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Record W3115316679 · doi:10.1149/ma2020-02372393mtgabs

Iron and Nitrogen-Doped Graphene As Cathode Catalyst for Anion Exchange Membrane Fuel Cell

2020· article· en· W3115316679 on OpenAlexaff
Elo Kibena‐Põldsepp, Roberta Sibul, Sander Ratso, Mati Kook, Moulay Tahar Sougrati, Maike Käärik, Maido Merisalu, Jaan Aruväli, Päärn Paiste, Alexey Treshchalov, Jaan Leis, Vambola Kisand, Väino Sammelselg, Steven Holdcroft, Frédéric Jaouen, Kaido Tammeveski

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsProton exchange membrane fuel cellGrapheneOxideCatalysisMaterials scienceChemical engineeringX-ray photoelectron spectroscopyDirect-ethanol fuel cellInorganic chemistryChemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Polymer electrolyte membrane fuel cells (FC) are devices in which electricity is produced via chemical reaction. Most often, hydrogen and oxygen are used as fuel and oxidizing agent in FC, respectively. Currently, proton-exchange membrane (PEM) fuel cells have already found applications for example in fuel cell electric vehicles, although the high Pt loading needed on the PEMFCs cathode for the oxygen reduction reaction (ORR) makes these devices expensive. Thus, anion-exchange membrane (AEM) FCs have been considered as an alternative technology to PEMFC especially in terms of the price, since Pt-free (e.g. metal-nitrogen/carbon) catalysts have shown promising ORR results in alkaline conditions. But not so many M-N/C materials have been tested in single-cell AEMFC test-systems because of the lack of commercially available AEM with high conductivity and stability as well as high performance anion exchange ionomers that has hold back the real era of AEMFCs.1 In recent years, great progress in the field of AEMs has been made.2 Thus, in this study, a simple synthesis procedure was used to prepare an active ORR catalyst based on Fe,N-graphene for AEMFC application.3 The synthesis of the catalyst involved the mixing of 1,10-phenanthroline, iron(II)acetate, polyvinylpyrrolidone and graphene (GRA) or graphene oxide (GO) followed by a high temperature pyrolysis. According to the different physico-chemical analyses including but not limited to X-ray photoelectron spectroscopy (XPS), 57Fe Mössbauer spectroscopy and inductively coupled plasma mass spectrometry (ICP-MS), the doping procedure was successful because both synthesized materials (Fe-N-GO and Fe-N-Gra) contained Fe and nitrogen moieties and showed better ORR performance than the undoped materials. Furthermore, the half-cell experiments conducted by using the rotating disc electrode (RDE) method revealed that Fe-N-Gra exhibited much higher ORR electrocatalytic activity in terms of onset potential and half-wave potential than Fe-N-GO in alkaline medium. This is attributed to the higher surface area, micro-/mesoporous nature and larger amount of Fe-Nx moieties present in Fe-N-Gra compared to Fe-N-GO, as shown by different physico-chemical methods. Almost half of the iron was confirmed to be in highly active Fe-Nx form by 57Fe Mössbauer spectroscopy (Fig. 1a). Based on these results, the Fe-N-Gra as ORR catalyst was further selected to apply this for AEMFC test using hexamethyl-p-terphenyl poly(benzimidazolium) (HMT-PMBI) anion-exchange membrane.4 The performance of the AEMFC single-test with Fe-N-Gra as cathode was very promising. Namely, the peak power density (P max) for Fe-N-Gra was 243 mW cm–2 (Fig. 1b),3 which is a quite good result compared to the ones published in the literature. References A. Sarapuu, E. Kibena-Põldsepp, M. Borghei, and K. Tammeveski, J. Mater. Chem. A, 6, 776 (2018). D. R. Dekel, J. Power Sources, 375, 158–169 (2018). R. Sibul, E. Kibena-Põldsepp, S. Ratso, M. Kook, M. T. Sougrati, M. Käärik, M. Merisalu, J. Aruväli, P. Paiste, A. Treshchalov, J. Leis, V. Kisand, V. Sammelselg, S. Holdcroft, F. Jaouen, and K. Tammeveski, ChemElectroChem, 7, 1739–1747 (2020). A. G. Wright, J. T. Fan, B. Britton, T. Weissbach, H. F. Lee, E. A. Kitching, T. J. Peckham, and S. Holdcroft, Energy Environ. Sci., 9, 2130–2142 (2016). 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.208
Teacher spread0.195 · 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".

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Citations0
Published2020
Admission routes1
Has abstractyes

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