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Record W2897926992 · doi:10.1002/er.4260

Geometric pore surface area and fractal dimension of catalyzed electrodes in polymer electrolyte membrane fuel cells

2018· article· en· W2897926992 on OpenAlexafffund
Jian Zhao, Samaneh Shahgaldi, Adnan Ozden, Ibrahim Alaefour, Xianguo Li, Feridun Hamdullahpur

Bibliographic record

VenueInternational Journal of Energy Research · 2018
Typearticle
Languageen
FieldChemistry
TopicElectrochemical Analysis and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsFractal dimensionElectrolyteMaterials scienceFractalMembraneSpecific surface areaElectrodeBET theoryPorosityAccessible surface areaPolymerSurface (topology)GeometryComposite materialCatalysisChemistryMathematicsPhysical chemistryComputational chemistry

Abstract

fetched live from OpenAlex

Geometric pore surface area is a significant parameter for the description of the irregular, somewhat random, porous structure of the catalyzed electrodes in polymer electrolyte membrane (PEM) fuel cells; however, its value is sensitive to the experimental methods employed, which necessitates the measurements via different methods. In this study, the geometric surface area of the porous electrode is determined by two different methods: the method of standard porosimetry (MSP) and Brunauer-Emmett-Teller (BET). The theory of fractal dimension is employed to analyze the data obtained from the MSP, and the fractal surface area calculated using nitrogen molecules as the scale is compared with the BET surface area. The results indicate that the geometric pore surface area is a property of the porous electrode that depends greatly on the “scale” size (ie, molecule size of the working fluid)—a smaller scale yields a larger value of the surface area. The surface area determined by the BET is found to be about one order of magnitude larger than that obtained by the MSP. Thus, the fractal dimension theory based on MSP demonstrates a useful tool to determine the accessible pore surface area at different length scales.

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)0.001
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 score0.002
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.018
GPT teacher head0.313
Teacher spread0.294 · 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

Citations11
Published2018
Admission routes2
Has abstractyes

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