Oxygen Reduction over Dealloyed Pt Layers on Glancing Angle Deposited Ni Nanostructures and Efficient Water Oxidation over Easily Prepared Ir-Ni Oxide Nanoparticles
Bibliographic record
Abstract
A challenge in the commercialization of proton exchange membrane fuel cells (PEMFC) is the large overpotential for the oxygen reduction reaction (ORR) at the cathode. Platinum is the most active single-component catalyst, but it has the inherent drawbacks of high cost and scarcity. Combining Pt with non-noble metals (eg. Ni, Fe, Cu etc.) has proven to enhance the ORR activity and reduce the Pt mass (1). We describe the use of glancing angle deposition (GLAD, developed by Brett et al. (2), a physical vapor deposition technique) to deposit Ni with controlled nanostructures on glassy carbon supports (Ni GLAD/GC). A unique electrodeposition method was utilized to deposit a thin layer of Pt over the surface of the Ni GLAD structure in a controlled and conformal way. The Pt loading was varied by interrupting the deposition at various coverages, and a series of the Ni GLAD{Pt}/GC deposits was evaluated towards the ORR in acid (3). These Ni GLAD{Pt}/GC deposits are remarkably more active and durable than {Pt}/GC during ORR in acid and in base (4). In addition, dealloying (selective electrodissolution of Ni to make a more robust and active resulting catalytic material) in acid further enhanced the activity of Ni GLAD{Pt}/GC towards the ORR. Water electrolyzers (WEs) are used to store/transform energy from renewable sources. The sluggish kinetics for the water oxidation reaction (WOR) severely lowers the efficiency of WEs (5). Iridium is an active and stable catalyst for WOR in acid. Due to its high cost, Ir based catalysts with better activity and decreased loading are required for the widespread adoption of WEs. In this talk, we will present a one-pot synthesis of novel highly active and durable Ir-Ni oxide nanoparticles under mild conditions. A mass activity > 140 A g-1 Ir at 0.25 V overpotential was obtained for IrNi0.125 atomic composition. Long term galvanostatic polarization and duty cycles showed that the catalysts prepared by this procedure are remarkably stable (6). References 1. I. Katsounaros, S. Cherevko, A. R. Zeradjanin and K. J. J. Mayrhofer, Angewandte Chemie International Edition, 53, 102 (2014). 2. K. Robbie and M. J. Brett, Journal of Vacuum Science & Technology A, 15, 1460 (1997). 3. C. Wang, R. B. Moghaddam, J. B. Sorge, S. Xu, M. J. Brett and S. H. Bergens, Electrochimica Acta, 176, 620 (2015). 4. S. Xu, C. Wang, S. A. Francis, R. T. Tucker, J. B. Sorge, R. B. Moghaddam, M. J. Brett and S. H. Bergens, Electrochimica Acta, 151, 537 (2015). 5. E. Fabbri, A. Habereder, K. Waltar, R. Kotz and T. J. Schmidt, Catalysis Science & Technology, 4, 3800 (2014). 6. R. B. Moghaddam, C. Wang, J. B. Sorge, M. J. Brett and S. H. Bergens, Electrochemistry Communications, 60, 109 (2015). Figure 1
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".