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Record W2602129352 · doi:10.3390/en10030409

Doping-Induced Isotopic Mg11B2 Bulk Superconductor for Fusion Application

2017· article· en· W2602129352 on OpenAlexfundno aff
Qi Cai, Qianying Guo, Yongchang Liu, Zongqing Ma, Huijun Li, Wenbin Qiu, Dipak Patel, Hyunseock Jie, Jung Ho Kim, Mehmet Somer, E. Yanmaz, A. Devred, Vladimir Luzin, Amanullah Fatehmulla, W.A. Farooq, Daniel Gajda, Yoshio Bando, Yusuke Yamauchi, Subrata Pradhan, Md. Shahriar A. Hossain

Bibliographic record

VenueEnergies · 2017
Typearticle
Languageen
FieldPhysics and Astronomy
TopicSuperconductivity in MgB2 and Alloys
Canadian institutionsnot available
FundersInstitute of Population and Public HealthBoard of Research in Nuclear SciencesDepartment of Industry, Innovation and Science, Australian GovernmentAustralian Research CouncilKing Saud UniversityAustralian Academy of ScienceAustralian Government
KeywordsSuperconductivityFusion powerBoronMaterials scienceDopingMagnetFusionNuclear engineeringSuperconducting magnetFlux pinningCondensed matter physicsCritical currentNuclear physicsOptoelectronicsPlasmaPhysics

Abstract

fetched live from OpenAlex

Superconducting wires are widely used for fabricating magnetic coils in fusion reactors. Superconducting magnet system represents a key determinant of the thermal efficiency and the construction/operating costs of such a reactor. In consideration of the stability of 11B against fast neutron irradiation and its lower induced radioactivation properties, MgB2 superconductor with 11B serving as the boron source is an alternative candidate for use in fusion reactors with a severe high neutron flux environment. In the present work, the glycine-doped Mg11B2 bulk superconductor was synthesized from isotopic 11B powder to enhance the high field properties. The critical current density was enhanced (103 A·cm−2 at 20 K and 5 T) over the entire field in contrast with the sample prepared from natural boron.

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 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 score0.143
Threshold uncertainty score0.539

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.031
GPT teacher head0.301
Teacher spread0.270 · 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

Citations9
Published2017
Admission routes1
Has abstractyes

Explore more

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