Surpassing the Exciton Diffusion Limit in Single-Walled Carbon Nanotube Sensitized Solar Cells
Bibliographic record
Abstract
Semiconducting single-walled carbon nanotube (s-SWNT) light sensitized devices, such as infrared photodetectors and solar cells, have recently been widely reported. Despite their excellent individual electrical properties, efficient carrier transport from one carbon nanotube to another remains a fundamental challenge. Specifically, photovoltaic devices with active layers made from s-SWNTs have suffered from low efficiencies caused by three main challenges: the overwhelming presence of high-bandgap polymers in the films, the weak bandgap offset between the LUMO of the s-SWNTs and the acceptor C 60, and the limited exciton diffusion length from one SWNT to another of around 5 nm that limits the carrier extraction efficiency. Herein, we employ a combination of processing and device architecture design strategies to address each of these transport challenges and fabricate photovoltaic devices with s-SWNT films well beyond the exciton diffusion limit of 5 nm. While our solution processing method minimizes the presence of undesired polymers in our active films, our interfacial designs led to a significant increase in current generation with the addition of a highly doped C 60 layer (n-doped C 60 ), resulting in increased carrier separation efficiency from the s-SWNTs films. We create a dense interconnected nanoporous mesh of s-SWNTs using solution shearing and infiltrate it with the acceptor C 60 . Thus, our final engineered bulk heterojunction allows carriers from deep within to be extracted by the C 60 registering a 10-fold improvement in performance from our preliminary structures.
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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 | 0.001 | 0.001 |
| 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".