Impact of the Structural and Chemical Parameters of Carbon-Supported Pt(Ni)-Based Catalysts Towards Phosphoric Acid Poisoning for HT-PEMFC Application
Bibliographic record
Abstract
High-temperature proton exchange membrane fuel cells (HT-PEMFCs) are interesting alternatives to fossil fuel-based technologies for power generation. Their higher operating temperature compared to classic low temperature (LT) PEMFCs is highly beneficial regarding the total system complexity and weight, especially for applications which cannot involve bulky/heavy cooling systems, like aeronautics. However, at the current state-of-art of the technology, the membrane electrode assembly performance in HT-PEMFC does not reach that of LT-PEMFCs and needs to be improved; this requires better catalyst material, catalyst layer structure and membrane doping [1], [2]. A given catalyst performance is largely influenced by phosphoric acid electrolyte poisoning [3], [4] and some studies have already evaluated such poisoning level on various catalysts toward oxygen reduction reaction [5], [6]. The current study further addresses this issue and specifically aims to understand the impact of various parameters which should impact the catalyst activity in H 3 PO 4 electrolytes (loading, nano-particle size and shape, chemistry of the catalyst particles (Pt vs PtNi), density of aggregates, nature of the carbon support) and to evaluate the poisoning effect whether at low potential (anode) or at high potential (cathode). The effect of different parameters was unveiled thanks to a catalyst library (Figure a), analyzed comparatively in 1 M HClO 4 and 1 M H 3 PO 4 electrolytes at room temperature with a classic rotating disk electrode set-up and a gas diffusion electrode set-up. Pseudo CO-Stripping voltammetry, Hupd and CO-stripping voltammetry enable to shed light on the poisoning of the Pt surfaces: the electrochemical surface area (ECSA) is divided by a factor around 2 in H 3 PO 4 electrolyte compared to HClO 4 and varies according to the catalyst properties. The impact on hydrogen oxidation reaction is then noticeable. The poisoning at high potential (> 0.6 V vsRHE ) was mainly evaluated thanks to the ORR activity (Figure b): the Pt nanoparticle size/shape, their loading on the carbon support and alloying with Ni do impact their ORR activity. All the catalysts exhibit significantly lower activity in H 3 PO 4 than in HClO 4 (by a factor ca 10). The mechanisms of H 3 PO 4 -induced poisoning and potential strategies to mitigate it will be detailed. [1] S. S. Araya et al. , “A comprehensive review of PBI-based high temperature PEM fuel cells,” Int J Hydrogen Energy , vol. 41, no. 46, pp. 21310–21344, Dec. 2016, doi: 10.1016/j.ijhydene.2016.09.024. [2] R. E. Rosli et al. , “A review of high-temperature proton exchange membrane fuel cell (HT-PEMFC) system,” Int J Hydrogen Energy , vol. 42, no. 14, pp. 9293–9314, Apr. 2017, doi: 10.1016/j.ijhydene.2016.06.211. [3] B. F. Gomes et al. , “Effect of phosphoric acid purity on the electrochemically active surface area of Pt-based electrodes,” Journal of Electroanalytical Chemistry , vol. 918, Aug. 2022, doi: 10.1016/j.jelechem.2022.116450. [4] N. Sugishima et al ., “Phosphorous Acid Impurities in Phosphoric Acid Fuel Cell Electrolytes: I . Voltammetric Study of Impurity Formation,” J Electrochem Soc , vol. 141, no. 12, pp. 3325–3331, Dec. 1994, doi: 10.1149/1.2059334. [5] Q. He et al , “Influence of phosphate anion adsorption on the kinetics of oxygen electroreduction on low index Pt(hkl) single crystals,” Physical Chemistry Chemical Physics , vol. 12, no. 39, pp. 12544–12555, Oct. 2010, doi: 10.1039/c0cp00433b. [6] K. ‐L. Hsueh et al , “Effects of Phosphoric Acid Concentration on Oxygen Reduction Kinetics at Platinum,” J Electrochem Soc , vol. 131, no. 4, pp. 823–828, Apr. 1984, doi: 10.1149/1.2115707. Figure 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".