Particle Size of Mechanically Alloyed La0.5Sr0.5Fe0.5Mn0.25Ti0.25O3 Powders Prepared with the Assistance of Ultrasonic Irradiation
Bibliographic record
Abstract
This paper reports the particle size characterisation of mechanically alloyed La0,5Sr0,5Fe0,5Mn0,25Ti0,25O3 prepared with the assistance of a high-power ultrasonic treatment. After a solid-state reaction on quasi-crystalline powders at 1000°C for 3 hour, the presence of a single phase was confirmed by X-ray Diffraction (XRD). It was found that powder materials derived from mechanical alloying and successive sintering have several disadvantages, namely, that the particle morphology is seldom controllable and results in large variations in the particle size and distribution. A significant improvement in both particle size and distribution was obtained upon subjecting the mechanically milled powder materials to an ultrasonication treatment for a relatively short period of time. As determined by a particle size analyser, the mean particle size gradually decreased from the original size of 6.23 to 1.13mm. A narrow size variation was also observed by Scanning Electron Microscope (SEM). The line broadening analysis by XRD revealed that the particles consist of nanocrystallites with an average size of ~ 22-26 nm. These results indicate that the sintering of mechanically milled particles followed by a high-power ultrasonication treatment promotes the formation of particles containing nanocrystals.
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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) | 0.001 | 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".