Ferroelectric Self‐Poling, Switching, and Monoclinic Domain Configuration in BiFeO<sub>3</sub> Thin Films
Bibliographic record
Abstract
Self‐poling of ferroelectric films, i.e., a preferred, uniform direction of the ferroelectric polarization in as‐grown samples is often observed yet poorly understood despite its importance for device applications. The multiferroic perovskite BiFeO3, which crystallizes in two distinct structural polymorphs depending on applied epitaxial strain, is well known to exhibit self‐poling. This study investigates the effect of self‐poling on the monoclinic domain configuration and the switching properties of the two polymorphs of BiFeO3 (R′ and T′) in thin films grown on LaAlO3 substrates with slightly different La0.3Sr0.7MnO3 buffer layers. This study shows that the polarization state formed during the growth acts as “imprint” on the polarization and that switching the polarization away from this self‐poled direction can only be done at the expense of the sample's monoclinic domain configuration. The observed reduction of the monoclinic domain size is largely reversible; hence, the domain size is restored when the polarization is switched back to its original orientation. This is a direct consequence of the growth taking place in the polar phase (below Tc). Switching the polarization away from the preferred configuration, in which defects and domain patterns synergistically minimize the system's energy, leads to a domain state with smaller (and more highly strained and distorted) monoclinic domains.
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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".