Shellac as dielectric materials in organic field-effect transistors: from silicon to paper substrates
Bibliographic record
Abstract
Abstract Recent advances in the design and preparation of electroactive materials, particularly semiconducting and conductive polymers, have resulted in the creation of novel organic electronics with advanced functionality and performance competitive with that of devices made of silicon. With an increasing number of organic and printed electronics being engineered and produced at a larger scale, the environmental cost of the final organic electronic devices (life cycle, environmental impact, etc) needs to be considered. While e-waste is already a growing global problem, improving the sustainability of emerging electronics through a careful materials selection is highly desirable. In this work, we explore the use of shellac as a sustainable greener dielectric material in organic field-effect transistors. A careful examination of shellac in combination with diketopyrrolopyrrole-based semiconducting polymers was performed on rigid substrates through atomic force microscopy (AFM) and the fabrication of thin film transistors. All devices made from this green dielectric showed good performance and device characteristics. Building from this investigation, shellac was further integrated with paper substrates to fabricate paper-based thin film transistors. Thin film samples based on shellac on both silicon wafer and paper substrates were characterized by AFM to investigate solid-state morphology of shellac and selected semiconducting materials. Through careful optimization of the device architecture and processing time, device characteristics and performances on paper substrates (average charge mobilities and on/off current ratios) were comparable to those of devices prepared on silicon wafers, confirming that shellac, in combination with organic semiconducting polymers, can be an advantageous dielectric material to be used for the fabrication of greener and sustainable thin film electronics from renewable feedstocks and components.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.003 | 0.001 |
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 teacher head, 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".