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Record W4414624064 · doi:10.1139/cjp-2025-0104

Tracking the progression of nonuniform deterioration in a lithium-ion battery by Bragg-edge imaging using the AISTANS compact accelerator-driven neutron source

2025· article· en· W4414624064 on OpenAlexvenueno aff
K. Kino, Brian E. O’Rourke, Takeshi Fujiwara

Bibliographic record

VenueCanadian Journal of Physics · 2025
Typearticle
Languageen
FieldPhysics and Astronomy
TopicNuclear Physics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsTracking (education)PixelNeutronBattery (electricity)Neutron imagingColumn (typography)Spectral line

Abstract

fetched live from OpenAlex

A commercial lithium-ion battery was studied nondestructively by neutron Bragg-edge imaging at the AISTANS compact accelerator-driven neutron source for the purpose of tracking the progression of nonuniform deterioration of the battery. Neutron transmission spectra with a pixel size of 5 × 5 mm 2 in a 55 × 50 mm 2 area on the battery were obtained. For the negative electrode, graphite, LiC 12 , and LiC 6 crystals were observed in the spectra. Column density (cm −2 ) images of these crystals at three different charge/discharge-cycle states (0, 92, and 200 cycles) were obtained. Obvious nonuniformity in the images were not recognized. However, slight shifts of average column densities of all pixels after 200 cycles, which might suggest power-storage degradation of the battery sample, were observed with the LiC 6 crystals.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.219
Threshold uncertainty score0.327

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 communication0.0000.000
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 head0.015
GPT teacher head0.273
Teacher spread0.258 · 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
Published2025
Admission routes1
Has abstractyes

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