Prediction of the Active Layer Nanomorphology in Polymer Solar Cells Using Molecular Dynamics Simulation
Bibliographic record
Abstract
Active layer nanomorphology is a major factor that determines the efficiency of bulk heterojunction polymer solar cells (PSCs). Synthesizing diblock copolymers in which acceptor and donor materials are the constituent blocks is the most recent method to control the structure of the active layer. In the current work, a computational method is proposed to predict the nanomorphology of the active layer consisting of a diblock copolymer. Diblock copolymers have a tendency to self-organize and form well-defined nanostructures. The shape of the structure depends on the Flory-Huggins interaction parameter (i.e., χ), the total degree of polymerization (N) and volume fractions of the constituent blocks (φi). In this work, molecular dynamics (MD) simulations were used to calculate χ parameters for two different block copolymers used in PSCs: P3HT-b-poly(S8A2)-C60 and P3HT-b-poly(n-butyl acrylate-stat-acrylate perylene) also known as P3HT-b-PPerAcr. Such calculations indicated strong segregation of blocks into cylindrical structure for P3HT-b-poly(S8A2)-C60 and intermediate segregation into cylindrical structure for P3HT-b-PPerAcr. Experimental results of P3HT-b-poly(S8A2)-C60 and P3HT-b-PTP4AP, a diblock copolymer having very similar structure to P3HT-b-PPerAcr, validate our predictions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".