(Invited) Challenges in Quantifying the Purity of Semiconducting Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Semiconducting carbon nanotubes (sc-SWCNT) sit amongst the most promising nanomaterials for a new generation of electronic devices. Progress towards commercial applications is combined with challenges related to materials development and characterization, fabrication methods as well as device modeling. This talk will focus primarily on the enrichment process and the related ink formulation for printed electronics and integrated circuits. [1] The quality of carbon nanotube ink solutions is closely tied with the rapid feedback provided by optical spectroscopy methods: UV-vis, Raman and fluorescence. [2] I will describe our efforts on improving the material’s assessment when purity exceeds well beyond 99%. [3] [1] Ding, J.; Li, Z.; Lefebvre, J.; Cheng, F.; Dubey, G.; Zou, S.; Finnie, P.; Hrdina, A.; Scoles, L.; Lopinski, G. P.; Kingston, C. T.; Simard, B.; Malenfant, P. R. L. Enrichment of Large-Diameter Semiconducting SWCNTs by Polyfluorene Extraction for High Network Density Thin Film Transistors. Nanoscale 2014, 6, 2328-2339. [2] J. Lefebvre, J.; Finnie, P.; Fagan, J.; Zheng, M.; Hight Walker, A. R. Metrological Assessment of Single-Wall Carbon Nanotube Materials by Optical Methods. Handbook of Carbon Nanomaterials 2019, 9, R. B. Weisman and J. Kono editors (World Scientific) ISBN 978-981-3235-45-8. [3] Lefebvre, J.; Ding, J.; Li, Z.; Finnie, P.; Lopinski, G.; Malenfant, P. R. L. High-Purity Semiconducting Single-Walled Carbon Nanotubes: A Key Enabling Material in Emerging Electronics. Acc. Chem. Res. 2017, 50 (10), 2479-2486. Figure 1
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.010 |
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".