Nitrogen-Doped Carbide-Derived Carbon/Carbon Nanotube Composites As Cathode Catalysts for Anion Exchange Membrane Fuel Cell Application
Bibliographic record
Abstract
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
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 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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".