3D Nanoscale Morphology Characterization of Ternary Organic Solar Cells
Bibliographic record
Abstract
It is highly desired to develop advanced characterization techniques to explore the 3D nanoscale morphology of the complicated blend film of ternary organic solar cells (OSCs). Here, ternary OSCs are constructed by incorporating the nonfullerene acceptor perylenediimide (PDI)-diketopyrrolopyrrole (DPP)-PDI and their morphology is characterized in depth to understand the performance variation. In particular, photoinduced force microscopy (PiFM) coupled with infrared laser spectroscopy is conducted to qualitatively study the distribution of donor and acceptors in the blend film by chemical identification and to quantitatively probe the segmentation of domains and the domain size distribution after PDI-DPP-PDI acceptor incorporation by PiFM imaging and data processing. In addition, the energy-filtered transmission electron microscopy with energy loss spectra is utilized to visualize the nanoscale morphology of ultrathin cross-sections in the configuration of the real ternary device for the first time in the field of photovoltaics. These measurements allow to "view" the surface and cross-sectional morphology and provide strong evidence that the PDI-DPP-PDI acceptor can suppress the aggregation of the fullerene molecules and generate the homogenous morphology with a higher-level of the molecularly mixed phase, which can prevent the charge recombination and stabilize the morphology of photoactive layer.
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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".