Bay-Region, Nitrogen Functionalized Perylene Diimides for Printed Electronic Sensors
Bibliographic record
Abstract
The chemistry of organic conjugated materials is rich. Chemical structures can be infinity tuned to tailor optical, electronic, and physical properties. With an ability to transport electrical charge, such materials have found widespread use in electronic devices ranging from solar cells to sensors, so called ‘organic electronics. As organic electronic technologies move towards commercialization there becomes a focus on cost and sustainably. Perylene diimide (PDI) is a classic organic dye that has been well manipulated and studied in organic electronics and championed as chemically versatile, high-performance, and accessible (i.e. low-cost). While many electronic devices featuring PDI as an active electronic component have been engineered to achieved high performance, sustainably (i.e. greenness) has often been overlooked. Our research team at the university of Calgary is a member of the Canadian NSERC Green Electronics Network (GreEN) developing new materials and processes for sustainable organic electronic devices. This presentation will showcase two new PDI dyes, modified with active pyrrole or amine functional groups to produce electronically active, large area, green solvent roll-coated films. The materials design, chemistry, properties, and utility as electronic amine sensors, fully roll coated, will be discussed. References: Flexible dual-action colorimetric-electronic amine sensors based on N-annulated perylene diimide dyes. RSC Sensors and Diagnostics. Early View. 10.1039/D4SD00004H Organic heterojunction charge-transfer chemical sensors. Journal of Materials Chemistry C. 2024. 12, 5083-5087. An air-stable n-type bay-and-headland substituted bis-cyano N-H functionalized perylene diimide for printed electronics. Journal of Materials Chemistry C. 2021. 9, 13630-13634. Acid dyeing for green solvent processing of solvent resistant semiconducting organic thin films. Materials Horizons. 2020. 7, 2959-2969.
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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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".