Mn-inlaid antiphase boundaries in perovskite structure
Bibliographic record
Abstract
Improvements in the polarization of environmentally-friendly perovskite ferroelectrics have proved to be a challenging task in order to replace the toxic Pb-based counterparts. In contrast to common methods by complex chemical composition designs, we have formed Mn-inlaid antiphase boundaries in Mn-doped (K,Na)NbO3 thin films using pulsed laser deposition method. Here, we observed that mono- or bi-atomic layer of Mn has been identified to inlay along the antiphase boundaries to balance the charges originated from the deficiency of alkali ions and to induce the strain in the KNN films. Thus, rectangular saturated polarization-electric field hysteresis loops have been achieved, with a significantly improved twice remanent polarization of 114 μC/cm2 with an applied electric field of 606 kV/cm, which can be comparable to that of the typical Pb-based thin films. Moreover, we directly see the Mn occupation at the A-site of KNN perovskite structure using atomic-scale microstructure and composition analysis. The Mn-inlaid antiphase boundary can further enrich the understanding of perovskite crystal structure and give more possibilities for the design and optimization of perovskite materials. The volatilization of alkaline ions is a main factor to degrade the performances of KNN films. Here, the authors utilized the volatilization and incorporation of Mn to construct periodical Mn-inlaid antiphase boundaries in KNN matrix, leading to an improvement of ferroelectric properties.
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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".