Graphene induced electrical percolation enables more efficient charge transport at a hybrid organic semiconductor/graphene interface
Bibliographic record
Abstract
Self-assembly of semiconducting polymer chains during crystallization from a liquid or melt dictates to a large degree the electronic properties of the resulting solid film. However, it is still unclear how charge transport pathways are created during crystallization. Here, we performed complementary in situ electrical measurements and synchrotron grazing incidence X-ray diffraction (GIXD), during slow cooling from the melt of highly regio-regular poly(3-hexylthiophene) (P3HT) films deposited on both graphene and on silicon. Two different charge transport mechanisms were identified, and were correlated to the difference in crystallites' orientations and overall amount of crystallites in the films on each surface as molecular self-assembly proceeded. On silicon, a weak charge transport was enabled as soon as the first edge-on lamellae formed, and further increased with the higher amount of crystallites (predominantly edge-on and randomly oriented lamellae) during cooling. On graphene however, the current remained low until a minimum amount of crystallites was reached, at which point interconnection of conducting units (face-on, randomly oriented lamellae and tie-chains) formed percolated conducting pathways across the film. This lead to a sudden rapid increase in current by ≈10 fold, and strongly enhanced charge transport, despite a much lower amount of crystallites than on silicon.
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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".