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Record W2049063224 · doi:10.1149/05801.0907ecst

Visualizing Bubble Flows in Electrolyzer GDLs Using Microfluidic Platforms

2013· article· en· W2049063224 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueECS Transactions · 2013
Typearticle
Languageen
FieldEngineering
TopicInnovative Microfluidic and Catalytic Techniques Innovation
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsBubblePorosityMaterials scienceProton exchange membrane fuel cellGaseous diffusionElectrolysisMultiphase flowElectrolyteNanotechnologyChemical engineeringFuel cellsComposite materialElectrodeMechanicsChemistryEngineeringPhysics

Abstract

fetched live from OpenAlex

In this study, microfluidic platforms were used to visualize air bubble transport in two-dimensional (2D) representations of gas diffusion layers (GDLs) to gain insight into how the geometrical features of the GDL impact multiphase flow in polymer electrolyte membrane (PEM) electrolyzers. Two dimensional porous networks were designed using volumetric pore space information, such as average porosity and average throat size obtained from micro-computed tomography (micro CT) visualizations. Microfluidic chips were fabricated to represent felt, paper, and sintered powder GDLs and used to simulate the transfer of oxygen bubbles generated at the catalyst layer, through the GDL towards the flow channels of a PEM electrolyzer. The results of this work indicate that microfluidic platforms for evaluating PEM electrolyzer GDLs is highly promising.

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.

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: none
Teacher disagreement score0.584
Threshold uncertainty score0.785

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.241
Teacher spread0.226 · 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