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Record W2338498037 · doi:10.1149/ma2015-01/28/1650

(Invited) Imaging and Quantitative Chemical Mapping of PEM-FC Catalyst Layers By Scanning Transmission X-Ray Microscopy

2015· article· en· W2338498037 on OpenAlexaffabout
Adam P. Hitchcock

Bibliographic record

VenueECS Meeting Abstracts · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMachine Learning in Materials Science
Canadian institutionsMcMaster University
Fundersnot available
KeywordsProton exchange membrane fuel cellMaterials scienceMicroscopyCathodeElectrolyteTransmission electron microscopyNanotechnologyChemical engineeringElectrodeFuel cellsChemistryOpticsPhysics

Abstract

fetched live from OpenAlex

Low temperature, hydrogen-fueled, proton exchange membrane fuel cell (PEM-FC) based engines are being developed rapidly for near-term implementation in mass production, personal automobiles. Materials and process research aiming to further optimize these systems is focused on understanding and controlling various degradation processes (carbon corrosion, Pt migration, cold start), and reducing cost by reducing or eliminating Pt in the electro-catalyst, especially for the oxygen reduction reaction (ORR), and optimizing the nanoscale distribution of the cathode components. Soft X-ray scanning transmission X-ray microscopy (STXM) [1] is a powerful tool to study PEM-FC catalyst layers (membrane electrode assemblies, MEA). STXM provides spectroscopic identification and quantitative mapping of chemical components with 30 nm spatial resolution in both 2D projection and 3D spectro-tomography. For a given radiation dose, it provides much more chemical information than analytical transmission electron microscopy (TEM) and thus it has significant advantages for mapping ionomer in MEA cathodes [2, 3], which has proven to be a very challenging component to detect by TEM due to the high sensitivity of ionomer to radiation damage. This tutorial will describe the instrumentation, methodology and data analysis involved in applying STXM to PEM-FC catalyst layers, and illustrate its capabilities with results from recent studies [4, 5]. STXM performed on BL 10ID1 at the Canadian Light Source and on BL 5.3.2.2 at the Advanced Light Source. Research supported by AFCC, NSERC, Canada Research Chairs, and the Catalyst Research for Polymer Electrolyte Fuel Cells (CaRPE-FC) network. [1] A.P. Hitchcock, Soft X-ray Imaging and Spectromicroscopy in Handbook on Nanoscopy , eds.G. Van Tendeloo, D. Van Dyck and S. J. Pennycook 2012. (Wiley) [2] D. Susac, V. Berejnov, A.P. Hitchcock, and J. Stumper, ECS Transactions 41 (2011) 629. [3] D. Susac, V. Berejnov, A.P. Hitchcock and J. Stumper, ECS Transactions 50 (2012) 405 [4] A.P. Hitchcock, et al., J. Power Sources 266 (2014) 66 [5] V. Lee, et al. J. Power Sources 263 (2014) 163 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 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)0.003
metaresearch head score (Gemma)0.001
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 score0.077
Threshold uncertainty score0.936

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.019
GPT teacher head0.285
Teacher spread0.266 · 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".

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Citations0
Published2015
Admission routes2
Has abstractyes

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