Tailored Energy Funneling in Photocatalytic π-Conjugated Polymer Nanofibers for High-Performance Hydrogen Production
Bibliographic record
Abstract
The creation of artificial high-performance photosynthetic assemblies with a tailorable antenna system to deliver absorbed solar energy to a photosynthetic reaction center, thereby mimicking biological photosynthesis, remains a major challenge. We report the construction of recyclable, high-performance photosynthetic nanofibers with a crystalline π-conjugated polyfluorene core as an antenna system that funnels absorbed solar energy to spatially defined sensitized Co(II) porphyrin photocatalysts for the hydrogen evolution reaction. Highly effective energy funneling was achieved by tuning the dimensions of the nanofibers to exploit the very long exciton diffusion lengths (>200 nm) associated with the highly crystalline polyfluorene core formed using the living crystallization-driven self-assembly seeded growth method. This enabled efficient solar light-driven hydrogen production from water with a turnover number of over 450 for 8 h of irradiation, an H 2 production rate of ca. 65 mmol h –1 g –1, and an overall quantum yield of 0.4% in the wavelength region (<405 nm) beyond the absorption of the molecular photocatalyst. The strategy of using a tailored antenna system based on π-conjugated polymers and maximizing exciton transport to a reaction center reported in this work opens up future opportunities for potential applications in other fields such as solar overall water splitting, CO 2 reduction, and photocatalytic small molecule synthesis.
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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".