Low activation energy field-effect transistors fabricated by bar-assisted meniscus shearing
Bibliographic record
Abstract
Here, we study the temperature-dependent transport properties of OFETs with the prototypical OSC 6,13-bis(triisopropylsilylethynyl)pentacene (TIPS-pentacene) co-processed with polystyrene (PS) as the active layer. The active layer is deposited directly on SiO2 using the bar-assisted meniscus shearing (BAMS) method. The co-processing with PS favors a consequential decrease in interfacial trap densities as previously reported. Furthermore, we demonstrate how this processing method leads to devices exhibiting activation energies well below the current state of the art for TIPS-pentacene on SiO2 or other dielectrics. Altogether, our study reports on TIPS-pentacene thin films exhibiting an activation energy (Ea) as low as 15 meV when the active material is blended with PS and processed via BAMS. Such an unprecedentedly low value originates not only from a decrease in the interfacial trap densities but also from trapping energies much shallower than previously reported elsewhere for the same material. This allows us to clarify the previously reported notion that significant passivation of interfacial traps occurs following the separation of PS from TIPS-pentacene into an individual layer at the interface with the insulator and to confirm that the enhanced transport originates from a synergistic effect wherein both trapping density and depth are reduced.
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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".