Performant Non-Precious Metal-Oxide Electrocatalysts for Oxygen Evolution Reaction in Alkaline Media: From Electrochemical Treatments of Ni Substrates to Surfactant-Assisted Electrodeposition of Perovskites
Bibliographic record
Abstract
To meet the global energy demand in a sustainable way, the development of reliable and efficient energy storage systems seems crucial. One of the attractive ways to store energy is through water electrolysis, producing hydrogen as an energy carrier (1). This process is limited by the sluggish kinetics of the oxygen evolution reaction (OER) occurring at the anode of an electrolyzer (eq. 1) (1). 4OH- → O2 + 2H2O + 4e- (Eo 298 = 0.401 VSHE) (1) In the alkaline media, a wide range of electrocatalyst materials including both precious and non-precious group metal compounds such as IrO2, RuO2, nickel oxides, manganese oxides, FeNiOx and perovskites have been intensively studied for OER electrocatalysis (2-8). Among these oxides, manganese oxides along with cobalt-based perovskites (e.g. LaCoOx) have shown promising electrocatalytic activity and long-term durability toward OER (2, 5, 7, 8). To alleviate the low electrical conductivity of such oxides, carbon-based materials are widely being used as catalyst supports and backing layers (5, 9, 10). However, typical carbonaceous materials suffer durability issues mainly caused by carbon corrosion at the high anodic potentials corresponding to the OER (11, 12). The Nickle-based electrodes have been employed in such applications in alkaline media to avoid the carbon corrosion showing high electrocatalytic activity and durability toward OER (13, 14). This study aims at investigating electrocatalytic activity and long-term stability of manganese oxides and perovskites deposited on Ni foams. Novel chemical and electrochemical treatments on the Ni foam substrates have been employed to enhance the durability of the deposited oxides. The deposited manganese oxide has provided the lowest initial OER overpotentials of between 210-230 mV at 10 mA cm-2, losing about 20 mV over 1 hr of testing (Fig. 1). The surfactant-assisted LaCoOx deposit has shown excellent durability over 1 hr of testing with an OER overpotential of about 260 mV at 10 mA cm-2. The electrochemical treatments of the Ni foam substrates have significantly decreased the OER overpotentials of the deposited oxides while increasing their long-term durability for OER through enhanced adhesion of the electrocatalysts to the treated Ni substrates. Figure 1. OER benchmarking study of the electrodeposited non-PGM electrocatalysts. The conditions are as follows: MnOx (T): MnOx electrodeposited in presence of Triton X-100. LaCoOx (T): LaCoOx electrodeposited in presence of Triton X-100. Acid-etched Ni: Mixed acid treatment on the Ni foam. CV Ni: Cyclic voltammetry treatment on the Ni foam for 10 cycles in 45 wt% KOH at 293 K. LSV5: Linear sweep voltammetry treatment on the Ni foam for 5 sweeps in 45 wt% KOH at 293 K. CA1: Chronoamperometry treatment on the Ni foam for 1 min in 45 wt% KOH at 293 K. Test conditions are: 10 mA cm-2. 45 wt% KOH. 400 rpm. 323 K. References: L. Giordano, B. Han, M. Risch, W. T. Hong, R. R. Rao, K. A. Stoerzinger and Y. Shao-Horn, Catalysis Today, 262, 2 (2016). P. Hosseini-Benhangi, M. A. Garcia-Contreras, A. Alfantazi and E. L. Gyenge, Journal of The Electrochemical Society, 162, F1356 (2015). Y. Lee, J. Suntivich, K. J. May, E. E. Perry and Y. Shao-Horn, The Journal of Physical Chemistry Letters, 3, 399 (2012). C. G. Morales-Guio, M. T. Mayer, A. Yella, S. D. Tilley, M. Grätzel and X. Hu, Journal of the American Chemical Society, 137, 9927 (2015). P. Hosseini-Benhangi, A. Alfantazi and E. Gyenge, Electrochimica Acta, 123, 42 (2014). S. Jung, C. C. L. McCrory, I. M. Ferrer, J. C. Peters and T. F. Jaramillo, Journal of Materials Chemistry A, 4, 3068 (2016). P. Hosseini-Benhangi, C. H. Kung, A. Alfantazi and E. L. Gyenge, ACS Applied Materials & Interfaces (2017). E. Gyenge and P. Hosseini-Benhangi, An oxygen electrode and a method of manufacturing the same, in, U.S. (15/251,267) and Canadian (2,940,921) patent applications (Filed on August 30, 2016). R. Cao, J.-S. Lee, M. Liu and J. Cho, Advanced Energy Materials, 2, 816 (2012). K. A. Stoerzinger, M. Risch, B. Han and Y. Shao-Horn, ACS Catalysis, 5, 6021 (2015). P. N. Ross and H. Sokol, Journal of The Electrochemical Society, 131, 1742 (1984). N. Staud and P. N. Ross, Journal of The Electrochemical Society, 133, 1079 (1986). D. E. Hall, Journal of The Electrochemical Society, 132, 41C (1985). J. van Drunen, B. K. Pilapil, Y. Makonnen, D. Beauchemin, B. D. Gates and G. Jerkiewicz, ACS Applied Materials & Interfaces, 6, 12046 (2014). 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".