A Study on Effect of Ionomer Content on Catalyst Ink Property and PEM Water Electrolyzer Performance
Bibliographic record
Abstract
Producing hydrogen from water electrolysis with renewable electricity is essential for a carbon-free and environment-friendly economy. Proton exchange membrane (PEM) water electrolysis has advantages over other types of water electrolysis technologies with respect to compact system, high purity H 2 , high current density operation, better safety and reliability etc. Catalyst coated membrane (CCM) is the core of the membrane electrode assembly (MEA) and PEM water electrolyzer [1]. The CCMs are prepared by depositing catalyst inks onto the polymer electrolyte membrane. The composition of the catalyst inks plays an important role in determining the CCM and PEMWE performance. Catalyst ink is prepared from catalyst, ionomer and solvent. Commonly used is IrO x as catalyst, Nafion solution as ionomer (binder and proton conductivity path), and a mixture of organic solvent and water as solvent. One of the key parameters determining the CCM performance is the ionomer content in the catalyst layer. A wide range of ionomer content was reported in the literature, ranging from 2 to 30wt%. Xu et al. reported an optimal value of 25 wt% ionomer content using Ru 0.7 Ir 0.3 O 2 [1]. The same Nafion content was used by Su et al. with IrO 2 [2]. Bernt and Gasteiger found 11.6 wt% ionomer content showed the best performance with IrO 2 /TiO 2 [3]. Ma et al. concluded that 30 wt% ionomer content was the best using Ir black [4]. P. Holzapfel et al [5] and S. Khandavali et al [6] used 2 wt% of ionomer content in their studies with IrO 2 . The large variability of the ionomer content indicates that there is a need on fundamental understanding of the effect of ionomer content for PEMWE applications. Catalyst ink properties may affect the catalyst layer structure and further PEMWE performance. S. Khandavali et al. studied the rheology and microstructure of the catalyst inks [7]. However, no further steps were presented such as fabricating CCMs using the ink and testing the CCMs in PEMWE. How the ink properties affect the catalyst layer structure, and further the PEMWE performance are not studied to our knowledge. In this work, a study on effect of ionomer content on catalyst ink property and further PEMWE performance is presented. In this work, catalyst ink was prepared from a mixture of isopropanol and water (1:1), Nafion solution, and IrO 2 . Inks with Nafion concentrations ranging from 1.0 wt. % to 20 wt. % were investigated. Ink properties such as viscosity, Zeta-potential and average particle size were studied. Properties of CCMs developed from the inks by directly coating the catalyst ink on Nafion membrane using ultrasonic spray were also investigated. The CCMs prepared from inks with 5.0, 7.0, 8.5 and 10% Nafion were tested in PEM water electrolyzer single cell at 80 o C and ambient pressure. The CCM with the 7.0% Nafion shows the highest performance, while the 5.0% Nafion shows the lowest. The 8.5 and 10% Nafion CCMs show slightly lower performance than the 7.0%. The PEMWE was diagnosed with AC impedance. Fig. 1 presents the EIS spectra of the four CCMs in PEMWE obtained at 50 mA.cm -2 . It can be seen that other than the CCM with 5.0% Nafion, all other 3 CCMs showed similar spectra. This is in agreement with the polarization curves. Impedance data fitting using the modified Randles equivalent circuit (solid lines in Fig. 1) shows that the 5.0% Nafion demonstrated the highest anode charge transfer resistance (oxygen evolution reaction (OER)) and the 7.0% shows the lowest value. Correlation of the ink properties with the PEMWE performance will be presented. References Xu, K. Scott, Int. J. Hydrogen Energy, 35 (2010) 12029 – 12037 Su, B. J. Bladergroen, V. Linkov, S. Pasupathi, S. Ji, Int. J. Hydrogen Energy, 36 (2011) 1615081 – 15088 Bernt, H. Gasteiger, J. Electrochem. Soc., 163 (11) (2016) F3179 – F3189 Ma, S. Sui, Y. Zhai, Int. J. Hydrogen Energy, 34 (2009) 678 – 684 Holzapfel, M. Bühler, C. V. Pham, F. Hegge, T. Böhm, D. McLaughlin, M. Breitwieser, S. Thiele, Electrochem. Commun. 110 (2020) 106640 Buhler, P. Holzapfel, D. McLaughlin, S. Thiele, J. Electrochem. Soc., 166 (14) (2019) F1070 – F1078 Khandavali, J. H. Park, N. N. Nariuki, S. F. Zaccarine, S. Pylypenko, D. J. Myers, M. Ulsh, S. A. Mauger, ACS Appl. Mater. Interfaces, 11 (2019) 45068 – 45079 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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".