Effect of citric acid and starch as emulsifier on phase formation and crystallite size of lanthanum oxide nanoparticles
Bibliographic record
Abstract
Lanthanum oxide nanoparticles were synthesized via thermal decomposition method of the lanthanum nitrate in the presence of citric acid or starch as emulsifier. The effects of emulsifier and calcination temperature were investigated on the phase transformation and particle size distribution of the products. La2O3 nanoparticles were synthesized by drying lanthanum precursor and emulsifier solution, followed by calcination process at 600 and 900°C, respectively. Products were characterized by Fourier Transform Infrared (FT‐IR) spectroscopy, X‐ray diffraction (XRD), thermal analysis (TG/DTA) and nitrogen adsorption method (porous characteristics). The morphology of the samples analyzed using scanning electron microscopy (SEM). Average crystallite size of the products was calculated by XRD data and average particle size was measured from the TEM micrographs. Lanthanum dioxycarbonate in different forms of the tetragonal and monoclinic is crystallized in the presence of citric acid and starch during the calcination at 600°C, respectively. The hexagonal structure, however, is detected as the only crystalline phase formed by calcination at 900°C.
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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".