Controlled Ionomer Deposition into the Cathode Catalyst Layer By Inkjet Printer for PEM Fuel Cells
Bibliographic record
Abstract
Proton exchange membrane (PEM) fuel cells are being intensively investigated as alternative energy conversion systems for both residential and automotive applications. In order for this technology to become fully commercial, the reduction of cost and improvements in performance and durability of PEM fuel cells membrane electrode assemblies (MEAs) are still required. The ionomer plays several important roles in the catalyst layer (CL), as the binder, proton conductor as well as oxygen transport media. It has been shown that amount of ionomer and its morphology and interaction with catalyst particles play an important role in Pt utilization and cell performance. However, controlling the ionomer structure and morphology inside the CL with conventional method of MEA preparation has been a challenge. There are several reports describing the effect of ionomer loading in catalyst layers on the fuel cell performance, ionic conductivity, and microstructure of MEA but less attention has been paid to the role of the distribution of ionomer within the catalyst layer [1–4]. It is conjectured that incorporating a graded distribution of ionomer such that its content is higher at the CL/membrane interface and lower at the CL/carbon paper interface might be beneficial to proton migration and mass transport, respectively. That is, the region of the catalyst layer possessing the highest ionic current density would be designed to possess the higher proton conductivity, and the region associated with the highest flux of gaseous oxygen would be bestowed with the higher porosity. Some studies that examined novel methodologies for MEA preparation have specifically tried to achieve a controlled ionomer distribution inside the CL [1–4], with the aim of improving three-phase boundary and catalyst utilization. For instance, Xie et al. [1] prepared a gas diffusion electrode (GDE) that contains a graded distribution of ionomer. The performance was improved when the Nafion® content in the GDE was higher toward the CL/membrane interface and lower toward the CL/carbon paper interface. It was hypothesized that this maximizes proton transport in the GDE in the region of greatest ion flux and maximizes porosity in the region of greatest gaseous flux, respectively. Shin et al. [2] impregnated lower ionomer content into a catalyst layer and coated additional higher ionomer content on its surface in order to increase proton conductivity at the catalyst layer/membrane interface. Cheng et al. [3] also suggested that impregnating the catalyst layer with additional Nafion® ionomer improves proton conduction. A theoretical study of PEMFC cathodes [4] suggested that the catalyst layer possessing a graded distribution of ionomer in which the Nafion® content was larger toward the catalyst layer/membrane interface should exhibit a slightly higher performance in fuel cells. Recently, great interest has arisen with respect to inkjet printing technology for manufacturing CL’s [5–7]. Our recent publications on inkjet printing techniques show great potential for increasing Pt utilization by reducing amount of ionomer [6]. The high-precision of inkjet printing allows for controlled catalyst deposition, especially for low Pt loadings, as well as ionomer patterning and gradient structures inside the CL. Therefore, in this work, inkjet printing technology was used to study ionomer distribution inside the CL. An experimental strategy is devised and implemented to examine the influence of a gradient of ionomer printed by inkjet nozzles into the CL and to verify the theoretical prediction of Wang et al. [4]. The effect of a graded distribution of ionomer content on ionic conductivity, active catalyst area, and porosity as well as performance results under different simulated automotive conditions are examined. References [1] Z. Xie, T. Navessin, K. Shi, R. Chow, Q. Wang, D. Song, B. Andreaus, M. Eikerling, Z. Liu, S. Holdcroft, J. Electrochem. Soc., 152 (2005) A1171. [2] S.-J. Shin, J.-K. Lee, H.-Y. Ha, S.-A. Hong, H.-S. Chun, I.-H. Oh, J. Power Sources 106 (2002) 146–152. [3] X. Cheng, B. Yi, M. Han, J. Zhang, Y. Qiao, J. Yu, J. Power Sources 79 (1999) 75–81. [4] Q. Wang, M. Eikerling, D. Song, Z. Liu, T. Navessin, Z. Xie, S. Holdcroft, J. Electrochem. Soc. 151 (2004) A950. [5] S. Shukla, K. Domican, K. Karan, S. Bhattacharjee, M. Secanell, Electrochim. Acta 156 (2015) 289–300. [6] M.S. Saha, M. Tam, V. Berejnov, D. Susac, S. McDermid, A. P. Hitchcock, J. Stumper, ECS Trans. 58 (2013) 797–806. [7] M.S. Saha, D. Paul, D. Malevich, B. Peppley, K. Karan, ECS Trans. 25 (2009) 2049–2059. Acknowledgements The authors would like to thank Dorina Manolescu and Beniamin Zahiri for prototyping support.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".