EFFECT OF Mn-Ti IONS DOPING AND SINTERING TEMPERATURE ON PROPERTIES OF BARIUM HEXAFERRITE
Bibliographic record
Abstract
EFFECT OF Mn-Ti IONS DOPING AND SINTERING TEMPERATURE ON PROPERTIES OF BARIUM HEXAFERRITE . Mn-Ti doped (0, 0.1, 0.4, 0.5, and 0.6 mole%-barium hexaferrite powders have been prepared from BaCO 3 , Fe 2 O 3 , TiO 2 andMnO powder by mechanical alloying technique for 20 hours. The mixture powder were grinded and then dried at 100 °C for 24 hours, followed by calcined at 1,000 °C for 2 hours. The calcined powder was then crushed into 400 mesh (38 μm) in particle size. X-Ray Diffraction analysis was performed to determine the phase formed. The powder was mixed with 3 wt%Celuna WE -518 polymer, and compressed with applied force of 5 tons. The sintering process was done at temperatures of 1100 °C and 1150 °C for 2 hours. The microstructure of sintered samples was observed by Scanning Electron Microscope - Energy Dispersive X-Ray Spectroscopy (SEM-EDS). The magnetic properties and Reflection Loss (RL) was measured by permeagraph Magnet-Physik and Vector Network Analyzer (VNA), respectively. The results show that the remanance (Br) of samples are likely to decrease with increase in %mol of Mn-Ti and the optimum coercivity (HCJ) 4,42 kOe was achieved at 0,5 mole% Mn-Ti. The maximum reflection loss of -25,6 dB was obtained at 0,4mole%Mn-Ti with sintering temperature of 1100 °C for 2 hours. Accordingly, it can be potentially used for microwave absorption application.
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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".