Toward Weak Epitaxial Growth of Silicon Phthalocyanines: How the Choice of the Optimal Templating Layer Differs from Traditional Phthalocyanines
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Bibliographic record
Abstract
Weak epitaxial growth typically utilizes oligomeric or polymeric phenyls or thiophenes as a templating layer to improve the deposition of metal phthalocyanines (MPc) and other disk-like molecules. In this study, we report the use of perfluorinated para -sexiphenyl ( p -6PF) as a templating layer, for the fabrication of bis(pentafluorophenoxy) silicon phthalocyanine (F 10 -SiPc)-, copper phthalocyanine (CuPc)-, and perfluorinated copper phthalocyanine (F 16 -CuPc)-based organic thin-film transistors (OTFTs). By optimizing the deposition time and substrate temperature during deposition, we were able to control the surface coverage, roughness, and growth morphology of p -6PF leading to F 10 -SiPc OTFTs with n-type mobilities (μ) of 0.14 cm 2 V –1 s –1 . In comparison, using CuPc and F 16 -CuPc with p -6PF led to mobilities of 0.009 (holes) and 0.012 cm 2 V –1 s –1 (electrons), respectively. In contrast, an unfluorinated para -sexiphenyl ( p -6P) templating layer demonstrates inferior performance as a template for F 10 -SiPc while proving to be more effective for CuPc and F 16 -CuPc. Atomic force microscopy and powder X-ray diffraction suggest that higher surface coverage of the p -6PF layer increased the grain sizes and crystallinity of F 10 -SiPc. Grazing-incidence wide-angle X-ray scattering shows improved crystallinity of F 10 -SiPc on p -6PF over p -6P and vice versa for F 16 -CuPc. Overall, these results demonstrate that p -6PF is a promising templating candidate for F 10 -SiPc-based OTFTs and that the choice of the templating layer needs to be optimized for the semiconductor.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it