A variable temperature Fe3+ electron paramagnetic resonance study of Sn1−xFexO2 (0.00⩽x⩽0.05)
Bibliographic record
Abstract
X -band (∼9.5GHz) electron paramagnetic resonance (EPR) studies of Fe3+ ions in Sn1−xFexO2 powders with 0.00⩽x⩽0.05 at various temperatures (5–300K) are reported. These samples are interesting to investigate as Fe doping (⩽5%) produces ferromagnetism in SnO2 [A. Punnooose et al., Phys. Rev. B 72, 054402 (2005)], making it a promising ferromagnetic semiconductor at room temperature. The EPR spectrum at 5K can be simulated reasonably well as the overlap of spectra due to seven magnetically inequivalent Fe3+ ions: four low-spin (S=1∕2) and three high-spin (S=5∕2) ions, characterized by different spin-Hamiltonian parameters, overlapped by three broad ferromagnetic resonance spectra. The three high-spin ions, situated substitutionally in the interior of nanodomains, are characterized by smaller zero-field splitting (ZFS) parameters D and E, so that all their energy levels are populated at 5K. On the other hand, the four low-spin ions are situated interstitially at the surfaces of nanodomains. They are characterized by much larger ZFS, so that only their lowest Kramers doublets are occupied at 5K. Based on this simulation, it is concluded that the observed spectra at different temperatures can be reproduced by changing appropriately the relative overlaps of the various paramagnetic and ferromagnetic characters, which remain present over the temperature range studied.
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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".