Sequentially <i>N</i>‐Doped Acceptor Primer Layer Facilitates Electron Collection of Inverted Non‐Fullerene Organic Solar Cells
Bibliographic record
Abstract
Abstract In most non‐fullerene organic solar cells comprising bulk‐heterojunction active layers, the inter‐domain connectivity of small‐molecule acceptors is generally inferior to those of polymeric donors due to their intrinsic short‐range ordering. This issue is even exacerbated by the physiochemical mismatch between acceptor‐phases and metal‐oxide electron transport layers in most inverted n‐i‐p devices, leading to inefficient electron collection. By pre‐depositing an ultra‐thin acceptor primer layer, it develops a novel acceptor‐enriched‐bottom active layer to reinforce the acceptor‐phase continuity. It is however challenging to preserve the primer layer during non‐orthogonal solvent processing. Thus, sequential n ‐type doping is implemented on the surface of the primer layer, which allows to slightly reduce the acceptor solubility by polarity regulation, as well as stabilize the film structure via strong π–π interaction between dopant/host acceptor. Upon acceptor enrichment, higher interfacial electron density enhances the built‐in potential while the enlarged domains suppress both charge‐transfer state and bimolecular recombination. Consequently, the champion device efficiency is greatly improved from ca. 16.1% to 18.0%, mainly resulting from the simultaneously elevated fill factor and short‐circuit current density.
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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".