Silicon Phthalocyanines for n-Type Organic Thin-Film Transistors: Development of Structure–Property Relationships
Bibliographic record
Abstract
Silicon phthalocyanines (SiPcs) have shown great potential as n-type or ambipolar organic semiconductors in organic thin-film transistors (OTFTs) and organic photovoltaics. Although properly designed SiPcs rival current state-of-the-art n-type organic semiconducting materials, relatively few structure–property relationships have been established to determine the impact of axial substituents on OTFT performance, hindering the intelligent design of the next generation of SiPcs. To address this omission, we have developed structure–property relationships for vapor-deposited SiPcs with phenoxy axial substituents. In addition to thorough electrical characterization of bottom-gate top-contact OTFTs, we extensively investigated SiPc thin films using X-ray diffraction, atomic force microscopy (AFM), grazing-incidence wide-angle X-ray scattering (GIWAXS), and density functional theory (DFT) modeling. OTFT performance, including relative electron mobility (μ e ) of materials, was in general agreement with values obtained through DFT modeling including reorganization energy. Another significant trend observed from device performance was that increasing the electron-withdrawing character of the axial pendant groups led to a reduction in threshold voltage ( V T ) from 47.9 to 21.1 V. This was corroborated by DFT modeling, which predicted that V T decreases with the square of the dipole induced at the interface between the SiPc pendant and substrate. Discrepancies between modeling predictions and experimental results can be explained through analysis of thin-film morphology and orientation by AFM and GIWAXS. Our results demonstrate that a combination of DFT modeling to select prospective candidate materials, combined with appropriate processing conditions to deposit molecules with a favorable thin-film morphology in an “edge-on” orientation relative to the substrate, yields high-performance n-type SiPc-based OTFTs.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".