Enhancement of Carrier Mobilities of Organic Semiconductors on Sol–Gel Dielectrics: Investigations of Molecular Organization and Interfacial Chemistry Effects
Bibliographic record
Abstract
Abstract The dielectric‐semiconductor interfacial interactions critically influence the morphology and molecular ordering of the organic semiconductor molecules, and hence have a profound influence on mobility, threshold voltage, and other vital device characteristics of organic field‐effect transistors. In this study, p‐channel small molecule/polymer (evaporated pentacene and spin‐coated poly(3,3‴;‐didodecylquarterthiophene) – PQT) and n‐channel fullerene derivative ({6}‐1‐(3‐(2‐thienylethoxycarbonyl)‐propyl)‐{5}‐1‐phenyl‐[5,6]‐C61 – TEPP‐C61) show a significant enhancement in device mobilities ranging from ∼6 to ∼45 times higher for all classes of semiconductors deposited on sol–gel silica gate‐dielectric than on pristine/octyltrichlorosilane (OTS)‐treated thermally grown silica. Atomic force microscopy, synchrotron X‐ray diffraction, photoluminescence/absorption, and Raman spectroscopy studies provide comprehensive evidences that sol–gel silica dielectrics‐induced enhancement in both p‐ and n‐channel organic semiconductors is attributable to better molecular ordering/packing, and hence reduced charge trapping centers due to lesser structural defects at the dielectric‐semiconductor interface.
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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".