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Record W3025315095 · doi:10.1149/ma2020-01381590mtgabs

(Invited) Non-PGM Electrocatalysts for PEM Fuel Cells: Origin of Their Instability

2020· article· en· W3025315095 on OpenAlexaff
Gaixia Zhang, Régis Chenitz, Marc Dubois, Jean‐Pol Dodelet, Shuhui Sun

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsCatalysisProton exchange membrane fuel cellAnodeCathodeRenewable energyMaterials scienceChemical engineeringChemistryElectrodeEngineeringElectrical engineeringOrganic chemistry

Abstract

fetched live from OpenAlex

Proton exchange membrane fuel cells (PEMFCs) have earned substantial commercial and government interests worldwide as highly promising renewable energy power sources for various applications (e.g., electric vehicles). However, a key contributor to their high cost remains the use of Platinum Group Metals (PGM) catalysts, especially at the cathode where the oxygen reduction reaction (ORR) is much more sluggish than the hydrogen oxidation reaction (HOR) occurring at the anode. The replacement of PGM catalysts by PGM-free catalysts at the cathode has so far been mainly hampered by the poor stability of the most active and performing PGM-free catalysts [1,2] that would otherwise become serious contenders to PGM catalysts. It is therefore important to determine the origin of the instability plaguing the PGM-free catalysts [3-8]. Based on our highly performing catalyst (Nature Commun., 2011, 2, 416) [2], INRS team has devoted, during recent years, much attention to the instability of this PGM-free catalyst [3-8]. This communication will present our very recent work on the catalyst stability which is summarized in two publications in Energy and Environmental Science [6, 8]. Our catalyst was obtained by ball milling ZIF-8, FeAc and 1,10 Phenanthroline, followed by two heat-treatments in Ar and in NH3. Via a systematic study of the instability behavior of the catalyst at different fuel cell potentials from 0.8 to 0.2 V at 80 °C and 25 °C, we discovered that the decay of the current density always involves the superposition of a fast and a slow exponential decay. With the combination of various characterization (e.g., BET, NAA and Mössbauer spectroscopy), we concluded that the fast exponential decay of the current density was the result of the specific demetalation of FeNx sites located in the micropores of the catalyst. Following this work, we explored the behavior of this catalyst before and after fluorination by F2 at room temperature. We discovered that all Fe-based catalytic sites were poisoned by reaction with F2, but not the ORR active CNx sites (and edge carbon sites) located at the surface of the catalyst carbonaceous support. Consequently, the instability behavior of fluorinated catalysts was found to be different from that of pristine catalyst, but similar to that of MOF_CNx_Ar+NH3 (a catalyst devoid of Fe, with only CNx sites and edge carbon sites). Further, the F2-poisoned catalysts can be partially reactivated under different heat-treatments. With a systematic study by fuel cell, BET, IR, TGA, XPS, XAS, as well as DFT and thermodynamic calculations, all the results demonstrate that the root-causes of instability of the FeN4 and CNx catalytic sites do not have the same origin. The two models (from INRS and Los Alamos) proposed so far to describe the instability curves of Fe-based catalysts will also be discussed. References 1. Lefèvre, E. Proietti, F. Jaouen and J. P. Dodelet, Science, 2009, 324, 71-74. 2. Proietti, F. Jaouen, M. Lefèvre, N. Larouche, J. Tian, J. Herranz and J. P.Dodelet, Nature Communications |2:416|DOI: 10.1038/ncomms1427. 3. Yang , N. Larouche , R. Chenitz , G. Zhang , M. Lefèvre and J. P. Dodelet , Electrochim. Acta, 2015, 159 , 184 -197. 4. Zhang, R. Chenitz, M. Lefèvre, S. Sun and J. P. Dodelet, Nano Energy, 2016, 29, 111-125. 5. P. Glibin, J. P. Dodelet, J. Electrochem. Soc., 2017, 164, F948-F957. 6. Chenitz, U. Kramm, M. Lefèvre, V. Glibin, G. Zhang, S. Sun and J.-P. Dodelet, Energy Environ. Sci. 2018, 11, 365-382. 7. P. Glibin, M. Cherif, F. Vidal, J. P. Dodelet, G. Zhang and S. Sun, J. Electrochem. Soc., 2019, 166, F3277-F3286. 8. Zhang, X. Yang, M. Dubois, M. Herraiz, R. Chenitz, M. Lefèvre,M. Cherif, F. Vidal, V. P. Glibin, S. Sun, J.-P. Dodelet, Energy Environ. Sci. 2019, 12, 3015-303. 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)0.001
metaresearch head score (Gemma)0.001
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 score0.016
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0160.008

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 head0.015
GPT teacher head0.213
Teacher spread0.198 · 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
Published2020
Admission routes1
Has abstractyes

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