Substituent-induced electronic localization of nickel phthalocyanine with enhanced electrocatalytic CO2 reduction
Bibliographic record
Abstract
Abstract Designing efficient catalysts with high activity and selectivity is desirable and challenging for CO2 reduction reaction (CO2RR). Nickel phthalocyanine (NiPc) is a promising molecule catalyst for CO2RR. However, the pristine NiPc suffers from poor CO2 adsorption and activation due to its electron deficiency of Ni–N4 site, which leads to inferior activity and stability during CO2RR. Here, we develop a substituent-induced electronic localization strategy to improve CO2 adsorption and activation, and thus catalytic performance. Theoretic calculations and experimental results indicate that the electronic localization on the Ni site induced by electron-donating substituents (hydroxyl or amino) of NiPc greatly enhances the CO2 adsorption and activation, which is positively associated with the electron-donating abilities of substituents. Employing the optimal catalyst of amino-substituted NiPc to catalyze CO2 into CO in flow cell can achieve an ultrahigh activity and selectivity of 99.8% at the current densities up to 400 mA cm-2. This work offers a novel strategy to regulate the electronic structure of the active site by introducing substituents 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".