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Record W4398165774 · doi:10.1103/physrevb.109.174523

Magnetic and structural properties of the iron silicide superconductor LaFeSiH

2024· article· en· W4398165774 on OpenAlexfundno aff
Mads F. Hansen, Samar Layek, Jean‐Baptiste Vaney, L. Chaix, Matthew R. Suchomel, Mirko Mikolasek, Gastón Garbarino, A. I. Chumakov, R. Rüffer, Vivian Nassif, Thomas C. Hansen, Erik Elkaı̈m, Thomas Pelletier, H. Mayaffre, Fabio Bernardini, A. Sulpice, M. Núñez‐Regueiro, P. Rodière, A. Cano, Sophie Tencé, P. Toulemonde, M.-H. Julien, M. d’Astuto

Bibliographic record

VenuePhysical review. B./Physical review. B · 2024
Typearticle
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsnot available
FundersEnvironmental Studies Research FundsEuropean Synchrotron Radiation FacilityAgence Nationale de la Recherche
KeywordsSilicideSuperconductivityCondensed matter physicsMaterials scienceMetallurgyPhysics

Abstract

fetched live from OpenAlex

The magnetic and structural properties of the recently discovered pnictogen/chalcogen-free superconductor LaFeSiH have been investigated by $^{57}\mathrm{Fe}$ synchrotron M\"ossbauer source spectroscopy, X-ray and neutron powder diffraction, and $^{29}\mathrm{Si}$ nuclear magnetic resonance spectroscopy. In contrast with earlier work suggesting the presence of an orthorhombic and magnetic ground state as in underdoped Fe-based pnictides, our results unambiguously establish that LaFeSiH is in fact similar to strongly overdoped Fe-based pnictides: there is no magnetic order (including under hydrostatic pressure up to 18.8 GPa), nor even fluctuating local moments and the system remains tetragonal down to 2 K. This raises the prospect of enhancing the ${T}_{c}$ of LaFeSiH by reducing its carrier concentration through appropriate chemical substitutions.

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 score0.002

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 head0.029
GPT teacher head0.356
Teacher spread0.327 · 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 routes1
Has abstractyes

Explore more

Same venuePhysical review. B./Physical review. BSame topicIron-based superconductors researchFrench-language works237,207