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Record W3114589188 · doi:10.1149/ma2020-02372361mtgabs

Transition Metal-Containing Nitrogen-Doped Nanocarbons Derived from 5-Methylresorcinol for Anion Exchange Membrane Fuel Cell Application

2020· article· en· W3114589188 on OpenAlexaff
Ave Sarapuu, Kaarel Kisand, Dmytro Danilian, Arvo Kikas, Vambola Kisand, Mihkel Rähn, Maike Käärik, Maido Merisalu, Päärn Paiste, Jaan Aruväli, Jaan Leis, Väino Sammelselg, Steven Holdcroft, Kaido Tammeveski

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsMaterials scienceCarbonizationPyrolysisCatalysisCarbon fibersChemical engineeringGrapheneTransition metalInorganic chemistryNanomaterialsElectrolyteNanoparticleElectrocatalystElectrochemistryNanotechnologyChemistryOrganic chemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

Development of novel energy conversion and storage technologies is highly relevant for the future renewable energy-based economy. Low-temperature polymer electrolyte fuel cells are clean and efficient devices, but their commercialization is hindered by high cost and scarcity of Pt-based cathode catalysts for the electrochemical oxygen reduction reaction (ORR). Among the various non-precious metal catalysts that have been intensively explored through the last decades, transition metal-containing nitrogen-doped carbon nanomaterials have shown the most promising results. One of the most common and simple strategies to prepare these catalysts is high-temperature pyrolysis of organic carbon precursors in the presence of nitrogen and metal sources, where carbonization and N-doping occur simultaneously.1 This work describes preparation of highly active ORR electrocatalysts from 5-methylresorcinol, Co and/or Fe salts and dicyandiamide (DCDA) via a simple one-step pyrolysis procedure at 800 °C. It has been proposed that DCDA forms graphitic carbon nitride below 600 °C, which acts as a reactive template during carbonization of organic precursors, decomposing at above 750 °C and yielding N-doped graphene-like carbon structures.2 The SEM and TEM analysis showed that the materials consist of wrinkled carbon structures doped with nitrogen and metals, and also contain metal nanoparticles encapsulated in N-doped carbon layers. According to XRD analysis, these nanoparticles are mainly composed of Co, Fe/Fe3O4 and FeCo alloy for the CoNC, FeNC and FeCoNC catalysts, respectively. The specific surface area of the materials was between 290 and 410 m2 g−1. The XPS analysis revealed that the pyridinic- N is the most abundant N species in all materials and the catalysts also contain metal-coordinated N centers. The electrocatalytic activity of the catalysts for ORR was evaluated in alkaline solution using the rotating disc electrode (RDE) method and the optimal ratio of the precursors was determined. The bimetallic catalysts were more active toward ORR than their monometallic counterparts. Treatment of the materials in acid solutions followed by a second pyrolysis slightly increased their ORR activity. The acid-treated bimetallic catalyst (FeCoNC-at) showed a remarkable ORR performance, comparable to that of commercial Pt/C (20 wt%), which can be attributed to the high surface metal and nitrogen contents (Figure 1a). This material also demonstrated a rather high stability in short-time tests (15000 potential cycles) and good tolerance to methanol. The FeCoNC-at catalyst was further tested in anion exchange membrane fuel cell (AEMFC) with hexamethyl-p-terphenyl poly(benzimidazolium) (HMP-PMBI)3 membrane, where a high value of peak power density (P max= 415 mW cm–2) was achieved (Figure 1b). References A. Sarapuu, E. Kibena-Põldsepp, M. Borghei, and K. Tammeveski, J. Mater. Chem. A, 6, 776-804 (2018). X.H. Li, S. Kurasch, U. Kaiser, and M. Antonietti, Angew. Chem. Int. Ed., 51, 9689-9692 (2012). 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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.015
GPT teacher head0.203
Teacher spread0.188 · 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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