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Superconducting neutron transmission imaging for investigating a sequential change in phase separations of low-melting Wood’s metal

2023· article· en· W4385668292 on OpenAlexaff
The Dang Vu, Hiroaki Shishido, Kazuya Aizawa, Takayuki Oku, Kenichi Oikawa, Masahide Harada, Kenji Kojima, Shigeyuki Miyajima, Kazuhiko Soyama, Tomio Koyama, Mutsuo Hidaka, S. Suzuki, M. Tanaka, Masahiko Machida, Shuichi Kawamata, Takekazu Ishida

Bibliographic record

VenueJournal of Physics Conference Series · 2023
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsTRIUMF
Fundersnot available
KeywordsMaterials scienceNeutronNeutron imagingNeutron radiationAlloyPhase (matter)Beam (structure)OpticsNuclear physicsComposite materialPhysics

Abstract

fetched live from OpenAlex

Abstract We built a new cryostat system for a current-biased kinetic inductance detector (CB-KID) to be able to place a sample at room temperature for conducting the neutron transmission imaging experiments with pulsed neutrons. A Wood’s metal alloy sample (Bi 50wt.%, Pb 25wt.%, Sn 12.5wt.%, Cd 12.5wt.%) of melting temperature 75.2°C was used to check a practical test by observing fine microstructures under beam power of 812 kW at beamline BL10 of J-PARC (MLF). The Wood’s metal is composed of four phases, of which one is a Cd-rich needle-like phase of an average width of 25 µm and length of 5 mm. Since Cd is a strong neutron absorber, it is suitable for observing the fine mosaic structure contrastingly in neutron transmission imaging. After neutron-transmission imaging with the Wood’s metal in the initial state, we subsequently melted the Wood’s metal sample and solidified it again by slow cooling during the same beam time. We were successful in observing an impressive change in morphology of the phases by neutron-transmission imaging. Room-temperature sample imaging is very convenient for possible users, and is expected to be very useful for applying our CB-KID system to versatile different materials of interests.

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 score0.108
Threshold uncertainty score0.523

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 communication0.0000.001
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 head0.081
GPT teacher head0.352
Teacher spread0.271 · 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

Citations0
Published2023
Admission routes1
Has abstractyes

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