Inkjet-printed thin-film transistors using surfactant-based transition-metal dichalcogenide nanocomposites suspended in polymeric semiconductors
Bibliographic record
Abstract
Abstract Ink formulations containing a suspension of single-crystalline molybdenum disulfide (MoS 2 ) nanosheets suspended in the polymeric semiconductor poly(3-hexylthiophene-2,5-diyl) (P3HT) were inkjet printed for the fabrication of thin-film transistors (TFT). The MoS 2 nanosheets were treated with the surfactant trichloro(dodecyl)silane (DDTS) to functionalize the MoS 2 surface and created a more stable suspension, reducing the agglomeration of MoS 2 suspended in the P3HT solution. This ink formulation was inkjet printed onto the surface of thermal oxide coated, p + -Si wafers to form common-gate TFT device structures. The printed semiconductor formed the active region of a hybrid MoS 2 suspension in P3HT of the TFTs. The field-effect mobility for the hybrid-ink TFTs was found to be three times (3×) higher compared to reference devices using pristine P3HT without the suspension. The functionalized MoS 2 suspension was also found to form thinner nanosheet suspensions within the P3HT matrix that resulted in approximately 60% higher field-effect mobility compared to hybrid inks without the surfactant. The enhancement of the electrical properties of the TFTs was determined to be due to a structural change in the thin-film semiconductor. The observed current–voltage ( I – V ) changes were correlated to measurable structural alterations in the semiconductor thin film characterized by x-ray diffraction, atomic force microscopy, and UV–visible absorption spectroscopy.
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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".