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Record W3116395510 · doi:10.1149/ma2020-02372390mtgabs

Nitrogen-Doped Carbide-Derived Carbon/Carbon Nanotube Composites As Cathode Catalysts for Anion Exchange Membrane Fuel Cell Application

2020· article· en· W3116395510 on OpenAlexaff
Jaana Lilloja, Elo Kibena‐Põldsepp, Ave Sarapuu, Arvo Kikas, Vambola Kisand, Maike Käärik, Maido Merisalu, Alexey Treshchalov, Jaan Leis, Väino Sammelselg, Qiliang Wei, Steven Holdcroft, Kaido Tammeveski

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCatalysisCarbon nanotubeMaterials scienceCarbon fibersChemical engineeringInorganic chemistryProton exchange membrane fuel cellMelamineElectrochemistryCarbide-derived carbonChemistryComposite numberComposite materialCarbon nanofiberOrganic chemistryElectrode

Abstract

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Due to the increasing energy consumption, there is a need for renewable energy production devices. Low temperature fuel cells are among the possible options, but their performance is limited by the sluggish kinetics of the electrochemical oxygen reduction reaction (ORR) at the cathode. The most efficient electrocatalysts for the ORR are based on expensive and scarce noble metals, mainly platinum. As more sustainable replacements, various non-precious metal or entirely metal-free catalysts could be used. The latter ones include heteroatom-doped nanocarbons.1,2 Herein, a composite of carbide-derived carbon and carbon nanotubes (CDC/CNT) is doped with nitrogen by pyrolyzing of a mixture containing CDC, CNT and a nitrogen precursor at 800 °C. A composite of carbons were chosen on the premise of obtaining a feasible structure with both micro- and mesopores, which should be beneficial in the anion exchange membrane fuel cell (AEMFC) application. Four different precursors of nitrogen (cyanamide, dicyandiamide, urea or melamine) were used and compared in order to find the most suitable one.3 For the physico-chemical characterization of the catalysts SEM, XPS, Raman spectroscopy and N2 adsorption studies were applied. These proved indeed that a novel carbon structure was formed and the doping with nitrogen was successful with all four precursors (≥3 at% of N). The electrocatalytic activity of the four N-doped catalyst materials was studied in 0.1 M KOH using RDE and RRDE methods. The four N-CDC/CNT catalyst materials exhibited virtually the same and good activity toward the ORR. In order to assess the possible application of the N-CDC/CNT material, it was employed as a cathode catalyst in AEMFC together with a HMT-PMBI4 membrane. The tests were conducted at different conditions and the best performance with peak power density of 310 mW cm–2 was obtained at 70 °C with 1 bar back pressure (Figure 1). This good performance shows that the metal-free N-CDC/CNT materials are promising cathode catalysts for the AEMFC application.3 References A. Sarapuu, E. Kibena-Põldsepp, M. Borghei, and K. Tammeveski, J. Mater. Chem. A, 6, 776-804 (2018). C.Z. Zhu, H. Li, S.F. Fu, D. Du, and Y.H. Lin, Chem. Soc. Rev., 45, 517-531 (2016) J. Lilloja, E. Kibena-Põldsepp, A. Sarapuu, A. Kikas, V. Kisand, M. Käärik, M. Merisalu, A. Treshchalov, M. Rähn, J. Leis, V. Sammelselg, Q. Wei, S. Holdcroft, and K. Tammeveski, Appl. Catal., B, 272, 119012 (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

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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 score0.001
Threshold uncertainty score0.002

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0010.000
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 head0.011
GPT teacher head0.208
Teacher spread0.196 · 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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Same venueECS Meeting AbstractsSame topicFuel Cells and Related MaterialsFrench-language works237,207