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Record W2560074123 · doi:10.1021/jp506862b

Electronic Structure of FeSe<sub>1–<i>x</i></sub>Te<sub><i>x</i></sub> Studied by X-ray Spectroscopy and Density Functional Theory

2014· article· en· W2560074123 on OpenAlexafffund
Israel Pérez, John A. McLeod, R. J. Green, R. Escamilla, V. Ortiz, A. Moewes

Bibliographic record

VenueThe Journal of Physical Chemistry C · 2014
Typearticle
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsUniversity of British ColumbiaUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y TecnologíaCanada Research Chairs
KeywordsSpectroscopyCondensed matter physicsDensity functional theoryPhysicsResonant inelastic X-ray scatteringState (computer science)SuperconductivitySpin (aerodynamics)ScatteringOrder (exchange)Electronic structureCrystallographyAtomic physicsInelastic scatteringInelastic neutron scatteringChemistryQuantum mechanics

Abstract

fetched live from OpenAlex

We study the electronic properties of the FeSe 1– x Te x system ( x = 0, 0.25, 0.5, 0.75, and 1) from the perspective of X-ray spectroscopy and density functional theory (DFT). The analysis performed on the density of states reveals marked differences in the distribution of the 5p states of Te for x > 0. We think that this finding can be associated with the fact that superconductivity is suppressed in FeTe. Moreover, using resonant inelastic X-ray scattering, we estimate the spin state of our system which can be correlated to the magnetic order. We find that the spin state of the FeSe 1– x Te x system fluctuates, as a function of x, between S = 0 and S = 2 with Fe in FeSe in the highest spin state. Finally, our DFT calculations nicely reproduce the X-ray emission spectra performed at the Fe L -edge (which probe the occupied states) and suggest that the FeSe 1– x Te x system can be considered at most as a moderately correlated system.

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 score0.006
Threshold uncertainty score0.871

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.006
GPT teacher head0.225
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

Citations2
Published2014
Admission routes2
Has abstractyes

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