Effects of Fe-Doping on the Structural and Magnetic Properties of Indium Oxide Nanoparticles Synthesized by Bottom up Techniqu
Bibliographic record
Abstract
We study Fe doped In2O3 nanoparticles (NPs) from structural and magnetic point of view. X-ray diffraction (XRD) and transmission electron microscopy (TEM) reveal cubic bixbyite structure for both pure and Fe doped samples thereby confirming successful incorporation of Fe in host In2O3 lattice. Average crystallite size of pure and Fe doped (5% and 10%) In2O3 as calculated by Scherer’s formula shows slight increase from 21 nm for pure to 27 nm for the sample with 10% Fe content. The Williamson Hall (WH) method was also utilized to further determine crystallite size and Fe induced strain in In2O3 lattice. The crystallite sizes by WH plot are found to be 18 nm (for undoped), 22 nm (for 5% Fe) and 24 nm (for 10% Fe). These values are in good agreement with TEM results. Energy dispersive x-ray spectroscopy (EDX) indicates the existence of some oxygen vacancies in Fe doped In2O3 samples. Magnetic measurements show that all Fe doped In2O3 NPs exhibit typical ferromagnetic hysteresis loop with saturation magnetization Ms increasing with increasing Fe concentration. Temperature dependence of field cooled (FC) and zero field cooled (ZFC) magnetizations show no divergence and transition from ferromagnetism to paramagnetism in the temperature range of 5 to 300 K. This evidences a robust room-temperature-ferromagnetism (RTFM) in these NPs. The RTFM of our samples is attributed to the presence of oxygen vacancies in our samples.
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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".