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Record W4393862979 · doi:10.1039/d4cp00293h

Electronic structure and transport in the potential Luttinger liquids CsNb<sub>3</sub>Br<sub>7</sub>S and RbNb<sub>3</sub>Br<sub>7</sub>S

2024· article· en· W4393862979 on OpenAlexafffund
Fabian Grahlow, Fabian Strauß, Marcus Scheele, Markus Ströbele, Alberto Carta, Sophie F. Weber, Scott Kroeker, Carl P. Romao, Hans‐Jürgen Meyer

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2024
Typearticle
Languageen
FieldPhysics and Astronomy
TopicOptical properties and cooling technologies in crystalline materials
Canadian institutionsUniversity of Manitoba
FundersHORIZON EUROPE Marie Sklodowska-Curie ActionsNational Supercomputing Center, Korea Institute of Science and Technology InformationEuropean CommissionNatural Sciences and Engineering Research Council of CanadaEidgenössische Technische Hochschule ZürichDeutsche ForschungsgemeinschaftCanada Foundation for Innovation
KeywordsElectronic structurePhysicsLuttinger liquidMaterials scienceCondensed matter physicsChemistryQuantum mechanicsElectron

Abstract

fetched live from OpenAlex

) of the triangular niobium cluster, which is believed to induce an important electronic feature. Several types of studies are undertaken to obtain deeper insight into the understanding of this unusual material: the crystal structure, morphology and elastic properties are analysed, as well the (photo-)electrical properties and NMR relaxation. Electronic structure (DFT) calculations are performed in order to understand the electronic structure and transport in these compounds, and, based on the experimental and theoretical results, we propose that the electronic interactions along the Nb chains are sufficiently one-dimensional to give rise to Luttinger liquid (rather than Fermi liquid) behaviour of the metallic electrons.

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 score0.003
Threshold uncertainty score0.011

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.005
GPT teacher head0.209
Teacher spread0.204 · 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

Citations4
Published2024
Admission routes2
Has abstractyes

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