MétaCan
Menu
Back to cohort
Record W7117761095 · doi:10.1021/acs.nanolett.5c04373

Chemical Pressure Induced Strain Control of Magnetic Anisotropy in the Simple Perovskite ϵ-Fe <sub>2</sub> O <sub>3</sub>

2025· article· en· W7117761095 on OpenAlexafffund
Subir Roy, Gurleen K. Uppal, Alberto Acosta, Rachel Nickel, Charles A. Roberts, J. van Lierop

Bibliographic record

VenueNano Letters · 2025
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsResearch ManitobaUniversity of Manitoba
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for InnovationUniversity of Manitoba
KeywordsCoercivityMagnetocrystalline anisotropyMultiferroicsIonic radiusAnisotropyMagnetic anisotropyHysteresisMagnetizationPerovskite (structure)

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.945

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.008
GPT teacher head0.227
Teacher spread0.219 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes2
Has abstractyes

Explore more

Same venueNano LettersSame topicMultiferroics and related materialsFrench-language works237,207