Molecular-Level Tailoring of Energy Structure in Ternary Conjugated Polymers with a Built-in Ru-Complex Catalyst for Efficient CO<sub>2</sub> Reduction Photocatalysis
Bibliographic record
Abstract
Efficient catalytic CO 2 conversion by harnessing visible-light energy is a substantial challenge for sustainability. Photocatalytic materials consisting of light absorbers and catalysts have been extensively developed. However, efficient photocatalytic systems have so far relied on the use of precious metal-based compounds or materials as light-absorbing components, primarily because of their long-lived photoexcited states. Herein, we report the design principles of ternary conjugated polymers as a metal-free light absorber with a built-in metal complex catalyst for substantially activating CO 2 reduction photocatalysis. The ternary conjugated system enabled exceedingly flexible tuning of their energy structure, which is beneficial for long-range charge separation by manipulating the photoexcited electrons to the site-selectively introduced molecular catalyst center. The key cascade energy structure was tailored, and its impacts on photocatalysis were unveiled by using both spectroscopic experiments and theoretical calculations. The precise molecular design resulted in very active visible-light CO 2 reduction, even without the aid of a precious metal-based light absorber, recording an external quantum efficiency up to 32.2% and producing a concentrated formate (∼0.48 M).
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".