Enhanced Urea Oxidation Reaction through Layered Double Hydroxides: Insights from ZIF-67-Derived Nanostructures
Bibliographic record
Abstract
Layered double hydroxides (LDHs) are ionic layered compounds characterized by anion-containing intermediate regions within positively charged brucite-like layers. LDHs have shown high electrochemical activity in energy conversion systems such as batteries and fuel cells. In this study, we developed a hierarchically porous nanostructure derived from zeolitic imidazolate framework-67, which was subsequently transformed into an LDH structure with varying Ni concentrations. We precisely controlled the Ni-to-Co ratio within the LDH structure and investigated how different mole fractions of Co and Ni influence catalytic activity and selectivity for the electrochemical urea oxidation reaction (UOR). LDH structures with low Ni content (up to 40%) demonstrated high activity and selectivity for O 2 due to their structural instability and the predominant oxygen evolution reaction (OER) originating from ZIF-67. In contrast, LDHs with high Ni content (over 60%) supressed OER and exhibited enhanced activity for UOR. The resulting hollow structure with an expanded electrochemically active surface in LDHs with high Ni content could improve mass transport and diffusion at the electrode interface, leading to better reaction kinetics and higher current densities. These findings provide a foundational design guideline for metal–organic framework-derived nanostructure in UOR.
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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) | 0.000 | 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".