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Record W2801604307 · doi:10.1149/ma2018-01/30/1817

Full Characterization of an Operating Fuel Cell Using High Energy X-Rays

2018· article· en· W2801604307 on OpenAlexaff
Isaac Martens, Janne Pusa, María Valeria Blanco, Antonis Vamvakeros, Simon D. M. Jacques, H. Isérn, V. Honkimäki, Jakub Drnec

Bibliographic record

VenueECS Meeting Abstracts · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsProton exchange membrane fuel cellDurabilityMaterials scienceDissolutionCharacterization (materials science)Process engineeringAutomotive industryDegradation (telecommunications)DiffractionNuclear engineeringChemical engineeringFuel cellsNanotechnologyElectrical engineeringComposite materialOpticsPhysicsEngineeringThermodynamics

Abstract

fetched live from OpenAlex

The durability of proton exchange membrane fuel cells (PEMFC) is a major barrier to the commercialization of these systems for stationary and transportation power applications. The decrease in the performances is due to irreversible processes in the catalyst layers such as dissolution and contamination and reversible losses which are attributed to the evolution of the cell water distribution [1]. The mechanisms leading to the performance loss under PEMFC operation of individual components (catalyst, membrane, GDL) are well known, however, what is the connection between operating conditions and failure mechanism is still under debate. The degradation in real working conditions may be strongly accelerated and localized since all phenomena are highly inter-correlated and very dependent on the cell design and mode of operation. To provide a long-term stable cell operation, detailed knowledge about the underlying aging mechanisms and their correlation to fluid transport is essential. Here we show, for the first time, that it is possible to obtain simultaneously a space/time resolved information about the catalyst structure, water distribution and microstructural parameters in a running fuel cell using high energy X-ray diffraction techniques. The custom X-ray cell design (area of 5 cm2) allows us to reach current densities up to 4 A/cm2, therefore the performance of the most demanding application such as automotive fuel cells. We use advanced tomography imaging techniques coupled with the full Rietveld analysis of the diffraction (HE-XRD) patterns [2]. This allows us to distinguish (with micron resolution) different materials, their structure and to determine the water distribution as a function of the cell’s operating parameters (humidity, temperature, power output). For example the high resolution and large probed reciprocal space of HE-XRD patterns permit to determine the oxidation state of Pt cathode catalyst at different conditions. This information is then used to distinguish the potential distribution within the cell and to find the parts of the MEA where the Pt nanoparticles are either electrically disconnected from the carbon or they are not reachable by diffusing protons. Both phenomena would effectively lead to a lower performance of the cell. In another example we show that the parallel flowfield geometry of our cell and the defects in the GDL affect the water distribution within the membrane, catalyst layer and GDL. At 2 A/cm2 more water is generated close to the gas inlets of the cell and the defects in the GDL contain relatively less water. At lower current densities the water is much more homogeneously distributed within the MEA and the GDL defects do not play a major role in the water distribution. In conclusion we show that the high energy X-ray diffraction is an interesting non-invasive, relatively simple and robust technique which can be successfully used to study the fuel cell in operando conditions giving unprecedented insight into the working device. Ref.: [1] S. Cherevko et al, ChemElectroChem, 10, 2(2015), 1471–1478; [2] V. Rossi et al., Advanced Materials, 21(2009), 578-583. Fig. 1: (a) The top diagram shows the cross-section sketch of of the cathode side of fuel cell. The H 2 O, Pt and carbon cross-section maps obtained from the analysis of the HE-XRD patterns during the cell operation at 2 A/cm 2 are shown below. (b) The tomographic reconstructions obtained from HE-XRD measurements showing Pt distribution within catalyst layer for four different vertical positions and for three different electrochemical conditions. Figure 1

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)0.000
metaresearch head score (Gemma)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.001

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 head0.009
GPT teacher head0.198
Teacher spread0.189 · 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

Citations0
Published2018
Admission routes1
Has abstractyes

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Same venueECS Meeting Abstracts→Same topicFuel Cells and Related Materials→French-language works237,207→