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Record W4412535925 · doi:10.1038/s43246-025-00881-5

Observation of two cascading screening processes in an iron-based superconductor

2025· article· en· W4412535925 on OpenAlexaff
Ming-Hua Chang, Steffen Backes, Dong-Hui Lu, N. Gauthier, Makoto Hashimoto, Guanyu Chen, Hai‐Hu Wen, Sung‐Kwan Mo, Zhi‐Xun Shen, Roser Valentí, Heike Pfau

Bibliographic record

VenueCommunications Materials · 2025
Typearticle
Languageen
FieldMaterials Science
TopicIron-based superconductors research
Canadian institutionsInstitut National de la Recherche Scientifique
FundersDivision of Materials Sciences and EngineeringSLAC National Accelerator LaboratoryBasic Energy SciencesOffice of ScienceDeutsche ForschungsgemeinschaftU.S. Department of Energy
KeywordsSuperconductivityMaterials scienceCondensed matter physicsNuclear engineeringPhysicsEngineering

Abstract

fetched live from OpenAlex

Understanding how renormalized quasiparticles emerge in strongly correlated electron materials provides a challenge for both experiment and theory. It has been predicted that distinctive spin and orbital screening mechanisms drive this process in multiorbital materials with strong Coulomb and Hund’s interactions. Here, we provide the experimental evidence of both mechanisms from angle-resolved photoemission spectroscopy on RbFe2As2. We observe that the emergence of low-energy Fe 3dxy quasiparticles below 90K coincides with spin screening. A second process changes the spectral weight at high energies up to room temperature. Supported by theoretical calculations we attribute it to orbital screening of Fe 3d atomic excitations. These two cascading screening processes drive the temperature evolution from a bad metal to a correlated Fermi liquid. Understanding the emergence of renormalized quasiparticles in strongly correlated electron materials remains a significant challenge. Here, the authors use angle-resolved photoemission spectroscopy, supported by theoretical calculations, to provide experimental indication of distinct successive spin and orbital screening mechanisms in RbFe2As2, clarifying their role in the transition from a bad metal to a correlated Fermi liquid.

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 score0.004

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 head0.124
GPT teacher head0.388
Teacher spread0.264 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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