Improving the Activity and Stability of PdSn Catalyst for Oxygen Reduction by Heat Treatment
Bibliographic record
Abstract
The search for new efficient and inexpensive ORR electrocatalysts for cathodes of PEMFCs has been a fast-growing area of research over the past 10 years. One of the most promising cathode systems are the Pd-based alloys, such as PdCo, PdCr, PdNi [1,2], PdFe [3] and PdP [4] that compete with platinum in terms of electrocatalytic activity. A very important concern in the development of fuel cells is the electrochemical stability of the catalyst, which can affect the fuel cell lifetime. A few publications have described the ORR activity of PdSn alloys in acid media [5,6]. However, to the best of our knowledge, the effect of heat treatment on PdSn electrocatalyst activity and stability has not been yet investigated. In this work, PdSn/C electrocatalysts for ORR have been synthesized by the sodium borohydride method with sensitizing (Sn2+/SnO2 or Sn4+) and activating pretreatment (Pd2+/Pd) of the carbon support Vulcan XC72. The electrocatalysts have been then heat treated at 300 ºC in a reducing atmosphere (3% hydrogen in nitrogen). The morphology, structure and composition of the catalysts as-prepared and subject to thermal treatment have been characterized by XRD, SEM/EDS and TEM. The activity of PdSn/C catalysts for ORR in acid media has been studied using a rotating disc electrode (RDE) and electrochemical impedance spectroscopy (EIS). Specific activity-time curves measured at a fixed potential have been used to study the long-term catalyst performance for more than 2 days. The PdSn/C catalysts prepared with heat treatment exhibited an enhanced activity and stability as compared to those synthesized without treatment. [1] O. Savadogo, K. Lee, K. Oishi, S. Mitsushima, N. Kamiya, K.-I. Ota, Electrochem. Commun. 6 (2004) 105 [2] K. Lee, O. Savadogo, A. Ishihara, S. Mitsushima, N. Kamiya, K.-i Ota, J. Electrochem. Soc. 153 (2006) A20 [3] M.-H. Shao, K. Sasaki, R. R. Adzic, J. Am. Chem. Soc. 128 (2006) 3526 [4] R. Rego, A. M. Ferraria, A.M. Botelho do Rego, M. Cristina Oliveira, Electrochimica Acta 87 (2013) 73 [5] Md. R. Miah, J. Masud, T. Ohsaka, Electrochimica Acta 56 (2010) 285 [6] J. Salvador-Pascual, J. A. Chávez-Carvayar, O. Solorza-Feria, ECS Transactions 15 (2008) 3 Acknowledgements This work was supported by Fundação para a Ciência e a Tecnologia (FCT) and FEDER (contracts PTDC/QUI-QUI/110855/2009, PEst-OE/QUI/UI0616/2014) and COST Action MP1202 "Rational design of hybrid organic-inorganic interfaces". R. Rego acknowledges FCT for grant SFRH/BSAB/1337/2013.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".