Comparing electrocatalytic oxygen evolution using laser-fabricated NiO and CuO – A look at the influence of fabrication, kinetics and the inevitable phase transitions
Bibliographic record
Abstract
• ULPING produced binder-free nanoporous NiO and CuO electrodes with distinct morphologies and mixed oxidation states. • NiO demonstrated superior OER activity with lower overpotential, faster kinetics, and reduced charge-transfer resistance compared to CuO. • Oxidation-state transitions under applied potential shows Ni³⁺ and Cu³⁺ as the main OER-active phases, but further oxidation to Ni⁴⁺ or unstable Cu³⁺ species caused metastability and degradation. • Phase-diagram modelling and Pourbaix diagram predict stable NiOOH formation under OER conditions, while Cu phases seem metastable, and future operando XRD/XAS with DFT defect-engineering will track and optimize catalytic transformations. The oxygen evolution reaction (OER) is the principal energy barrier in water electrolysis, often demanding overpotentials exceeding 400 mV on state-of-the-art catalysts and thus limiting sustainable hydrogen production. Here, we compare nanoporous NiO and CuO electrodes fabricated via ultrashort-pulse laser irradiation (ULPING) directly onto metal foils. By tuning pulse duration (∼150 ps), repetition rate (1.2 MHz), power (10 W) and scan speed (50 mm/s), we produced extensive hierarchical pore networks without binders or post-treatment. SEM and EDX mapping confirm distinct morphologies—elongated, fibrous ridges in CuO vs. globular, cauliflower-like clusters in NiO—and near-stoichiometric metal-to-oxygen ratios, with subsurface non-stoichiometries revealed by XPS (mixed Cu¹⁺/Cu²⁺ and Ni²⁺/Ni³⁺ species, abundant surface hydroxyls). In 1 M KOH, iR-corrected LSVs show NiO achieves 10 mA·cm⁻² at 430 mV and sustains 200 mA·cm⁻² at <800 mV, outperforming CuO (η₁₀ ≈ 511 mV). Tafel slopes of 127 mV/dec (NiO) vs. 168 mV/dec (CuO) reflect faster charge-transfer kinetics, corroborated by exchange current densities (j₀ ≈ 4.1 × 10⁻³ vs. 9.2 × 10⁻³ mA cm⁻²). EIS fits reveal lower R ct and diffusion impedance for NiO, while representative free-energy diagrams illustrate how transition-state positioning governs kinetic asymmetry. Theory-guide information, such as phase-diagram modeling (Ni–O–H, Cu–O–H at 300 K) predicts stable NiOOH formation under OER conditions, contrasting with metastable Cu(OH)₂ that rapidly reverts to CuO/Cu₂O. Furthermore, this work highlights that transition metal oxides inevitably undergo oxidation-state transitions under applied potential, with the +3 states of Ni and Cu emerging as the most active for OER but prone to over-oxidation and instability at higher potentials. It emphasizes the importance of experimentally and theoretically tracking these dynamic oxidation-state transformations to distinguish between catalytic activity and degradation mechanisms. Future work will focus on operando XRD/XAS to track these dynamic phase changes and DFT-guided defect engineering to further optimize earth-abundant OER catalysts for large-scale green hydrogen.
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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 | 0.001 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".