Computational Insights Into All‐Fused Ring Non‐Fullerene Acceptors for Enhanced Stability and Performance
Bibliographic record
Abstract
The chemical stability and performance of non-fullerene acceptors (NFAs) are critical for achieving high power conversion efficiency (PCE) and device stability. This study presents a novel computational design strategy for addressing key stability challenges. The photochemical stability is improved by removing the vinylene bridge between the core and end groups, which often causes degradation and photoisomerization. All-fused non-fullerene acceptors (AFNFAs) are designed by directly fusing high-performance end groups with core units such as Y6 (FY6) and ITIC (FITIC). Density functional theory (DFT) and molecular dynamics simulations show that the new molecules exhibit superior optoelectronic properties and favorable bulk-phase morphologies. The results also show highly ordered packing of acceptor dimers and efficient charge transport. Additionally, the voltage losses associated with exciton diffusion, dissociation, and energetic disorder in electron affinities are minimal. Overall, the proposed AFNFAs with imide end groups emerge as promising candidates for stable high-performance acceptors in organic solar-cell applications.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".