Incorporating Co and Y into Amorphous Ni-Based Alloys to Stabilize Hydrous β-NiOOH for Efficient Electrocatalytic Water Oxidation
Bibliographic record
Abstract
The use of novel anion exchange membranes (AEM) are of interest in a wide variety of electrochemical systems. On particular interest is in alkaline water electrolysers since it eliminates the aqueous KOH electrolyte and can provide improved form factors and much higher current densities that rival proton exchange membrane (PEM) electrolysers.1 The use of AEM water electrolysis also enables the use of less expensive earth-abundant non-platinum group metals (PGM) materials, such as nickel-based alloys as electrocatalysts.1 Conventional Ni electrocatalysts for the oxygen evolution reaction (OER) still display large overpotentials and slower reaction kinetics. The addition of secondary metals, such as Fe and Co, are commonly added to enhance the OER performance but suffer from stability issues. 2–4 The leaching of Co is noted to be much less severe which allows for more long-term electrocatalytic benefits to Ni by way of stabilizing the β-NiOOH phase over γ-NiOOH.4–6 As a result, Co is a very desired alloying element but a reduction in the amount used is required as Co is quickly becoming limited in supply due to it being essential for many renewable energy applications.7 Recent studies have shown that Y has catalytic synergy with Co by accepting excess electrons to facilitate OH- absorption and improve the formation of active intermediates.8,9 These findings indicate Y can enhance the OER activity of Co and reduce the Co concentration required. In this work, amorphous Ni79.2-xCoxNb12.5Y8.3 (x = 0 and 5 at.% Co) nanoparticles were synthesized using cryogenic mechanical alloying followed by surfactant-assisted high energy ball milling (SA-HEBM).10 This two-stage ball milling process provided a novel processing route for the production of nanostructured / amorphous materials with a wide range of possible compositions not achievable through rapid solidification, electrodeposition, or chemical reduction techniques. The resulting structures were characterized through x-ray diffraction and electron microscopy. The electrocatalytic activity and stability of amorphous nanoparticles on the alkaline oxygen evolution reaction were investigated through prolonged cyclic voltammetry and Tafel measurements. Cyclic voltammagrams demonstrated stable and reproducible curves for amorphous alloys up to 10000 cycles while crystalline Ni and Ni95Co5 showed signs of deactivation with cycling. Initially the addition of Co to crystalline Ni resulted in enhanced OER catalytic performance, but the performance drastically reduced beyond 500 cycles. A similar increase in performance was observed when adding Co to amorphous Ni79.2Nb12.5Y8.3, except the activity and stability was maintained throughout the 10000 cycles. The pairing of X-ray photoelectron spectroscopy (XPS) revealed no signs of overcharging in amorphous Ni74.2Co5Nb12.5Y8.3 and that the Y and Co were integrated into the Ni oxy-hydroxide structure. In contrast, XPS analysis of amorphous Ni79.2Nb12.5Y8.3 showed signs of overcharging which resulted in Y segregating from the surface to form Y2O3. From these results it is found that the presence of Co in amorphous Ni-based alloys stabilizes β-NiOOH and allows Y to remain incorporated for providing synergistic benefits. The presence of Co and Y in amorphous Ni-based materials is shown to enhance the OER activity and provide excellent long-term cycling stability. These features, along with the high surface area achieved through SA-HEBM, provide a cost-effective and simple method for producing stable and active amorphous electrocatalysts for the oxygen evolution reaction in AEM water electrolysis. V. R. Stamenkovic, D. Strmcnik, P. P. Lopes, and N. M. Markovic, Nat. Mater., 16, 57–69 (2016). F. Lyu, Q. Wang, S. M. Choi, and Y. Yin, Small, 15, 1804201 (2019). I. Roger, M. A. Shipman, and M. D. Symes, Nat. Rev. Chem., 1, 1–13 (2017). D. Y. Chung et al., Nat. Energy, 5, 222–230 (2020). T. N. Lambert et al., Chem. Commun., 51, 9511–9514 (2015). K. M. Cole, D. W. Kirk, and S. J. Thorpe, J. Electrochem. Soc., 165, J3122–J3129 (2018). X. Sun, H. Hao, Z. Liu, F. Zhao, and J. Song, Resour. Conserv. Recycl., 149, 45–55 (2019). M. Kim, B. Lee, H. Ju, S. W. Lee, and J. Kim, Adv. Mater., 31, 1901977 (2019). G. Zhang, B. Wang, L. Li, and S. Yang, Small, 15, 1904105 (2019). S. Ghobrial, K. M. Cole, D. W. Kirk, and S. J. Thorpe, Electrocatalysis, 10, 680–689 (2019). 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".