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Record W4416597416 · doi:10.1149/ma2025-02381782mtgabs

Impact of the Structural and Chemical Parameters of Carbon-Supported Pt(Ni)-Based Catalysts Towards Phosphoric Acid Poisoning for HT-PEMFC Application

2025· article· W4416597416 on OpenAlexaff
Axelle Baudy, Oliver Heinze, Marion Scohy, Marian Chatenet, Laëtitia Dubau

Bibliographic record

VenueECS Meeting Abstracts · 2025
Typearticle
Language
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsSafran Electronics (Canada)
Fundersnot available
KeywordsCatalysisElectrolytePhosphoric acidProton exchange membrane fuel cellElectrochemistryMembrane electrode assemblyCatalyst poisoningCyclic voltammetryElectrode

Abstract

fetched live from OpenAlex

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

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.007
GPT teacher head0.239
Teacher spread0.232 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

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Citations0
Published2025
Admission routes1
Has abstractyes

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