Polythiophene, Polypyrrole and Carbon Nanotube-Based Catalyst for Alkaline Membrane Fuel Cell Cathode
Bibliographic record
Abstract
The transformation from hydrocarbons to a cleaner and sustainable H2 economy is considered a favorable approach to meet the increasing energy demand. Among different H2 technologies, fuel cell technology is a viable option for environmentally clean energy conversion (1). Anion-exchange membrane fuel cell (AEMFC) is one of the suitable candidates to meet this demand as they could be more cost-effective than their widely used acidic counterpart, proton-exchange membrane fuel cell. For providing an affordable AEMFC, the non-precious metal catalyst (NPMC) for oxygen reduction reaction (ORR) at the cathode is needed (2). Co-doping (e.g. with N, S and Fe) of the nanocarbon material has been found to be more beneficial for the preparation of highly active ORR electrocatalysts compared to the application of just one dopant atom (e.g. N) (3). Therefore, we have prepared the polypyrrole (PPy), polythiophene (TPh), and multi-walled carbon nanotubes (MWCNT) based composite material that has been further pyrolysed and subjected to the acid treatment procedure to prepare the NPMC for the AEMFC cathode. Versatile optimization procedures were performed to finally obtain the nanocarbon material with the high ORR activity (A-PPy/PTh/MWCNT) (4). According to the scanning electron microscopy images, the composite catalyst exhibited mainly tubular morphology with PPy and PTh wrapped around the MWCNT. The physical characterization of A-PPy/PTh/MWCNT revealed the BET surface area of 379 m2 g−1 and micro-mesoporous tubular structure. The surface composition of the catalyst was found to contain S, C, N, O and Fe, while the latter originates from the FeCl3 polymerization catalyst. The ORR studies with A-PPy/PTh/MWCNT electrocatalyst in 0.1 M KOH showed the ORR half-wave potential of 0.85 V vs. RHE. According to the Koutecky-Levich analysis, the NPMC catalyzed a preferred 4-electron oxygen reduction pathway (4). A single-cell AEMFC experiment with A-PPy/PTh/MWCNT cathode catalyst showed the maximum power density (P max) of 284 mW cm−2, while the P max value of 328 mW cm−2 was recorded for commercial Pt/C in similar AEMFC conditions (Figure 1). The obtained data show that the three-component (PPy, PTh and MWCNT) composite prepared herein is a promising route for the development of AEMFC cathode catalyst (4). References A. Serov, I. V. Zenyuk, C. G. Arges and M. Chatenet, Journal of Power Sources, 375, 149 (2018). T.-W. Chen, P. Kalimuthu, P. Veerakumar, K.-C. Lin, S.-M. Chen, R. Ramachandran, V. Mariyappan and S. Chitra, Molecules, 27, 761 (2022). A. Sarapuu, E. Kibena-Põldsepp, M. Borghei and K. Tammeveski, Journal of Materials Chemistry A, 6, 776 (2018). A. Sokka, M. Mooste, M. Marandi, M. Käärik, J. Kozlova, A. Kikas, V. Kisand, A. Treshchalov, A. Tamm, J. Leis, S. Holdcroft and K. Tammeveski, ChemElectroChem, 9, e202200161 (2022). 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) | 0.000 | 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 | 0.001 |
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".