Soluble Ruthenium Phthalocyanines as Semiconductors for Organic Thin‐Film Transistors
Bibliographic record
Abstract
Abstract Ruthenium phthalocyanine (RuPcs) are multipurpose compounds characterized by their remarkable reactivity and photoelectronic properties, which yield a broad synthetic scope and easy derivatization at the axial position. However, RuPcs have been underexplored for use in organic thin‐film transistors (OTFTs), and therefore new studies are necessary to provide basic insight and a first approach in this new application. Herein, two novel RuPc derivatives, containing axial pyridine substituents with aliphatic chains (RuPc(CO)(PyrSiC6) (1) and RuPc(PyrSiC6)2 (2), were synthesized, characterized, and tested as the organic semiconductor in OTFTs. RuPc thin‐films were characterized by X‐ray diffraction (XRD), and atomic force microscopy (AFM) to assess film morphology and microstructure. 1 displayed comparable p‐type device performance to other phthalocyanine‐based OTFTs of similar design, with an average field effect mobility of 2.08×10−3 cm2 V−1 s−1 in air and 1.36×10−3 cm2 V−1 s−1 in nitrogen, and threshold voltages from −11 V to −20 V. 2 was found to be non‐functional as the semiconductor in the device architecture used, likely as a result of significant differences in thin‐film formation. The results of this work illustrate a promising starting point for future development of RuPc electronic devices, particularly in this new family of 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 | 0.000 | 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".