Impact of Different Supports on the Performance of Ir Oxide Based Catalysts Synthesized Using Incipient Wetness Method
Bibliographic record
Abstract
Development of an active and durable catalyst for oxygen evolution reaction (OER) is a pivotal part of designing an efficient PEM water electrolyser. Iridium is one of the best available catalysts because of its corrosion resistance and activity [1]. Use of Ir is however, limited by its scarcity, limited durability and high cost. Its deposition on a support is a popular way to improve the available surface area, activity and stability [2]. For applications in acidic water electrolysis, supports with the highest chemical resistance and conductivity are preferred. While different methods have been studied to impregnate a support with catalyst nanoparticles, such as Adams’s fusion method and polyol method, their scalability remains questionable due to intensive resource requirements. In this work, Ir oxide based supported catalysts have been synthesized using the incipient wetness method (IWM), one of the most commonly used method of catalyst synthesis in the industry due to its ease of scalability, limited resource requirement and the opportunity to explore the effect of strong metal support interactions. This method however has not been discussed in literature. In this study, Ir oxide (IrOx) (Ir loading on support = 20 wt. percent) was dispersed on commercial supports (ZrO2, Nb2O5, Ta2O5, ATO) using H2IrCl6.xH2O precursor (99.9% trace metal basis, Sigma Aldrich). The catalysts were calcined at 400oC for 2 h in a muffle furnace to produce Ir oxide based catalysts. Electrochemical measurements were carried out on a standard rotating-disk electrode (RDE) system (PINE Research MSR Rotator), and a three-electrode electrochemical cell in 1.0 M sulfuric acid electrolyte (H2SO4 optima grade, Fisher Scientific). The performance of the aforementioned supported Ir oxide catalyst was compared among themselves, and to benchmark commercial catalysts (Umicore Ir Black and IrOx TKK) using metrics such as mass normalized activity (A/g-1 Ir), ECSA normalized activity (mA/cm2 Ir ECSA), Tafel slope (mV/dec) and charge transfer resistance (Rct) measured during the electrochemical test. It was observed that the activity of IrOx/ZrO2 (415 A/gIr, 5.2 mA/cm2 Ir ECSA) was the best among all the 4 impregnated catalysts, and was in fact, more than an order of magnitude greater than that of IrOx/ATO (30 A/gIr, 0.3 mA/cm2 Ir ECSA) at a potential of 1.53 VRHE. A significant drop in the Tafel slope measured in the potential range of 1.45-1.55 VRHE was observed upon changing the support from ATO (80 mV/dec) to ZrO2 (60 mV/dec) hinting towards a change in the reaction mechanism. IrOx/ATO was used as a baseline due to prevalent recognition of ATO as an excellent support for OER catalysts in the literature. Upon comparison with commercial benchmark catalysts it was observed that the Ir ECSA normalized activity of IrOx/Nb2O5, IrOx/Ta2O5, IrOx/ZrO2 surpasses both Umicore Ir black ( 2.23 mA/cm2 Ir ECSA) and IrOx TKK (1.23 mA/cm2 Ir ECSA) with IrOx/ZrO2 providing the highest activity. While Yttria-stabilized zirconia (YSZ) has been a popular choice as an electrolyte and anode for high temperature SOFC due to its non-reducing nature, high thermal stability and mechanical strength, and acceptable oxygen ion conductivity [3], its application in PEM water electrolysis as catalyst support has not been discussed. In this work, we focus on finding the causes for superior performance of ZrO2 as a support for Ir oxide based OER reaction in acidic conditions through the lens of electrocatalysis. References: [1] X. Li, X. Hao, A. Abudula, and G. Guan, “Nanostructured catalysts for electrochemical water splitting: Current state and prospects,” Journal of Materials Chemistry A, vol. 4, no. 31, pp. 11973–12000, 2016, doi: 10.1039/c6ta02334g. [2] H. Dhawan, M. Secanell, and N. Semagina, “State-of-the-art iridium-based catalysts for acidic water electrolysis: a minireview of wet-chemistry synthesis methods,” Jan. 01, 2021. https://www.ingentaconnect.com/content/matthey/jmtr/pre-prints/content-jm_jmtr_semagapr21 (accessed Mar. 26, 2021). [3] T. K. Maiti et al., “Zirconia- and ceria-based electrolytes for fuel cell applications: critical advancements toward sustainable and clean energy production,” Environ Sci Pollut Res, vol. 29, no. 43, pp. 64489–64512, Sep. 2022, doi: 10.1007/s11356-022-22087-9. 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".