Toward Weak Epitaxial Growth of Silicon Phthalocyanines: How the Choice of the Optimal Templating Layer Differs from Traditional Phthalocyanines
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| 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 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".