deff − RM = 9.60 × 10−11 − 1.17 × 10−8 m2 · s−1) andripening parameter (K = 4.57 × 10−21 − 1.54 × 10−20 m2 · s−1) was developed to describe the growth behaviour of the nanoparticles. The experimental results were very close to the experimental results with slight errors (7–10 %). Finally, TEM micrographs of Ni‐B nanoparticles showed that particle growth as well as agglomeration can be effectively controlled by precise adjustment of each component in the reverse micelle technique.
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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 | 0.001 | 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".