Chemical Pressure Induced Strain Control of Magnetic Anisotropy in the Simple Perovskite ϵ-Fe <sub>2</sub> O <sub>3</sub>
Bibliographic record
Abstract
ϵ-Fe 2 O 3 is an exceptional nanoscale ferrimagnet, distinguished by its high coercivity (μ 0 H c > 2 T) and multiferroic behavior. Realizing its potential in advanced technologies requires precise control of its structural, electronic, and magnetic properties. Here, we report La-doping-induced chemical pressure arising from the larger ionic radius of La 3+ substituting for Fe 3+ that systematically modified the local chemical environments. These dopant-driven lattice strains and modified local chemical environments perturbed exchange pathways, producing a non-monotonic variation in saturation magnetization depending on the specific lattice sites occupied by La 3+ . Magnetic hysteresis concurrently revealed a remarkable rise in coercivity from ∼0.2 T for undoped nanoparticles ( x = 0) to ∼2.3 T at x = 0.072, predominantly resulting from a strain-driven enhancement of magnetocrystalline anisotropy. These results established rare-earth substitution as an effective strategy to engineer strain-mediated changes in the nanomagnetism of ϵ-Fe 2 O 3, offering a practical route to tailor high-coercivity and magnetoelectric properties for device applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".