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Record W4404088181 · doi:10.48550/arxiv.2410.14909

Electronic and magnetic properties of LaCu$_{x}$Sb$_{2}$ tuned by Cu occupancy

2024· preprint· en· W4404088181 on OpenAlexfundno aff
Suyoung Kim, Obinna P. Uzoh, Eundeok Mun

Bibliographic record

VenuearXiv (Cornell University) · 2024
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicRare-earth and actinide compounds
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsOccupancyMaterials scienceCondensed matter physicsPhysicsEngineering

Abstract

fetched live from OpenAlex

We report thermodynamic and transport properties of LaCu$_{x}$Sb$_{2}$ ($0.92 \leq x \leq 1.12$), synthesized by controlling the initial loading composition and investigated by magnetization, electrical resistivity, and specific heat measurements. The physical properties of this system are highly dependent on Cu-site occupancy $x$, where residual resistivity ratio (RRR), magnetoresistance (MR), superconducting transition temperature ($T_{c}$), and electronic specific heat coefficient ($γ$) indicate a systematic variation as a function of $x$. The Shubnikov-de Haas quantum oscillations are observed in magnetoresistance measurements for samples close to the Cu stoichiometry $x \sim 1$, while the de Haas-van Alphen oscillations are detected in a wide range of $x$ ($0.92 \leq x \le 1.12$). For $H \parallel c$, the oscillation frequency indicates a clear $x$-dependence, implying a systematic change of Fermi surface. DFT calculations for the sample closest to ideal Cu stoichiometry reveal electronic structures with a common feature of the square-net-based semimetals, which is in good agreement with the experimental observations. The magnetic response of LaCu$_{x}$Sb$_{2}$ to magnetic fields is anisotropic owing to the Fermi surface anisotropy. Our results show how the physical properties are influenced by the Cu-site occupancy $x$, linked to the electronic bands arising from the Sb square net.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.432
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

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 science0.0000.001
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 head0.028
GPT teacher head0.162
Teacher spread0.134 · 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 designTheoretical or conceptual
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

Citations0
Published2024
Admission routes1
Has abstractyes

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