Bibliographic record
Abstract
The National Research Council (NRC) is Canada’s premier organization for research and technology development. NRC’s Printable Electronics (PE) program develops materials and inks for additive manufacturing in order to enable a sustainable PE sector in Canada. Progress has been made over the last 20 years, yet many challenges remain at the materials, fabrication and integration level, limiting performance and commercialization. This presentation covers our progress in high purity semiconducting single-walled carbon nanotube (sc-SWCNT) enrichment and transistor fabrication via solution based processes as well as conductive molecular ink development [1-11]. I will highlight our recent progress in understanding sc-SWCNT enrichment using conjugated polymers (isolation of high purity sc-SWCNT), with special consideration given to the effects of solvent parameters and doping on the mechanism and yield/purity of the final product [2-4]. Removal of the wrapping polymer using a dry process on-wafer will also be described [5]. Challenges and advances associated with using polymer-based dielectrics and encapsulants will be discussed as well [6-7]. Such transistor packages have enabled the realization of fully inkjet-printed transistors as a result of the excellent electrical properties of sc-SWCNTs [8]. A demonstration of a fully additive process to make TFT backplanes via R2R printing will also be highlighted. Developments in conductive molecular inks and progress towards the commercialization of new applications such as in-mold electronicswill also be described [9-11]. The development of new functional nanomaterials and their successful integration into devices will enable additive manufacturing of TFT arrays and in-mold electronics. [1] J. Lefebvre et al. Acc. Chem. Res. 50, 2479 (2017). [2] J. Ouyang et al. ACS Nano 12, 1910 (2018). [3] J. Ding et al. J. Phys. Chem. C 120, 21946 (2016). [4] Z. Li et al. ACS Omega 3, 3413 (2018). [5] Z. Li et al. Adv. Funct. Mater. 28, 1705568 (2018). [6] J. Lefebvre et al. Appl. Phys. Lett. 107, 243301 (2015). [7] F. Lapointe et al. Appl. Mater. Interfaces 11, 36027 (2019). [8] C. Homenick et al. ACS Appl. Mater. Interfaces 8, 27900, (2016) [9] A. J. Kell et al. ACS Appl. Mater. Interfaces 9, 17226 (2017). [10] C. Paquet et al. Nanoscale 10, 17226 (2018). [11] B. Deore et al. ACS Appl. Mater. Interfaces 11, 38880 (2019).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.374 | 0.164 |
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".