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Record W2765834554 · doi:10.1038/s41598-017-12714-8

A comparative Raman study between PrMnO 3, NdMnO 3, TbMnO 3 and DyMnO 3

2017· article· en· W2765834554 on OpenAlexaff
S. Mansouri, S. Jandl, V. Yu. Ivanov, Anatoly M. Balbashov

Bibliographic record

VenueScientific Reports · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMultiferroics and related materials
Canadian institutionsRegroupement Québécois sur les Matériaux de PointeUniversité de Sherbrooke
Fundersnot available
KeywordsMultiferroicsRaman spectroscopyPhononCondensed matter physicsNuclear magnetic resonanceCoupling (piping)Field (mathematics)Magnetic fieldPhysicsMaterials scienceFerroelectricityQuantum mechanicsMathematics

Abstract

fetched live from OpenAlex

Abstract In this paper, we present a detailed Raman study of the non-multiferroic compounds PrMnO 3 and NdMnO 3 and the multiferroic compounds TbMnO 3 and DyMnO 3 as a function of temperature and magnetic field. All studied systems show anomalous phonon shifts close to the Néel transition T N . In PrMnO 3 and NdMnO 3 , the frequency softenings are partly attributed to an orbital-spin-phonon coupling whereas in TbMnO 3 and DyMnO 3 , the relatively weak frequency shifts are rather attributed to an expansion of the Mn − O bond lengths. On the other hand, the frequencies of TbMnO 3 phonons are shifted as a function of magnetic field, while those of PrMnO 3 remain unaffected. These frequency shifts are interpreted in terms of local oxygen rearrangements under magnetic field that could play an important role in the multiferroicity of TbMnO 3 and DyMnO 3 .

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 head0.064
GPT teacher head0.347
Teacher spread0.283 · 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

Citations23
Published2017
Admission routes1
Has abstractyes

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