Efficient Energy Funneling in Spatially Tailored Segmented Conjugated Block Copolymer Nanofiber–Quantum Dot or Rod Conjugates
Bibliographic record
Abstract
Hybrid systems composed of conjugated polymers and inorganic semiconductor nanocrystals such as quantum dots (QDs) and nanorods (QRs) represent highly desirable multifunctional materials for applications from energy harvesting to light emission and sensing. Herein, we describe energy transfer studies between low-dispersity segmented conjugated polymer micellar nanofibers integrated with quantum dots that are spatially confined to discrete regions in the hybrid assembly via noncovalent interactions. The nanofibers were prepared from diblock copolymers with a crystallizable poly(di- n -hexylfluorene) (PDHF) core-forming block and different corona-forming blocks using the seeded-growth “living” crystallization-driven self-assembly method. The highly ordered crystalline PDHF core in the fibers functions as a donor and permits long-range exciton transport (>200 nm). Energy can therefore be funneled through the fiber core to QDs and QRs that function as acceptor materials and which are noncovalently bound to spatially defined coronal regions of poly(2-vinylpyridine) (P2VP) or quaternized polyfluorene (QPF). Using steady-state and time-resolved spectroscopy, we demonstrate that efficient energy transfer (over 70%) occurs from the crystalline PDHF donor core to the acceptor CdSe QRs attached at the fiber termini. The emission of the PDHF donor in the hybrid conjugate was extensively quenched (by 84%), and a subsequent 4-fold enhancement of the QR emission in solution was observed. These results indicate that the conjugates prepared in this work show promise for potential applications in fields such as light-emitting diodes, photovoltaics, chemical sensors, and photocatalysis.
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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".