Air processed, high open‐circuit voltage indoor organic photovoltaic cells based on side chain modified N‐annulated perylene diimides
Bibliographic record
Abstract
Abstract To achieve high‐performance indoor organic photovoltaics (OPVs), it is important to match the photoactive layer optical absorption with the light‐source emission. This can be accomplished by developing organic photoactive materials that can efficiently absorb visible light and thus minimize energy losses. While indoor OPVs have achieved efficiencies above 33% under low light intensities, the power output is limited by low open circuit voltages ( V OC ), often well below 1 V. In this study, we present a series of visible‐light absorbing (energy gap >1.90 eV) non‐fullerene acceptors (NFAs) based on perylene diimide dimers, which have been systematically modified with side chains of varying polarity and steric bulk (trimethyl benzyl, ethyl adamantane, trialkoxyl phenyl, and oligo ethylene glycol). Our results show that the incorporation of sterically bulky side chains such as ethyl adamantane and trimethyl benzyl, blended with the common widegap polymer PTQ10, provides photoactive layers with absorption greater than 2.0 eV, and consequently, V OC s higher than 1.2 V are achieved under AM 1.5 G illumination. Importantly, we found that the NFA with ethyl adamantane based side chains (tPDI 2 N‐ethyl adamantane, compound 4) exhibited the best performance, with minimized energy loss. As a result, devices using PTQ10:tPDI 2 N‐ethyl adamantane photoactive layers demonstrated excellent indoor efficiencies of over 16% and 18 μW cm −2 power output under a 2700 K LED lamp at 300 lux, and showed better repeatability compared to other systems. The PTQ10:tPDI 2 N‐ethyl adamantane based devices maintained a high V OC (>1.0 V) across a wide range of indoor lighting conditions, including 2700 K and 6500 K LED lamps. Overall, this work provides a sidechain engineering method to create NFAs for efficient indoor OPV devices.
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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".