MétaCan
Menu
Back to cohort
Record W4285398133 · doi:10.1149/ma2022-01351418mtgabs

Fe- and Co-Containing Nitrogen-Doped Nanocarbon Catalysts from 5-Methylresorcinol for Anion Exchange Membrane Fuel Cells

2022· article· en· W4285398133 on OpenAlexaff
Kaarel Kisand, Ave Sarapuu, Dmytro Danilian, Arvo Kikas, Vambola Kisand, Mihkel Rähn, Alexey Treshchalov, Maike Käärik, Maido Merisalu, Päärn Paiste, Jaan Aruväli, Jaan Leis, Väino Sammelselg, Kaido Tammeveski, Steven Holdcroft

Bibliographic record

VenueECS Meeting Abstracts · 2022
Typearticle
Languageen
FieldEnergy
TopicElectrocatalysts for Energy Conversion
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsCarbonizationCatalysisMaterials scienceCarbon fibersPyrolysisChemical engineeringGrapheneInorganic chemistryElectrolyteNanotechnologyChemistryOrganic chemistryElectrodeComposite material

Abstract

fetched live from OpenAlex

Replacing fossil fuels with alternative energy sources has a growing scientific and societal interest because of the the environmental concerns related with their utilization. Low-temperature polymer electrolyte fuel cells have become a subject of intense research as the future cornerstone of renewable energy-based economy. Platinum nanoparticles supported on high-area carbon materials (Pt/C) are commonly used as the cathode catalyst in low-temperature fuel cells. However, the high cost of precious metal-based catalysts limits the commercial viability of such fuel cells. Considerable effort has been directed to design efficient and cheap electrocatalysts through the last decades and transition metal-containing nitrogen-doped carbon nanomaterials have shown the most promising results. A simple strategy to prepare these catalyst materials is high-temperature pyrolysis of organic carbon precursors in the presence of nitrogen and metal sources, where carbonization and N-doping occur simultaneously.1 In this work, highly active oxygen reduction reaction (ORR) electrocatalysts from 5-methylresorcinol, dicyandiamide (DCDA) and Fe and/or Co salts were prepared by a simple one-pot synthesis procedure.2 It has been proposed that DCDA polymerizes into graphitic carbon nitride at around 550 °C, which acts as a reactive template during carbonization of organic precursors, decomposing above 750 °C to produce the micro- and macropores necessary for efficient mass transfer and yielding N-doped graphene-like carbon structures.3 The SEM and TEM analysis revealed that the materials are composed of wrinkled carbon structures doped with nitrogen and metals, and also contain metal nanoparticles encapsulated in N-doped carbon layers. According to the 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 catalysts contain metal-coordinated N centers, while pyridinic nitrogen is the most abundant N species in all materials. Electrocatalytic activity for ORR in alkaline media was evaluated via the rotating disk electrode (RDE) method. The ratio of the precursors was optimized and bimetallic catalysts were found to be more active toward ORR than their monometallic counterparts. Acid treatment slightly increased the catalysts’ ORR activity. The acid-treated bimetallic catalyst (FeCoNC-at) showed exceptional ORR performance, comparable to that of commercial Pt/C (20 wt%) (Figure 1a). This can be attributed to the high surface metal and nitrogen contents, which at least partly are present as nitrogen-coordinated metal (M-Nx) centers, as confirmed by inhibition of ORR in the presence of cyanide ions. The catalyst 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 an anion exchange membrane fuel cell (AEMFC) with hexamethyl-p-terphenyl poly(benzimidazolium) (HMP-PMBI) membrane,4 where a high value of peak power density (P max= 415 mW cm–2) was achieved (Figure 1b).2 References Sarapuu, E. Kibena-Põldsepp, M. Borghei, K. Tammeveski, Electrocatalysis of oxygen reduction on heteroatom-doped nanocarbons and transition metal–nitrogen–carbon catalysts for alkaline membrane fuel cells, J. Mater. Chem. A 6, 776–804 (2018). Kisand, A. Sarapuu, D. Danilian, A. Kikas, V. Kisand, M. Rähn, A. Treshchalov, M. Käärik, M. Merisalu, P. Paiste, J. Aruväli, J. Leis, V. Sammelselg, S. Holdcroft, K. Tammeveski, Transition metal-containing nitrogen-doped nanocarbon catalysts derived from 5-methylresorcinol for anion exchange membrane fuel cell application, J. Colloid Interface Sci. 584, 263–274 (2021). -T. Ren, Z.-Y. Yuan, A universal route to N-coordinated metals anchored on porous carbon nanosheets for highly efficient oxygen electrochemistry, J. Mater. Chem. A 7, 13591–13601 (2019). G. Wright, J. Fan, B. Britton, T. Weissbach, H.-F. Lee, E. A. Kitching, T. J. Peckham, S. Holdcroft, Hexamethyl-p-terphenyl poly(benzimidazolium): a universal hydroxide-conducting polymer for energy conversion devices, 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: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.230
Teacher spread0.216 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueECS Meeting AbstractsSame topicElectrocatalysts for Energy ConversionFrench-language works237,207