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Record W3116658618 · doi:10.1149/ma2020-02382453mtgabs

Using Pore Former to Improve Performance of Anode Catalyst Layer of a PEM Water Electrolyzer

2020· article· en· W3116658618 on OpenAlexaff
Manas Mandal, Marc Secanell

Bibliographic record

VenueECS Meeting Abstracts · 2020
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMicrostructureMaterials scienceElectrolyteChemical engineeringPorosityCatalysisAnodeProton exchange membrane fuel cellCathodeLayer (electronics)FabricationElectrolysisComposite materialChemistryElectrodeOrganic chemistry

Abstract

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Polymer electrolyte membrane water electrolyzer (PEMWE) performance mostly depends on the catalyst layer (CL) composition and microstructure. The CL microstructure is influenced by catalyst ink composition and fabrication process [1]. Due to the use of an unsupported catalyst (IrO x ) in PEMWE, the porosity of the CL is low, resulting in a low CL surface area and high activation loss. By increasing the porosity of the CL, it is hypothesized that more catalyst surface area will be exposed to the reactant and electrolyte. One method to increase the CL porosity is to use pore formers (PF): sacrificial particles that are added to the ink and then removed after CL fabrication, leaving additional pores. Limited work has been done on the use of PF in fuel cell applications [2–6] which resulted in an increase of the porosity [3,5] and the surface area [2–4]. No work has been done using PF in PEMWE application. In this study, a PF is used to study the effect of PF addition on PEMWE microstructure and performance. In order to fabricate the catalyst coated membranes (CCMs), an anode catalyst ink was prepared by dispersing IrO x powder (Tanaka Kikinzoku Kogyo, ELC-0110) and PF in a dispersion media as described in ref. [7]. The PF to IrO x volume ratio was varied between 0 to 0.25 (Table 1). The ionomer content was kept constant at 35 %wt. The cathode ink was prepared using the method and material described in ref. [8]. The CCMs were prepared by inkjet printing method as described in ref. [7] with catalyst loading as shown in Table 1. One CCM for each PF to IrO x volume ratio was tested and the results are reported. The effect of the PF on the electrochemical surface area (ECSA) was studied using cyclic voltammetry (CV). ECSA was estimated, after conditioning and obtaining nine polarization curves, using the method developed by Tan et al. [9]. The average ECSA of CCM-0.1 is increased by 57 % compared to the CCM-0 after nine polarization curves as shown in Figure 1b and Table 1. While CCM-0.1 shows the highest ECSA, a similar improvement is not seen from CCM-0.25, indicating that the removal of PF is having an impact beyond increasing the ECSA. To study the electrode performance without the effect of varying loading between electrodes, the normalized electrochemical performance with respect to IrO x loading is compared in Figure 1a. CCM-0.1 exhibits the highest performance and shows an improvement of 30 and 80 mV at 2 A/mg compared to the CCM-0.25 and CCM-0 respectively. The cell voltage decreased with an increase in the PF to IrO x volume ratio from 0 to 0.1. When the ratio increased further to 0.25, the cell voltage increased, showing an optimum cell voltage at 0.1 PF to IrO x volume ratio which is in line with the estimated ECSA. The PF has a complex impact on the CL microstructure. These preliminary results indicate that the PEMWE performance can be improved by using PF, and that there might be an optimal PF content. K.-H. Kim, K.-Y. Lee, H.-J. Kim, E. Cho, S.-Y. Lee, T.-H. Lim, S. P. Yoon, I. C. Hwang and J. H. Jang, Int. J. Hydrog. Energy, 35, 2119 (2010). Y. Song, Y. Wei, H. Xu, M. Williams, Y. Liu, L. J. Bonville, H. R. Kunz and J. M. Fenton, J. Power Sources, 141, 250 (2005). T. V. Reshetenko, H.-T. Kim and H.-J. Kweon, J. Power Sources, 171, 433 (2007). Q. Huang, J. Jiang, J. Chai, T. Yuan, H. Zhang, Z. Zou, X. Zhang and H. Yang, J. Power Sources, 262, 213 (2014). A. Fischer, J. Jindra and H. Wendt, J. Appl. Electrochem., 28, 277 (1998). Y.-H. Cho, N. Jung, Y. S. Kang, D. Y. Chung, J. W. Lim, H. Choe, Y.-H. Cho and Y.-E. Sung, Int. J. Hydrog. Energy, 37, 11969 (2012). M. Mandal, A. Valls, N. Gangnus and M. Secanell, J. Electrochem. Soc., 165, F543 (2018). S. Shukla, K. Domican, K. Karan, S. Bhattacharjee and M. Secanell, Electrochimica Acta, 156, 289 (2015). X. Tan, J. Shen, N. Semagina and M. Secanell, J. Catal., 371, 57 (2019). Figure 1

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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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.287
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.015
GPT teacher head0.214
Teacher spread0.199 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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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Citations1
Published2020
Admission routes1
Has abstractyes

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