Synthesis, Characterization, and Second Harmonic Generation of Multiferroic Iron‐Doped Lithium Niobate Powders
Bibliographic record
Abstract
Abstract Random granular media can exhibit characteristics that are often related to ordered media. In the present work, this feature is observed in the polarized Second Harmonic Generation (SHG) response from reduced iron‐doped lithium niobate (LN:Fe) powders, which is an unexpected effect due to multiple scattering. In addition, the subsisting‐order properties of the powders can be further controlled by magnetic induction to tailor the SHG response. The samples are characterized by X‐ray Diffraction (XRD), X‐ray Photoelectron Spectroscopy (XPS), and confocal Raman Spectroscopy. The SHG response in the absence and presence of an external static magnetic field is then studied as the fundamental beam focus is translated from air into the powder. The SHG intensity polarization state is studied as a function of the linear polarization of the fundamental beam at the focus depth position, where the maximum SHG recorded intensity is observed. These results demonstrate that the SHG response of LN:Fe powders can be modified by post‐thermal treatment in a reducing atmosphere for photonic applications.
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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".