MétaCan
Menu
Back to cohort
Record W2054307753 · doi:10.1088/2043-6262/6/1/015007

Superconductivity in the splat-cooled UMo alloys

2014· article· en· W2054307753 on OpenAlexfundno aff
N-T H Kim-Ngan, Sylwia Sowa, M. Krupska, M. Paukov, I. Tkach, L. Havela

Bibliographic record

VenueAdvances in Natural Sciences Nanoscience and Nanotechnology · 2014
Typearticle
Languageen
FieldMaterials Science
TopicNuclear Materials and Properties
Canadian institutionsnot available
FundersNarodowe Centrum NaukiInstitut Périmètre de physique théoriqueIndustry CanadaDeutscher Akademischer AustauschdienstGovernment of Canada
KeywordsSuperconductivityElectrical resistivity and conductivityCondensed matter physicsMaterials scienceCritical fieldDrop (telecommunication)DopingTransition temperatureMagnetic fieldPhase (matter)ChemistryPhysicsElectrical engineering

Abstract

fetched live from OpenAlex

We have investigated the superconductivity in splat-cooled UMo alloys by low-temperature resistivity and specific-heat measurements down to 0.4 K. The γ-U materials, such as U-Mo15 (with 15 at.% Mo doping), exhibit a conventional BCS superconductivity with Tc = 2.1 K and upper critical field exceeding 5 T, much higher than that for α-U materials. The alloys with <10 at.% Mo doping consist of a mixed γ + α-U phase. The superconducting transition in the U-Mo6 revealed by a smooth decrease below 1.5 K and a sharp drop at 0.6 K in the resistivity indicating that γ-U grains are embedded in the α-U matrix. The superconductivity transition was revealed by λ-type peak at Tc in the C(T) curve only for U-Mo15, while only one broad peak at Tc in the C(T) curves were observed for other UMo splats. With applying the magnetic fields, the resistivity jumps and specific-heat peaks move to lower temperatures.

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)0.003
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 score0.150
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
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 head0.010
GPT teacher head0.253
Teacher spread0.244 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueAdvances in Natural Sciences Nanoscience and NanotechnologySame topicNuclear Materials and PropertiesFrench-language works237,207