Ligand Engineering in Nickel Phthalocyanine to Boost the Electrocatalytic Reduction of CO<sub>2</sub>
Bibliographic record
Abstract
Abstract Designing and synthesizing efficient molecular catalysts may unlock the great challenge of controlling the CO2 reduction reaction (CO2RR) with molecular precision. Nickel phthalocyanine (NiPc) appears as a promising candidate for this task due to its adjustable Ni active‐site. However, the pristine NiPc suffers from poor activity and stability for CO2RR owing to the poor CO2 adsorption and activation at the bare Ni site. Here, a ligand‐tuned strategy is developed to enhance the catalytic performance and unveil the ligand effect of NiPc on CO2RR. Theoretical calculations and experimental results indicate that NiPc with electron‐donating substituents (hydroxyl or amino) can induce electronic localization at the Ni site which greatly enhances the CO2 adsorption and activation. Employing the optimal catalyst—an amino‐substituted NiPc—to convert CO2 into CO in a flow cell can achieve an ultrahigh activity and selectivity of 99.8% at current densities up to −400 mA cm−2. This work offers a novel strategy to regulate the electronic structure of active sites by ligand design and discloses the ligand‐directed catalysis of the tailored NiPc for highly efficient CO2RR.
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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".