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Record W2618744850 · doi:10.1149/2.0231711jes

Considering Photon Scattering and Harmonics for Synchrotron X-ray Radiographic Imaging of Polymer Electrolyte Membrane Fuel Cells

2017· article· en· W2618744850 on OpenAlexaff
Nan Ge, Michael G. George, Jong‐Min Lee, Daniel Muirhead, Stéphane Chevalier, Rupak Banerjee, Hang Liu, Tomasz W. Wysokiński, George Belev, M. Adam Webb, Ning Zhu, Aimy Bazylak

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicAdvanced X-ray and CT Imaging
Canadian institutionsCanadian Light Source (Canada)University of Toronto
Fundersnot available
KeywordsSynchrotronElectrolyteX-rayMaterials scienceHarmonicAttenuation coefficientAnalytical Chemistry (journal)Intensity (physics)HarmonicsPolymerScatteringOpticsBeam (structure)Photon energyChemistryPhotonElectrodeComposite materialChromatographyPhysics

Abstract

fetched live from OpenAlex

We predicted the attenuated, undesired secondary scattered, and undesired harmonic components of measured X-ray intensities from synchrotron X-ray radiographic visualizations of liquid water in an operating polymer electrolyte membrane (PEM) fuel cell.The undesired secondary scattered component of the measured intensity increased as a function of the liquid water thickness (traversed by the X-ray beam).This increase in the secondary scattered component led to a decrease in the calibrated attenuation coefficient for liquid water, decreasing the accuracy of water quantification.We recommend calibrating the attenuated coefficient with a range of water thicknesses defined by the maximum expected water thickness present in the PEM fuel cell.The undesired harmonic component of the measured intensity also increased as a function of liquid water thickness, which led to a decrease in the accuracy of the measured liquid water thickness.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
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 science0.0010.000
Research integrity0.0010.000
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 head0.006
GPT teacher head0.214
Teacher spread0.209 · 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

Citations7
Published2017
Admission routes1
Has abstractyes

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