Solution‐Based Integration of Vertically Stacked Organic Photodetectors Toward Easy‐To‐Fabricate Filterless Multi‐Color Light Sensors
Bibliographic record
Abstract
Abstract Solution‐processed, color‐selective organic photodetectors are uniquely positioned to deliver high‐performance, low‐cost, multicolor light sensors/imagers beyond the limitations of conventional, color‐filter‐based technologies. To realize such potential, however, a prominent challenge has been the solution‐based, monolithic integration of vertically stacked organic photodetectors, which would enable multicolor sensing with optimum light collection while benefiting from the scalability, cost, and sustainability edges of solution‐based manufacturing. To tackle this challenge, this paper demonstrates, for the first time, the monolithic integration of vertically stacked solution‐processed organic photodetectors for independent, multicolor light sensing within the same pixel area. The solvent orthogonality challenge is tackled by selecting polymer‐based photoactive layers and an insulating polymeric spacer—for independent biasing and photocurrent readout—with compatible processing conditions. Based on the suitable characteristics of blue‐ and green‐sensitive standalone devices, the vertically stacked, monolithic device architecture is optimized by also incorporating semitransparent electrodes for photons to reach deep into the stack. The resultant device architecture enables efficient blue‐ and green‐selective photodetection with state‐of‐the‐art linearity, alongside speed of response adequate for real‐world applications. Based on its solution‐processability and modularity, this approach paves the way for the facile, solution‐based fabrication of organic imagers covering multiple spectral regions with high sensitivity and resolution.
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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) | 0.001 | 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".