Insights into the Electrochemical Oxidation and Reduction of Nickel Oxide Surfaces
Bibliographic record
Abstract
Surface oxidation/reduction processes, driven by varying electrochemical potentials, can substantially impact catalyst effectiveness and, consequently, electrolyzer performance. This study combines theoretical and experimental approaches to explore the surface redox behavior of nickel oxides, which are cost-effective and efficient catalysts for many electrochemical reactions. Surface Pourbaix diagrams for three different phases of nickel oxides, i.e., nickel hydroxide (Ni(OH) 2 ), nickel oxyhydroxide (NiOOH), and nickel dioxide (NiO 2 ), were constructed using density functional theory-based simulations. Various experimental methods, including cyclic voltammetry, in situ Raman spectroscopy, and electrochemical titration, were employed to probe the surface redox processes of nickel oxide thin films. Our findings indicate that the ABAB stacking sequence of Ni(OH) 2 lacks stability under oxidizing conditions to host the surface oxidation (deprotonation) events, while the AABBCC stacking sequence of NiOOH is energetically favorable due to the presence of interlayer hydrogen bonding. Rapid charge transfer facilitated by interlayer hydrogen bonding accounts for the higher reactivity of partially oxidized/reduced NiOOH (001) surfaces compared to Ni(OH) 2 (001) and NiO 2 (001) surfaces with the same stoichiometry, where interlayer hydrogen bonding is absent. Insights presented in this work can offer guidelines for optimizing operational conditions and tailoring the surface structures and oxidation states of nickel oxides to enhance performance in applications such as electrocatalysis and supercapacitors.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".