A Novel and Economical Rde-Based Approach for Investigating the Oxidation Evolution Reaction Activity of IrO<sub>2</sub>-Based Catalyst Coated Membranes
Bibliographic record
Abstract
Polymer electrolyte membrane water electrolyzers (PEMWE) are clean and scalable energy conversion devices that can assist with electricity peak shaving and storage by converting electricity into hydrogen as an energy carrier 1. However, performance, durability and cost of these systems still require further improvements before they can be widely adopted. In particular, it is desired to have more efficient and lower cost oxygen evolution reaction (OER) electrocatalysts for the anode, because of the high cost and potential losses that are associated with this electrode2. Screening of catalyst materials, as well other expensive cell components (e.g., Ti current collectors and Ti endplates), is in general a slow and rather expensive process due to the inherent high costs of test cell equipment, and the difficulties associated with cell assembly3. Alternative methods based on commonly available and small laboratory-scale electrochemical test equipment, such as the rotating disk electrode, can facilitate the screening process. We present a novel and economical method for testing electrolysis CCMs and potential current collectors in a rotating electrode (RDE) setup. This method eliminates the difficulties which are often associated with ink preparation and sample drying which are encountered in thin film RDE (TF-RDE) testing. Very recently, we have successfully demonstrated this approach for evaluation of oxygen reduction reaction (ORR) electrocatalysts used in PEMFC applications4. The modified-RDE (MRDE) presented here allows a user to obtain very high current densities (~ 2 A cm-2) which is similar to those normally obtained with PEMWE hardware and test stations (Figure 1). Polarization measurements, kinetic analysis and accelerated degradation tests (ADT) have been performed with a commercial IrO2-based CCM using the MRDE, and are compared with those of PEMWE hardware results. The approach, methodology and the results will be presented at the meeting. References: A. S. Aricò, S. Siracusano, N. Briguglio, V. Baglio, A. Di Blasi, and V. Antonucci, J. Appl. Electrochem., 43, 107–118 (2013). H. Dau, C. Limberg, T. Reier, M. Risch, S. Roggan, and P. Strasser, ChemCatChem, 2, 724–761 (2010). A. S. Lohoff, L. Poggemann, M. Carmo, M. Müller, and D. Stolten, J. Electrochem. Soc., 163, F3153–F3157 (2016) http://jes.ecsdl.org/lookup/doi/10.1149/2.0211611jes. J. T. H. Kwan, A. Bonakdarpour, G. Afonso, and D. P. Wilkinson, Electrochim. Acta, 258, 208–219 (2017) http://linkinghub.elsevier.com/retrieve/pii/S0013468617322090. Figure 1: The effect of different titanium current collector meshes on OER performance using a commercial IrO2-based CCM obtained by the MRDE tool. Inset shows pictures of the different Ti meshes examined. 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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".