Toward Active and Durable ORR/Oer Bifunctional Non-PGM Electrocatalysts: Surfactant-Assisted Electrodeposition of Mn Oxides
Bibliographic record
Abstract
Development of novel bifunctional electrocatalysts with high electrocatalytic activity and durability for both oxygen reduction and evolution reactions (ORR and OER) is of outmost importance to grasp the full potential of the regenerative H2-O2 fuel cells and rechargeable metal-air batteries (e.g., Zn-air, Al-air, Mg-air, Li-air) as reliable and efficient energy conversion/storage systems. Manganese oxides have been on the spotlight as an attractive catalyst material due to cost competitiveness, environmental friendliness, natural abundance as well as excellent reactivity for ORR and to some extent for OER (1, 2). The physicochemical and electrochemical properties of MnOx are highly dependent on its morphology and crystallographic nature (3). The aim of this work is to provide a systematic study on finding an active nanostructured manganese oxide for both ORR and OER via anodic electrodeposition method. A comprehensive study has been performed to investigate the main and interaction effects of key electrodeposition factors that significantly influence the electrosynthesis of manganese oxides, i.e. Mn2+ concentration (C), applied anodic potential (E), temperature (T), surfactant type and concentration (S), on the bifunctional activity of MnOx using a two-level half-fraction factorial design. Sodium dodecyl sulfate (SDS) as anioinc, hexadecyl-trimethyl-ammonium bromide (CTAB) as cataionic and Triton X-100 as non-ionic surfactants were used in this study to electro-synthesize the nanostructured MnOx. Surface characterization methods including XPS and SEM have been employed to analyze morphology and Mn valance of the synthesized electrocatalysts. Fig. 1 shows the surface plots of three different responses studied here for the electrodeposited manganese oxides in presence of Triton X-100, correlating them to the most important factors and two-factor interactions based on the Pareto plots of estimates. In this case, high surfactant concentration together with low applied anodic potential is believed to bring the best ORR/OER bifunctional performances for the electrodeposited Mn oxides (Fig. 1A and 1B). Mn concentration was found to be an insignificant player. Temperature, on the other hand, is believed to have different effect depending on its value with high temperatures providing low ORR/OER potential window while low temperatures lead to high ORR/OER mass activities (Fig. 1). Triton X-100 samples provide best performing nano-sized structures with promising ORR and OER performances comparing to both noble metals and other non-precious metals, i.e. between 50 to 150 mV lower ORR overpotential (at -2 mA cm-2) comparing to CoMn2O4 and Core-Corona Structured Bifunctional Catalyst (CCBC) and min. 100 mV lower OER overpotential (at 2 mA cm-2) comparing to Ir, Ru and IrO2 (1, 2). Galvanostatic polarizations at 5 mA cm-2 showed low OER potentials of 490 mV (at t=2 hrs), about 40 mV lower than commercial MnOx, and degradation rate of 43 mV h-1, about 10 mV h-1 lower than its commercial counterpart. The surface modifications of MnOx via surfactant-assisted electrodeposition can help destabilizing the HOO(ads) and HO(ads) intermediates, breaking away from the linear scaling relationship between their binding energies as a major contributor to the ORR and OER overpotentials, enhancing the ORR and OER electrocatalytic activity of electrodeposited manganese oxides. The formation of hydrogen-bonded complexes, i.e. HO(ads)…H-OH, with specially configured water molecules called “activated water” (4), can further enhance the ORR activity of the catalysts, depending on the surface coverage of OH(ads) which is needed to provide sites for formation of HO(ads)…H-OH complexes. References: 1. P. H. Benhangi, A. Alfantazi and E. Gyenge, Electrochimica Acta, 123, 42 (2014). 2. P. Hosseini-Benhangi, M. A. Garcia-Contreras, A. Alfantazi and E. L. Gyenge, Journal of The Electrochemical Society, 162, F1356 (2015). 3. Y. Chabre and J. Pannetier, Prog. Solid State Ch., 23, 1 (1995). 4. J. Staszak-Jirkovský, R. Subbaraman, D. Strmcnik, K. L. Harrison, C. E. Diesendruck, R. Assary, O. Frank, L. Kobr, G. K. H. Wiberg, B. Genorio, J. G. Connell, P. P. Lopes, V. R. Stamenkovic, L. Curtiss, J. S. Moore, K. R. Zavadil and N. M. Markovic, ACS Catalysis, 5, 6600 (2015). 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".