Molecular Engineering to Tune the Ligand Environment of Atomically Dispersed Nickel for Efficient Alcohol Electrochemical Oxidation
Bibliographic record
Abstract
Abstract Atomically dispersed metals maximize the number of catalytic sites and enhance their activity. However, their challenging synthesis and characterization strongly complicates their optimization. Here, the aim is to demonstrate that tuning the electronic environment of atomically dispersed metal catalysts through the modification of their edge coordination is an effective strategy to maximize their performance. This article focuses on optimizing nickel‐based electrocatalysts toward alcohol electrooxidation in alkaline solution. A new organic framework with atomically dispersed nickel is first developed. The coordination environment of nickel within this framework is modified through the addition of carbonyl (CO) groups. The authors then demonstrate that such nickel‐based organic frameworks, combined with carbon nanotubes, exhibit outstanding catalytic activity and durability toward the oxidation of methanol (CH 3 OH), ethanol (CH 3 CH 2 OH), and benzyl alcohol (C 6 H 5 CH 2 OH); the smaller molecule exhibits higher catalytic performance. These outstanding electrocatalytic activities for alcohol electrooxidation are attributed to the presence of the carbonyl group in the ligand chemical environment, which enhances the adsorption for alcohol, as revealed by density functional theory calculations. The work not only introduces a new atomically dispersed Ni‐based catalyst, but also demonstrates a new strategy for designing and engineering high‐performance catalysts through the tuning of their chemical environment.
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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".