Developing Cobalt Doped Pr<sub>0.5</sub>Ba<sub>0.5</sub>MnO<sub>3-δ</sub> Electrospun Nanofiber Bifunctional Catalyst for Oxygen Reduction Reaction and Oxygen Evolution Reaction
Bibliographic record
Abstract
The development of energy storage and conversion devices provides a beneficial approach for the renewable energy application, which can help relieve the severe reliance on fossil fuels and also address problems related to global climate change. Currently, the efficiencies of energy conversion devices such as the metal–air batteries and fuel cells are mainly limited by the sluggish kinetics of the oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) processes. Developing catalytically active and cost effective catalyst for the ORR and OER is of prime importance. So far, noble metals and their alloys, such as Pt and Pt-Pd, have been exclusively used as electrochemical bifunctional catalyst in ORR and OER due to their superior catalytic activity. However, the high cost, limited availability, poor durability and sluggish electron transfer kinetics of noble metal based bifunctional catalysts have impeded the practical application of metal-air batteries. Therefore, the discovery of cost effective, catalytically active alternative bifunctional catalyst such as non-precious metals, carbonaceous materials, and transition metal oxides is highly desirable. Perovskite oxide (ABO3) possesses a unique electronic structure and chemical defect properties, and has been demonstrated to be a promising non-precious metal catalyst among the transition metal oxides in the application of metal air batteries and alkaline fuel cells. It is known that the intrinsic electrochemical catalytic activity is mainly determined by the B site cation in perovskite oxide. Extensive research conducted by Yang et al. demonstrated that the ORR and OER catalytic activities of perovskite oxides follow a volcanic relationship with the filling of electrons in antibonding orbitals [1]. Among the typical LaMO3 (M= Mn, Co, Fe, Ni, Cr), LaMnO3 and LaCoO3 have exhibited highest ORR and OER catalytic activities, respectively. In addition, due to the desirable electronic properties of the perovskite oxides, strategies such as cation partial substitution and oxygen non-stoichiometry formation could, therefore, be utilized as the effective approaches to fine-tune the catalytic properties and to achieve a better bifunctional activity [2,3]. To enhance the bifunctional catalytic performance, improved specific surface area as well as enhanced oxygen channel are desired. Researchers have found that electrochemical catalysts with porous nanofiber structures could favor the ORR and OER pathways by providing uniform O2 electrolyte distribution, and beneficial oxygen diffusion channels. Zhao et al. have synthesized mesoporous La0.5Sr0.5CoO2.91 nanowires through the multistep microemulsion method, showing significant enhanced catalytic performance [4]. Xu et al. have demonstrated that the porous La0.75Sr0.25MnO3 nanotube catalyst fabricated through facile electrospinning technologies provides favorable advantages to the availability of the catalytic active sites in the organic solvent electrolyte in Li-O2 batteries [5]. Herein, we developed a bifunctional perovskite catalyst towards both ORR and OER in alkaline solution. Cobalt cations were doped into Pr0.5Ba0.5MnO3-δ perovskite to achieve the higher intrinsic ORR and OER activities by engineering the structure symmetry, electronic and the oxygen vacancy defects of the material. The bifunctional catalyst with a unique porous nanofiber structure was fabricated by electrospinning technology (Figure.1). A single phase perovskite oxide Co doped Pr0.5Ba0.5MnO3-δ was obtained after sintering the electrospun precursor at high temperature. The ORR and OER activities of the composite catalysts consisting 50 wt% of the as-synthesized perovskite oxides and 50 wt% carbon black were investigated in 0.1 KOH with rotating disk electrode (RDE). Significant enhancement in ORR and OER performance was achieved via using the composite catalyst with Co doped Pr0.5Ba0.5MnO3-δ nanofiber/carbon black with respect to the reduced overpotential and improved current density. In particular, the Co doped Pr0.5Ba0.5MnO3-δ nanofiber/carbon black composite demonstrated enhanced electron transfer number in the ORR process, indicating preferable dominance of four electron transfer pathway. Moreover, the Co doped Pr0.5Ba0.5MnO3-δ composite exhibited high stability during the cycling tests, indicating its promising applications in metal-air batteries. References: (1) Suntivich, Jin, et al. Nature chemistry 3.7 (2011): 546-550. (2) Zhu Yinlong, et al. Chemistry of Materials, 28.6 (2016):1691−1697 (3) Jung, Jae-ll, et al. Angewandte Chemie 126.18 (2014): 4670-4674. (4) Zhao, Yunlong, et al. Proceedings of the National Academy of Sciences 109.48 (2012): 19569-19574. (5) Xu, Jing Ji, et al. Angewandte Chemie International Edition, 52.14 (2013): 3887-3890. 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) | 0.000 | 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".