Batch‐to‐Batch Variation of Polymeric Photovoltaic Materials: its Origin and Impacts on Charge Carrier Transport and Device Performances
Bibliographic record
Abstract
A detailed investigation of the impact of molecular weight distribution of a photoactive polymer, poly[N‐9′‐heptadecanyl‐2,7‐carbazole‐alt‐5,5‐(4′,7′‐di‐2‐thienyl‐2′,1′,3′‐benzothiadiazole)] (PCDTBT), on photovoltaic device performance and carrier transport properties is reported. It is found that different batches of as‐received polymers have substantial differences in their molecular weight distribution. As revealed by gel permeation chromatography (GPC), two peaks can generally be observed. One of the peaks corresponds to a high molecular weight component and the other peak corresponds to a low molecular weight component. Photovoltaic devices fabricated with a higher proportion of low molecular weight component have power conversion efficiencies (PCEs) reduced from 5.7% to 2.5%. The corresponding charge carrier mobility at the short‐circuit region is also significantly reduced from 2.7 × 10−5 to 1.6 × 10−8 cm2 V−1 s−1. The carrier transport properties of the polymers at various temperatures are further analyzed by the Gaussian disorder model (GDM). All polymers have similar energetic disorders. However, they appear to have significant differences in carrier hopping distances. This result provides insight into the origin of the molecular weight effect on carrier transport in polymeric semiconducting materials.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".