From P‐type to N‐type: Peripheral fluorination of axially substituted silicon phthalocyanines enables fine tuning of charge transport
Bibliographic record
Abstract
Abstract Silicon phthalocyanines (R2‐SiPcs) are a family of promising tunable materials for organic electronic applications. We report the chemistry of the synthesis of axially substituted fluorinated SiPcs (tb‐Ph)2‐FxSiPc (where X = 0, 4, 8, or 16) and explore how the degree of fluorination effects optical and electronic properties. A new treatment with boron trichloride was included to obtain Cl2‐FXSiPcs from F2‐FXSiPcs, activating the axial position for further functionalization. We observed that as the degree of fluorination increased, so did the electron affinity of the compounds, leading to a drop in frontier orbital levels, as measured by electrochemistry and ultraviolet photoelectron spectroscopy (UPS). The deeper energy levels enabled successful (tb‐Ph)2‐F4SiPc and poly [[6,7‐difluoro[(2‐hexyldecyl)oxy]‐[5,8‐quinoxalinediyl]‐2,5‐thiophenediyl]] (PTQ10) blends for organic photovoltaics and photodetectors. All four compounds were incorporated in organic thin‐film transistors (OTFTs), where the degree of fluorination influenced device operation, changing it from p‐type conduction for (tb‐Ph)2‐F0SiPc, to ambipolar for (tb‐Ph)2‐F4SiPc, and n‐type for (tb‐Ph)2‐F8SiPc and (tb‐Ph)2‐F16SiPc. The OTFT devices made with (tb‐Ph)2‐F16SiPc achieved a low average threshold voltage of 7.0 V in N2 and retained its n‐type mobility when exposed to air.
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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".