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Record W2519945466 · doi:10.1149/ma2016-02/38/2521

Look Insight Advances in Nano X-Ray Computed Tomography for Fuel Cells

2016· article· en· W2519945466 on OpenAlexaffabout
Tom Engl, Erik Kjeang

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceDegradation (telecommunications)CorrosionHydrogen fuelFossil fuelChemical engineeringEnvironmental scienceNanotechnologyProcess engineeringFuel cellsWaste managementComposite materialEngineeringElectrical engineering

Abstract

fetched live from OpenAlex

We have heard it over and over again – internal combustion engines (ICE) operated with fossil fuels can cause severe damage to our health and environment.1 Nevertheless, they are still very widespread and will not disappear until a compelling alternative is found. One possible alternative is the low temperature polymer electrolyte fuel cell (LT-PEFC). They are commonly operated with hydrogen and air and only emit water. However, before LT-PEFCs can replace ICEs on a large scale mainly two barriers need to be overcome. Those barriers are cost and lifetime. Hence, reducing the costs of a fuel cell and increasing its lifetime are the two most relevant research fields regarding fuel cell commercialization. Whereas the costs are mainly dictated by the raw material prices (e.g. platinum) and the manufacturing process the reasons for limited lifetime are numerous. PEFCs in general are affected by various degradation effects at different locations of the fuel cell.2,3 The membrane suffers for example from pinhole formation and membrane thinning. The hydrophobicity of the gas diffusion layer decreases over operation time which decreases its water transport ability. The bipolar plates are prone to surface corrosion which reduces their conductivity. The catalyst and catalyst layer also represent a major degradation area. Some specific degradation mechanisms are carbon corrosion, catalyst detachment and catalyst particle growth. All degradation effects are complex functions of the specific operational parameters, making it challenging to identify them individually. This in turn is necessary to gain insights into limitations in fuel cell lifetime and is the key to successful development of mitigation strategies to reach a desired lifetime of >5,000 hours for automotive drive cycle operation. There are different techniques available to identify and characterize the individual degradation mechanisms. Some common ones are polarization curve or electrochemical active surface area measurements which are based on electrochemistry. Furthermore, imaging techniques like secondary electron microscopy (SEM) are frequently used for degradation analysis. Another established imaging technique is X-ray tomography. The main advantages of X-ray tomography over SEM imaging are its ability to capture the fuel cell area of interest as a whole and that the tomography scan can be recorded nondestructively and in-operando.4 For some time, fuel cell X-ray tomography was mostly carried out at large scale synchrotron beamline facilities. Unfortunately, they tend to have only very limited access and therefore only few experiments can be conducted in a short time period. In recent development though, lab sized X-ray microscopes have gained interest in the community.5 Such microscopes have excellent availability and therefore enable the possibility to carry out long term experiments. The present contribution will utilize this particular advantage. Our unique approach is to retrace fuel cell degradation with X-ray tomography imaging (cf. Figure 1) over time and correlate it with individual failure mechanisms. In particular, electrode degradation will be discussed. MEAs, with e.g. different carbon/platinum ratios, were exposed to accelerated stress tests and imaged at beginning and end of life. With the aid of X-ray tomography, we are able to segment the MEA and subsequently locate and analyze the degradation effects at the electrode. In combination with electrochemical measurements, exclusive in depth insights of fuel cell degradation are provided. In addition, a novel approach for post X-ray tomography processing is discussed. In summary, the combined findings lead to an improved understanding of LT-PEFC degradation which is a necessary first step in order to investigate new mitigation strategies to increase the fuel cell lifetime. Figure 1. A membrane electrode assembly visualized by X-ray computed tomography recorded at beginning of life. 1. S. M. Platt et al., ‘Two-stroke scooters are a dominant source of air pollution in many cities’, Nat. Commun., 5, p. 3749, 2014. 2. J. Wu et al., ‘A review of PEM fuel cell durability: Degradation mechanisms and mitigation strategies’, J. Power Sources, 184, pp. 104–119, 2008. 3. T. Engl, L. Gubler, and T. J. Schmidt, ‘Think Different! Carbon Corrosion Mitigation Strategy in High Temperature PEFC: A Rapid Aging Study’, J. Electrochem. Soc., 162, pp. F291–F297, 2015. 4. J. Eller et al., ‘Progress in In Situ X-Ray Tomographic Microscopy of Liquid Water in Gas Diffusion Layers of PEFC, J. Electrochem. Soc., 158, B963 (2011). 5. M. Andisheh-Tadbir, F. P. Orfino, and E. Kjeang, ‘Three-dimensional phase segregation of micro-porous layers for fuel cells by nano-scale X-ray computed tomography’, J. Power Sources, 310, pp. 61–69 (2016) Funding for this research was provided by the Natural Sciences and Engineering Research Council of Canada, Canada Foundation for Innovation, British Columbia Knowledge Development Fund, and Ballard Power Systems through an Automotive Partnership Canada grant. Figure 1

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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 score0.017
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0170.004

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 head0.198
Teacher spread0.192 · 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
Published2016
Admission routes2
Has abstractyes

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