Bifunctional Oxygen Reduction/Evolution Reaction Electrocatalyst Based on MnO<sub>2</sub> for Rechargeable Alkaline Metal-Air Batteries and Regenerative Fuel Cells: Challenges and Opportunities
Bibliographic record
Abstract
Development of non-PGM bifunctional electrocatalysts with high electrocatalytic activity and durability for both oxygen reduction and evolution reactions (ORR and OER) is of outmost importance to realize the full potential of rechargeable metal-air batteries (e.g., Zn-air, Al-air, Mg-air, Li-air) and regenerative H2-O2 fuel cells. Manganese oxides have been in the spotlight especially as alkaline ORR electrocatalysts, due to low cost and natural abundance. Regarding the bifunctional ORR and OER electrocatalytic performance of MnOx, further improvements in activity and durability are required for implementation in commercial energy storage and conversion systems. The aim of this study is to enhance the bifunctional activity and durability by investigating the role of MnOx morphology, co-catalyst addition, potassium ion doping and support effect (e.g., graphene and graphitized carbon). The experiments were performed in 6 M KOH, 293 K and 1 atm(abs) over a wide potential window encompassing the ORR and OER polarization regions. Results obtained with both flooded dissolved O2 and gas (O2 or air)-diffusion cells are presented. The combination of MnO2 with a structurally different oxide co-catalyst such as perovskite (LaCoO3) or fluorite-type oxide (Nd3IrO7) produces a synergistic catalytic effect improving both the activity and durability compared to the individual oxides. Doping of the oxide catalyst with potassium ions, either by long-term exposure to 6 M KOH or potential driven insertion (PDI), increases further the activity and durability as revealed in accelerated degradation experiments.1,2 The effect of MnO2 morphology on the bifunctional performance was investigated by carrying out a statistically designed MnO2 electrodeposition study. Four main variables were studied through factorial experiments: a) anodic MnO2 electrodeposition potential, b) surfactant type (non-ionic, cationic and anionic) and concentration, c) Mn(II) precursor salt concentration and d) temperature. Fig. 1 shows the surface plots for three different responses: ORR and OER mass activities, and ORR/OER onset potential window.3 Fig. 1. Effect of MnO2 electrodeposition conditions on the ORR and OER bifunctional electrocatalytic performance. Surface plots for the 24-1+3 factorial design showing the three responses: A) ORR mass activity, B) OER mass activity and C) OER/ORR onset potential window. Legend: E - anodic MnO2electrodeposition potential (mV vs. Hg/HgO/0.1 M KOH), S - surfactant (Triton X-100) concentration in the deposition bath (%vol.), T - electrodeposition temperature (K). As shown by Fig. 1, optimizing the MnOx electrodeposition conditions can produce nanostructured morphologies that are favorable for bifunctional activity. The activity metrics compare favorably to either commercially obtained MnO2 sampled or literature reported activities for various catalysts including CoMn2O4 and core-corona structured bifunctional catalyst. The electrochemical results are discussed in conjunction with extensive surface analysis (SEM, TEM, XPS, EDX, EELS) and the modern prevailing theory of ORR and OER electrocatalytic activity based on the scaling relationship between the binding energies of HO* and HOO*. References: 1. P. H. Benhangi, A. Alfantazi and E. Gyenge, Electrochim. Acta, 123, 42 (2014). 2. P. Hosseini-Benhangi, M. A. Garcia-Contreras, A. Alfantazi and E. L. Gyenge, JES, 162, F1356 (2015). 3. P. Hosseini-Benhangi, C.H. Kun, A. Alfantazi and E.L. Gyenge, manuscript in preparation (2016). 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".