Thin-Film Engineering of Solution-Processable n-Type Silicon Phthalocyanines for Organic Thin-Film Transistors
Bibliographic record
Abstract
Metal and metalloid phthalocyanines are an abundant and established class of materials widely used in the dye and pigment industry as well as in commercial photoreceptors. Silicon phthalocyanines (SiPcs) are among the highest-performing n-type semiconductor materials in this family when used in organic thin-film transistors (OTFTs) as their performance and solid-state arrangement are often increased through axial substitution. Herein, we study eight axially substituted SiPcs and their integration into solution-processed n-type OTFTs. Electrical characterization of the OTFTs, combined with atomic force microscopy (AFM), determined that the length of the alkyl chain affects device performance and thin-film morphology. The effects of high-temperature annealing and spin coating time on film formation, two key processing steps for fabrication of OTFTs, were investigated by grazing-incidence wide-angle X-ray scattering (GIWAXS) and X-ray diffraction (XRD) to elucidate the relationship between thin-film microstructure and device performance. Thermal annealing was shown to change both film crystallinity and SiPc molecular orientation relative to the substrate surface. Spin time affected film crystallinity, morphology, and interplanar d -spacing, thus ultimately modifying device performance. Of the eight materials studied, bis(tri- n- butylsilyl oxide) SiPc exhibited the greatest electron field-effect mobility (0.028 cm 2 V –1 s –1, a threshold voltage of 17.6 V) of all reported solution-processed SiPc derivatives.
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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) | 0.000 | 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".