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Record W2288353490 · doi:10.1063/1.4884679

Surface inhomogeneities and the electronic phase separated states in thin films of La0.35Pr0.35Ca0.3MnO3

2014· article· en· W2288353490 on OpenAlexafffund
Jae‐Chun Jeon, H. S. Alagoz, J. Jung, K. H. Chow

Bibliographic record

VenueJournal of Applied Physics · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnetic and transport properties of perovskites and related materials
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePhase (matter)Condensed matter physicsHysteresisThin filmElectrical resistivity and conductivitySurface roughnessSurface finishMagnetic fieldComposite materialNanotechnologyChemistry

Abstract

fetched live from OpenAlex

We have studied the effects of film thickness and surface inhomogeneities on the fluid and static phase separated states of La0.35Pr0.35Ca0.3MnO3 films grown on LaAlO3 and SrTiO3 substrates which provide compressive and tensile strain, respectively. The shapes and areas of the resistance versus magnetic field hysteresis loops were used to identify the type of phase separated state in the films. Atomic force microscopy revealed an increase of the relative surface roughness of the films with a decreasing thickness. The resultant distribution of the strain produces a dramatic enhancement of the inhomogeneous electronic phase separation as well as large changes in the dependence of the resistivity on magnetic field in the thinnest films deposited on both types of substrates. In contrast to thick films, the static phase separated state in the thinnest films is more inhomogeneous than the fluid phase separated one.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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

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 head0.007
GPT teacher head0.220
Teacher spread0.214 · 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 source (direct Gemma or distilled Codex), 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

Citations8
Published2014
Admission routes2
Has abstractyes

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