Solution-based fullerene-free route enables high-performance green-selective organic photodetectors
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Ongoing developments in machine vision, wearables, and the Internet of Things have led to strong demand for easy-to-fabricate, color-selective photodetectors. Narrowband-absorption-type (NBA) printable organic photodetectors provide an attractive solution, given their spectral robustness and fabrication simplicity. However, a key remaining challenge to realizing their potential is to concurrently achieve high photoconversion efficiency and spectral selectivity. Herein, this challenge is tackled by investigating a non-fullerene-based route to green-selective, solution-based photodetectors. Soluble phthalocyanine acceptor PhO-Cl 6 BsubPc is considered due to its high absorption selectivity to green photons. Blends with soluble quinacridones are pursued to realize the ideal of a donor:acceptor layer selectively absorbing the target photons throughout its volume. A latent-pigment route to the solution-based deposition of linear trans -quinacridone (QA) enables well-intermixed QA:PhO-Cl 6 BsubPc layers. Green-selective photodetectors with cutting-edge performance are thus realized, achieving a 25% increase in external quantum efficiency compared to all prior solution-based NBA implementations, as well as a nearly five-fold enhancement of the green-to-blue spectral rejection ratio. The merit of this approach is further illustrated by comparison with the corresponding fullerene-based photodetectors. By demonstrating an approach to solution-based NBA photodetectors with cutting-edge photoconversion efficiency and spectral selectivity, this study represents an important step toward printable, high-performance organic color sensors and imagers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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 it