MétaCan
Menu
Back to cohort
Record W2031162333 · doi:10.1103/physrevb.78.172405

Correlation of structural phase transition and electrical transport properties of manganite films on<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline"><mml:mrow><mml:msub><mml:mrow><mml:mtext>SrTiO</mml:mtext></mml:mrow><mml:mn>3</mml:mn></mml:msub></mml:mrow></mml:math>

2008· article· lv· W2031162333 on OpenAlexaff
M. Egilmez, M. M. Saber, I. Fan, K. H. Chow, J. Jung

Bibliographic record

VenuePhysical Review B · 2008
Typearticle
Languagelv
FieldMaterials Science
TopicMagnetic and transport properties of perovskites and related materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsManganiteTetragonal crystal systemCondensed matter physicsMaterials scienceElectrical resistivity and conductivityPhase transitionPhase (matter)Transition temperatureCoupling (piping)Temperature coefficientPhysicsFerromagnetismComposite materialSuperconductivityQuantum mechanics

Abstract

fetched live from OpenAlex

Magnetic phase transition in the manganite films has been found to be induced by the structural phase transition of the ${\text{SrTiO}}_{3}$ substrate at ${T}_{s}=105\text{ }\text{K}$ [Vlasko-Vlasov et al., Phys. Rev. Lett. 84, 2239 (2000) and Ziese et al., New J. Phys. 10, 063024 (2008)]. However, no change in the electrical transport properties has been detected at this temperature. Here we report the observation of the satellite peaks in the temperature dependence of the temperature coefficient of resistivity (TCR) at temperatures around 105 K in the ultrathin manganite films grown on ${\text{SrTiO}}_{3}$ substrates, triggered by the cubic-to-tetragonal structural phase transition in ${\text{SrTiO}}_{3}$. The TCR peak's magnitude decreases with an increasing thickness of the manganite film and with an increasing applied magnetic field. Our results demonstrate the strong coupling between the structural and transport properties in the manganite films.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.760
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.1270.000

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 head0.019
GPT teacher head0.244
Teacher spread0.225 · 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.

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

Citations31
Published2008
Admission routes1
Has abstractyes

Explore more

Same venuePhysical Review BSame topicMagnetic and transport properties of perovskites and related materialsFrench-language works237,207