Ptni/C Catalysts for Improved Selectivity and Performance in Ethanol Fuel Cells
Bibliographic record
Abstract
Fossil fuels are currently the primary source of energy, producing a huge amount of greenhouse gases such as CO2. In general, greenhouse gases contribute to environmental problems like global warming. For this reason, it is necessary to improve and increase the use of renewable and clean energy sources with low CO2 production. In recent years, the direct ethanol fuel cell (DEFC) has been considered as an attractive power source with high potential for vehicles and electronic devices. Ethanol is used as a renewable energy resource because it is easy to handle and store, has low toxicity, and is produced in large quantities from agricultural waste and biomass. Moreover, ethanol has advantages over methanol and hydrogen. Its theoretical energy density is around (8.0 kWh kg-1), which is higher than methanol (6.1 kWh kg-1) and its volumetric density is 6.28 kWh L-1, which is higher than hydrogen (0.18 kWh L-1) and methanol (4.82 kWh L-1) [1, 2]. In spite of that, many problems have impeded DEFC uses, including low current densities, incomplete ethanol oxidation, low faradic efficiencies, and crossover through the membrane. To overcome these problems, many anode catalysts have been developed to increase the activity, selectivity, and efficiency of DEFCs. Studying the effects of alloying Pt catalysts with nickel on the oxidation of ethanol has gained interest. Nickel, is a more electropositive metal than Pt, and has been found to enhance its activity toward the ethanol oxidation reaction. A bifunctional, Langmuir–Hinshelwood mechanism, in which a more electropositive metal provides more OH- species on the surface of the catalyst and drives the oxidation of the adsorbed CO (COads) intermediate to produce CO2, was used to explain the behavior of the PtNi catalyst’s activity [3]. Sulaiman et al studied the effects of shape-controlled octahedral PtNi/C nanoparticles on the activity of ethanol oxidation. Activities of commercial Pt/C, conventional Pt2Ni/C alloy, and octahedral Pt2.3Ni/C nanoparticles toward ethanol oxidation were measured by cyclic voltammetry. The results showed that the octahedral PtNi/C nanocatalyst was 4.6 and 7.7 times more active than conventional PtNi/C and commercial Pt/C catalysts, respectively [4]. Furthermore, Altarawneh et al evaluated the octahedral PtNi/C for use in DEFCs by measuring its selectivity and performance. The selectivity of this catalyst was significantly higher than commercial Pt/C at low potential. At 0.20 V, the faradaic yield of CO2 at PtNi/C was 73%, while at Pt/C it was 55%. [5]. The primary objective of our research is to understand the effects of PtNi/C catalyst composition, structure, and shape on the activity, performance, and selectivity for the complete oxidation of ethanol to carbon dioxide in DEFCs. PtNi/C catalysts were synthesized in various solvents by using a polyol method. The prepared catalysts were characterized by X-ray powder diffraction (XRD), thermal gravimetric analysis (TGA), and energy dispersive X-ray spectroscopy (EDX) to investigate the composition, metal loading, and crystal structure. Electrochemical analysis was carried out by cyclic voltammetry and chronoamperometry in order to study the activity of these catalysts toward the oxidation of ethanol. Moreover, a commercial PtNi/C catalyst was evaluated using the same methods and its results were compared with our catalysts’ results. Furthermore, the product distribution, selectivity for CO2 formation, and efficiency were investigated for some of our PtNi/C catalysts and the commercial PtNi/C in 5 cm2 fuel cell. 1. An, T.S. Zhao, and Y.S. Li, Renewable and Sustainable Energy Reviews., 50, 1462–1468 (2015). 2. Wang, S. Zou, and W. B. Cai, Journal of Catalysts., 5, 1507-1534 (2015). 3. Soundararajan, J. Park, K. Kim, and J. Ko, Current Applied Physics., 12, 854−859 (2012). 4. E. Sulaiman, S.Q. Zhu, Z.L. Xiang, Q.W. Chang, and M.H, Shao. ACS Catalysis., 7, 5134–5141 (2017). 5. R. Altarawneh, T. Brueckner, B. Chen, and P. Pickup, Journal of Power Sources., 400, 369–376 (2018).
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".