Cathodic Pd<sub>1-X</sub>Ni<sub>X</sub> Nanocatalyst Development for Alkaline Fuel Cell Applications
Bibliographic record
Abstract
Alkaline fuel cells are a versatile, low-emission alternative energy source for use in transportation, as well as for portable and stationary power applications. These systems use chemical energy from the electrochemical conversion of hydrogen and oxygen gas to produce electrical energy, with only water and heat as by-products. They operate through two interdependent redox reactions within the cell consisting of oxidation of hydrogen fuel at the anode and reduction of oxygen at the cathode. One of the most significant barriers that fuel cells face is the sluggish nature of the oxygen reduction reaction (ORR) and its dependence on platinum (Pt), a rare and costly metal, as a catalyst for the reaction. Through the development and optimization of the structures and materials, the amount of precious metal catalyst can be decreased without sacrificing performance and enabling an increase in the commercial viability of the device. An alternative cathode material is palladium (Pd), which possesses similar chemical properties to Pt but is more stable in alkaline media and available in a greater abundance. Additionally, the catalytic ability of Pd is comparable to that of Pt, but due to the electronic structure of Pd, it bonds oxygen to a stronger extent, leading to a reduced activity. Theoretical analyses of the Pd electronic structure have shown that tuning the metal d-band decreases the oxygen-metal adsorption strength and increases the catalytic ability.1 Tuning of the metal d-band can be achieved experimentally through the addition of ancillary metals, thus altering the electronic structure of the catalyst. Furthermore, volcano trends of metal alloys in acidic media have shown a significant increase in activity when plotted against the oxygen adsorption strength.2 The increase in activity is particularly evident when Pt, for example, is alloyed with ancillary nickel (Ni). Due to the decreased stability of Pt in alkaline media, this study aims to assess the variations in activity of Pd through the addition of varying amounts of Ni. The incorporation of Ni increases the Pd utilization and incorporates a more cost-effective and abundant transition metal, enabling a decrease in the amount of precious metal required for the catalyst. Furthermore, the use of nanoparticles as the basis for the cathode catalyst creates a high surface area on which the ORR can occur. Nanoparticles allow for a more efficient use of the catalyst materials and, consequently, decrease the amount of metal required and the overall cost of the catalyst. This study assesses the viability of palladium-nickel nanocatalysts synthesized using a water-in-oil microemulsion3 method. The synthetic method was optimized to produce small, well-dispersed nanoparticles with a narrow size distribution ideal for the ORR. Furthermore, the synthesis was tuned to produce the desired ratios of Pd to Ni to determine an optimal composition for increased catalytic performance. The analysis of these nanocatalysts includes assessing the relationship between their composition, structure, and function using a combination of electrochemical and microscopy techniques. The results demonstrate a promising catalytic activity, indicating a comparable performance for all compositions. The consistent performance allows for a significant decrease in the precious metal content through the replacement with a more abundant and cost-effective metal. Further studies are being carried out to better understand the stability of these materials in alkaline media under a range of operating conditions relevant to the ORR. These studies aim to enhance the durability and to increase the overall viability of the catalyst. B. Hammer, J.K. Nørskov, Theoretical surface science and catalysis - calculations and concepts, Adv. Catal. 45 (2000) 71-129. A. Holewinski, H. Xin, E. Nikolla, S. Linic, Identifying optimal active sites for heterogeneous catalysis by metal alloys based on molecular descriptors and electronic structure engineering, Curr. Opin. Chem. Eng. 2 (2013) 312-319. S. Lankiang, M. Chiwata, S. Baranton, H. Uchida, C. Coutanceau, Oxygen Reduction Reaction at Binary and Ternary Nanocatalysts Based on Pt, Pd and Au. Electrochim. Acta. 182 (2015) 131–142.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".