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Record W4242712715 · doi:10.1149/ma2017-02/32/1410

Quantitative Mapping of Ionomer in Catalyst Layers by Electron and X-ray Spectromicroscopy

2017· article· en· W4242712715 on OpenAlexaffabout
Lis G. A. Melo, Adam P. Hitchcock, Jasna Janković, Jürgen Stumper, Darija Susac, Viatcheslav Berejnov

Bibliographic record

VenueECS Meeting Abstracts · 2017
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)McMaster University
Fundersnot available
KeywordsIonomerScanning transmission electron microscopyTransmission electron microscopyMaterials scienceScanning electron microscopeAnalytical Chemistry (journal)ChemistryPolymerNanotechnologyComposite materialCopolymerOrganic chemistry

Abstract

fetched live from OpenAlex

The perfluorosulfonic acid (PFSA) ionomer commonly used in Polymer Electrolyte Membrane Fuel Cells (PEM-FC) consists of a hydrophobic tetrafluoroethylene backbone with side chains terminated with sulfonate groups responsible for proton conduction. Optimizing the spatial distribution of ionomer in the catalyst layer maximizes the catalyst that is accessible to protons and O2, reducing the Pt loading. Thus, the methods of quantifying the distributions of ionomer relative to catalyst are important. PFSA is prone to radiation damage [1], losing fluorine which makes it challenging to analyze quantitatively with electron [2] and x-ray spectro-microscopies [3]. Scanning Transmission X-ray Microscopy (STXM) is known to cause less damage in soft materials than analytical Transmission Electron Microscopy (TEM), with either core level Electron Energy Loss (EELS) [4] or Energy Dispersive X-ray Spectroscopy (EDS). We have applied Scanning TEM – EDS (STEM-EDS) and STXM at room temperature to the same microtomed sample of a PEM-FC catalyst layer. STEM-EDS was performed using an FEI Tecnai Osiris equipped with CHEM-STEM 4 area detectors. STXM was performed using soft X-ray STXMs at the Canadian Light Source (Saskatoon) and the Advanced Light Source (Berkeley). The extent of ionomer damage (F-loss) as a function of absorbed radiation dose in the cathode due to each imaging technique was measured from F Ka maps in STEM-EDS, and by F1s stack maps (OD694 eV - OD684 eV) in STXM. Figure 1 plots the extent of fluorine signal and thus component loss, normalized to the initial amount, for a range of electron and X-ray exposures. Both the electron and X-ray beams damage the ionomer at a similar rate. However, decent quality EDS maps at room temperature (Figure 1C) with sufficient counts for statistical analysis need ~30000 e-/nm2. At that exposure, approximately 70-80% of the original fluorine signal in the ionomer material is lost. On the other hand, high quality ionomer maps using STXM require less than 10 photons/nm2 (Figure 1A), which causes negligible fluorine loss. The ‘multi’ mode of TEM-EDS acquisition does provide significantly less damage at lower exposures (Figure 1B) but by the time adequate image quality is achieved (Figure 1C), the extent of fluorine loss is unacceptably high. Whether the ionomer damage is attributed to only fluorine loss (EDS), or ionomer component loss (STXM), or significant change of ionomer chemistry is still debatable. However, this study indicates that after applying “common” imaging practices for STXM and EDS, the remaining cathode areas are significantly different. STXM performed on BL 10ID1 at CLS and on BL 5.3.2.2 at ALS. Research supported by NSERC and the Catalyst Research for Polymer Electrolyte Fuel Cells (CaRPE-FC) network. S. Yakovlev, et.al., Membranes. 3(2013) 424–439 D.A. Cullen, et al., J. Electrochem. Soc. 161 (2014) F1111–F1117 D. Susac, et.al. STXM ECS Trans. 41 (2011) 629–635 J. Wang, et.al., J. Phys. Chem. B. 113 (2009) 1869–76. 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)0.000
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 score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.016
GPT teacher head0.289
Teacher spread0.273 · 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

Citations2
Published2017
Admission routes2
Has abstractyes

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