Transition Metal and Nitrogen-Doped Mesoporous Carbons As Cathode Catalysts for Anion-Exchange Membrane Fuel Cells
Bibliographic record
Abstract
Energy is an essential part of our lives and causes significant impact on the environment. As hydrogen is clean, cost-efficient and easily producible, then it is believed to become an essential player in the energy field.1 The proton exchange membrane fuel cells fueled by hydrogen are already used in the transport sector, but their further commercialization is limited by the excess usage of precious metals. To ease the search for replacements, the harsh acidic environment can be changed to alkaline, thus giving opportunity to use non-precious metal catalysts.2 Especially for the cathodic oxygen reduction reaction (ORR), different transition metal-containing nitrogen-doped carbon-based materials have shown great promise.3 This gives an opportunity for the rise of anion exchange membrane fuel cells (AEMFCs) as future hydrogen-based energy devices.2,3 In this work, a novel catalyst support based on mesoporous carbon (MPC, from Pajarito Powder, LLC) is used to produce M−N−C type catalyst materials. Such mesoporous structure is useful for better mass transport in the catalyst layer during AEMFC operation. This MPC support is mixed with 1,10-phenanthroline as nitrogen source and transition metal acetates, followed by high-temperature pyrolysis at 800 °C in an inert atmosphere. Five M-N-C materials are prepared with the following transition metal combinations: Co, Fe, CoFe, CoMn, and FeMn. Several physico-chemical characterization methods (SEM-EDX, STEM, XPS, MP-AES, Raman spectroscopy, and N2 physisorption) are employed to study the materials. These indeed proved that all five catalyst materials have feasible mesoporous structure (pore diameters predominantly 7-8 and 25-35 nm) and the doping with transition metals (content ca. 1 wt%) and nitrogen (ca. 2.3 at%) has been a success. The electrochemical testing to study the ORR pathway and activity of these materials in alkaline media was done using the RRDE method. CoFe-N-MPC, Fe-N-MPC and FeMn-N-MPC catalysts showed similar and excellent electrocatalytic performance by obtaining half-wave potential of 0.9 V vs RHE. The lowest peroxide yield was obtained with Fe-N-MPC and FeMn-N-MPC. The latter two catalyst materials also showed very good performance as cathode catalysts in an H2/O2 AEMFC together with an HMT-PMBI4 membrane, obtaining peak power density of >470 mW cm–2. This indicates that the M-N-MPC materials are promising cathode catalysts for the AEMFC application. References Nazir, H.; Louis, C.; Jose, S.; Prakash, J.; Muthuswamy, N.; Buan, M.E.M.; Flox, C.; Chavan, S.; Shi, X.; Kauranen, P.; Kallio, T.; Maia, G.: Tammeveski, K.; Lymperopoulos, N.; Carcadea, E.; Veziroglu, E.; Iranzo, A.; Kannan, A.M. Is the H2 economy realizable in the foreseeable future? Part I: H2 production methods. J. Hydrogen Energy 2020, 45, 13777-13788, DOI: 10.1016/j.ijhydene.2020.03.092 Gottesfeld, S.; Dekel, D. R.; Page, M.; Bae, C.; Yan, Y. S.; Zelenay, P.; Kim, Y. S. Anion exchange membrane fuel cells: current status and remaining challenges. Power Sources 2018, 375, 170-184, DOI: 10.1016/j.jpowsour.2017.08.010 Sarapuu, A.; Kibena-Põldsepp, E.; Borghei, M.; Tammeveski, K. Electrocatalysis of oxygen reduction on heteroatom-doped nanocarbons and transition metal–nitrogen–carbon catalysts for alkaline membrane fuel cells. J. Mater. Chem. A 2018, 6, 776-804, DOI: 10.1039/C7TA08690C. Wright, A.G.; Fan, J.T.; Britton, B.; Weissbach, T.; Lee, H.F.; Kitching, E.A.; Peckham, T.J.; Holdcroft, S. Hexamethyl-p-terphenyl poly(benzimidazolium): a universal hydroxide-conducting polymer for energy conversion devices. Energy Environ. Sci., 2016, 9, 2130-2142, DOI: 10.1039/C6EE00656F.
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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) | 0.000 | 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
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".