Hybrid PCDTBT:PCBM:Graphene-Nanoplatelet Photoabsorbers
Bibliographic record
Abstract
We study the photoactive properties of poly [N-9′-heptadecanyl-2,7-carbazole-alt-5,5-(4′,7′di-2-thienyl-2′,1′,3′-benzothiadiazole)] (PCDTBT): [6,6]-Phenyl-C71-butyric acid methyl ester (PCBM) thin films under ultraviolet irradiation as well as their photo-oxidation, thermal and electrical properties. We compare their stability and performance with composites obtained by integrating graphene nanoplatelets (GNPs). Compared to the pristine polymer, an increase in light absorption and photoluminescence quenching were observed in PCDTBT:PCBM:GNPs. This indicates the occurrence of electron transfer from the conjugated polymer to the empty state of the GNP, through the interfacial bonding based on CH-π and ππ interactions, thereby reducing the photodegradation of the active layer. This is manifested by a significant decline in photo-oxidation, followed by improved thermal stability and suppressed phase separation and aggregation of the PCBM molecules. Atomic force microscopy imaging of the PCDTBT:PCBM:GNPs nanocomposite revealed that graphene flakes enhance the order of the polymeric structures. Finally, we discuss the effect of the GNPs content on the electrical conductivity and electron mobility of the photoactive layer. Our findings offer significant insights into the PV properties and photo physics of hybrid organic bulk-heterojunction solar cells, paving the way towards enhancing their durability and long term performance.
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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".