Electrocatalysts Based on Cobalt- and Nitrogen-Doped Nanocarbon Composites for Oxygen Reduction Reaction and Anion Exchange Membrane Fuel Cells
Bibliographic record
Abstract
With an increasing energy demand, there is a need for renewable energy conversion devices. Among the possible options are low temperature polymer electrolyte fuel cells, in which electrocatalysts play vital roles, especially on the cathode, where the oxygen reduction reaction (ORR) occurs. The most efficient electrocatalysts for the ORR are based on noble metals, mainly platinum, which however have various disadvantages including their high price and scarcity. As a replacement, different transition metal-nitrogen-carbon (M−N−C) catalyst materials have shown great promise.1,2 Herein, a composite of nanocarbons, namely carbide-derived carbon/carbon nanotube (CDC/CNT), is employed as a support to produce the M−N−C type of catalysts. This composite offers a novel catalyst structure in which both micro- and mesopores are present that can be beneficial for the anion exchange membrane fuel cell (AEMFC) application. The doping was done via pyrolysis at 800 °C in the presence of a cobalt salt and a nitrogen precursor (either dicyandiamide, urea or melamine).3 The catalysts were characterized using different physico-chemical methods (e.g. SEM, TEM, XPS, XRD, Raman spectroscopy, and N2 adsorption), which proved the success of doping as well as the feasible micro- and mesoporous structure with defects present. Both the RDE and RRDE methods were employed for the electrochemical testing and in alkaline medium all three catalyst materials exhibited similar and good electrocatalytic activity for the ORR. The half-wave potential for ORR of Co-N-CDC/CNT catalysts was close to that of a Pt/C catalyst (Figure 1, left). The possible application of the Co-N-CDC/CNT material was tested out by employing it as a cathode catalyst in AEMFC. The Co-N-CDC/CNT catalyst together with a novel HMT-PMBI4 membrane exhibited excellent performance with peak power density of 577 mW cm–2, which was significantly higher than that obtained with a Pt/C cathode (Figure 1, right). This shows that the Co-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). G. Wu, A. Santandreu, W. Kellogg, S. Gupta, O. Ogoke, H. Zhang, H. L. Wang, and L. Dai, Nano Energy, 29, 83−110 (2016). J. Lilloja, E. Kibena-Põldsepp, A. Sarapuu, M. Kodali, Y. Chen, T. Asset, M. Käärik, M. Merisalu, P. Paiste, J. Aruväli, A. Treshchalov, M. Rähn, J. Leis, V. Sammelselg, S. Holdcroft, P. Atanassov, and K. Tammeveski, ACS Appl. Energy Mater., 2020, (DOI: 10.1021/acsaem.0c00381) 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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".