Surface-Emanated Vertical Organic Semiconducting Nanobrushes
Bibliographic record
Abstract
Polymer self-assembly offers an important route to construct well-defined nanostructures. However, it remains challenging to assemble polymers into vertically oriented nanostructures. Here, we use a seed-induced confinement self-assembly strategy to construct vertically aligned semiconducting nanobrushes from polyfluorene-based polymers on conductive substrates. Mechanism studies elucidate that the immobilized seeds on the substrate initiate the vertical growth of nanobrushes, and supercritical drying as well as the rigid charged coronas collectively contribute to retaining the vertical architecture. This process enables nanobrushes with ∼40× higher charge mobilities than their bulk film counterparts. Thus, inverted organic solar cells using the nanobrushes as the electron transporting layer (ETL) exhibit a record power conversion efficiency of 18.51% as a result of increased ETL texturing and the ETL-active layer interface favoring electron extraction. Moreover, our approach also enables the uniform growth of nanobrushes on a nanoporous photoanode (bismuth vanadate) for photoelectrochemical water splitting, improving catalyst distribution and electron transfer. Our work presents a feasible approach to fabricating challenging vertical polymer nanostructures, thereby unlocking the tremendous potential of conjugated polymers in optoelectronic applications.
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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".