Understanding and Designing Oxygen Reduction/Evolution Reaction (ORR/OER) Catalysts By Combining Experimental and Ab-Initio Studies
Bibliographic record
Abstract
The increased awareness of low-carbon economy and sustainable energy generation continues to push the development of next-generation of energy conversion and storage systems, which aims to alleviate dependence on fossil fuels and reduce carbon emissions that cause global warming (1). There has been a tremendous interest in pursuing both fundamental and applied research towards developing new types of sustainable energy systems such as fuel cells, metal-air batteries and electrochemical water splitting system (1-3). The significant and share reactions of those applications are oxygen reactions which are oxygen reduction reaction (ORR) and oxygen evolution reaction (OER) (1-3). So far, it is well known that the best catalysts are precious metals like Pt and Pt-based electrocatalysts, which are very expensive (especially in acidic media) because of their critical challenges: i) large overpotentials resulting from insufficient drive for sluggish ORR and OER kinetics, and ii) catalyst degradation by relatively unstable electrochemistry in operating condition. These challenges arise from insufficient activity and durability of the air electrode, which includes electrocatalysts that lower activation energies of the reactions to reduce overpotential in working condition (1-3). Hence recent research efforts in energy materials development have been focused on addressing the above challenges (4, 5). Recent approaches in density functional theory (ab-initio studies, DFT) combining with experimental studies have allowed researchers to precisely simulate catalytic activities and gain fundamental understandings of the bifunctional oxygen reactions (4-6). Especially, it enables designing of efficient non-precious material-based catalysts and effectively minimizing the use of noble metals to render sufficiently active low cost catalysts such as non-precious transition metal-based materials, functionalized carbon-based materials, metal–nitrogen complex and noble metals (7). Particularly in the case of minimizing the use of noble metals, Pt3Ni has been revealed to show enhanced ORR activity due to downward shifted d-band center in electronic structure by Nørskov and associates, which results in a weak adsorption with oxygen intermediates on catalytic surface (6). Understanding eg orbital of valence electrons makes it possible to predict the oxygen reactivity that can be controlled by the number of outer electrons of transition metal in non-precious catalysts (4, 8). In addition, the electrochemical stability has been associated with the dissolution potential and cohesive energy term modelled by changing the morphology and size of the transition metal nanoparticles, as well as support materials (9, 10). Accordingly, a synergetic approach using both experimental and ab initio computational studies with physicochemical analyses is required to efficiently and accurately develop a new catalyst with highly improved activity. To apply energy conversion and storage devices such as fuel cells, metal-air batteries systems, in this work, we have predicted the ORR and OER activity and stability for self-assembled nitrogen-doped fullerenes (N-fullerene), and studied the perovskite oxides for the reaction mechanism in aspect of understanding OER activity. In addition, a highly efficient bifunctional oxygen electrocatalyst, combining Pd and three-dimensionally ordered mesoporous spinel cobalt oxide (3DOM Co3O4), has manly discussed in terms of obtaining a stability. This study provides a way of rationally designing efficient electro-catalyst based on the principle that governs thermodynamic and electrochemical activities and stabilities by applying first principles calculations and state of the art experimental measurements to well-defined model systems. M. E. Scofield, H. Liu and S. S. Wong, Chem. Soc. Rev., 44, 5836 (2015). Z.-L. Wang, D. Xu, J.-J. Xu and X.-B. Zhang, Chem Soc Rev, 43, 7746 (2014). J. K. Norskov and C. H. Christensen, Science 312, 1322 (2006). M. H. Seo, H. W. Park, D. U. Lee, M. G. Park and Z. Chen, Acs Catal, 5, 4337 (2015). M. G. Park, D. U. Lee, M. H. Seo, Z. P. Cano and Z. Chen, Small, 12, 2707 (2016). J. Greeley, I. E. L. Stephens, A. S. Bondarenko, T. P. Johansson, H. A. Hansen, T. F. Jaramillo, J. Rossmeisl, I. Chorkendorff and J. K. Nørskov, Nat. Chem., 1, 552 (2009). Z. Chen, D. Higgins, A. Yu, L. Zhang and J. Zhang, Energy Environ. Sci., 4, 3167 (2011). F. Calle-Vallejo, O. A. Díaz-Morales, M. J. Kolb and M. T. M. Koper, ACS Catal., 5, 869 (2015). M. H. Seo, S. M. Choi, E. J. Lim, I. H. Kwon, J. K. Seo, S. H. Noh, W. B. Kim and B. Han, Chemsuschem, 7, 2609 (2014). D. Higgins, M. A. Hoque, M. H. Seo, R. Wang, F. Hassan, J.-Y. Choi, M. Pritzker, A. Yu, J. Zhang and Z. Chen, Adv. Funct. Mater., 24, 4325 (2014).
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".