Self‐Assembled Epitaxial Ferroelectric Oxide Nanospring with Super‐Scalability
Bibliographic record
Abstract
Abstract Oxide nanosprings have attracted many research interests because of their anticorrosion, high‐temperature tolerance, oxidation resistance, and enhanced‐mechanic‐response from unique helix structures, enabling various applications like nanomanipulators, nanomotors, nanoswitches, sensors, and energy harvesters. However, preparing oxide nanosprings is a challenge for their intrinsic lack of elasticity. Here, an approach for preparing self‐assembled, epitaxial, ferroelectric nanosprings with built‐in strain due to the lattice mismatch in freestanding La 0.7 Sr 0.3 MnO 3 /BaTiO 3 (LSMO/BTO) bilayer heterostructures is developed. It is found that these LSMO/BTO nanosprings can be extensively pulled or pushed up to their geometrical limits back and forth without breaking, exhibiting super‐scalability with full recovery capability. The phase‐field simulations reveal that the excellent scalability originates from the continuous ferroelastic domain structures, resulting from twisting under co‐existing axial and shear strains. In addition, the oxide heterostructural springs exhibit strong resilience due to the limited plastic deformation nature and the built‐in strain between the bilayers. This discovery provides an alternative way for preparing and operating functional oxide nanosprings that can be applied to various technologies.
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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".