Organic Thin Film Transistors with Boron Nitride Nanotube-integrated rr-P3HT Polymer Semiconductor
Bibliographic record
Abstract
Regioregular poly(3-hexylthiophene) (rr-P3HT) has become a preferred polymeric semiconductor material for organic electronic devices such as organic thin film transistors (OTFTs), photovoltaics, and phototransistors. However, its charge transport characteristics and air stability in pristine form, and adapting it for sensory applications, remains a challenge. In this work, we have introduced boron nitride nanotubes (BNNTs) as inorganic additives in rr-P3HT and examined the performance of the resultant solution-processed rr-P3HT-based OTFTs. Different loadings of BNNTs were integrated in pristine rr-P3HT to investigate the effect of BNNTs additives on semiconductor behavior and the OTFT device characteristics. In addition, we have compared the air stability of the pristine rr-P3HT OTFTs and the hybrid rr-P3HT: BNNTs OTFTs, when the semiconductor layer is annealed under different environmental conditions including vacuum annealing, over a one-month period. The results obtained indicate formation of a vertical separation layer within the hybrid rr-P3HT: BNNTs devices, leading to superior air stability and extended shelf-life, along with comparable electronic characteristics for the high BNNTs loading in comparison to the pristine rr-P3HT OTFT devices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".