Nanoscale Size Effects on Push–Pull Fe–O Hybridization through the Multiferroic Transition of Perovskite ϵ-Fe<sub>2</sub>O<sub>3</sub>
Bibliographic record
Abstract
Multiferroics have tremendous potential to revolutionize logic and memory devices through new functionalities and energy efficiencies. To reach their optimal capabilities will require better understanding and enhancement of the ferroic orders and couplings. Herein, we use ϵ-Fe 2 O 3 as a model system with a simplifying single magnetic ion. Using 15, 20, and 30 nm nanoparticles, we identify that a modified and size-dependent Fe–O hybridization changes the spin–orbit coupling and strengthens it via longer octahedra chains. Fe–O hybridization is modified through the incommensurate phase, with a unique two-step rearrangement of the electronic environment through this transition with attraction and then repulsion of electrons around tetrahedral Fe. Interestingly, size effects disappear in the high-temperature phase where the strongest Fe–O hybridization occurs. By manipulating this hybridization, we tune and control the multiferroic 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) | 0.001 | 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".