(Invited) Towards Industrialization of Boron Nitride Nanotubes: Purification and Quality Assessment
Bibliographic record
Abstract
Boron nitride nanotubes (BNNTs) exhibit as impressive a range of properties as their isoelectronic carbon nanotube (CNT) cousins, and include unique features like substantially higher thermal stability, a wide band gap, transparency through the visible region and better biocompatibility.1 Historically, very low production volumes had prevented the science and technology of BNNTs from evolving at even a fraction of the pace of CNTs. We helped to addressed this limitation through the development of an industrially scalable plasma process for the manufacturing of BNNT,2,3 which can synthesize kilogram quantities of highly crystalline, small diameter few-walled BNNTs daily. The next hurdles on the path to fully exploiting the properties of BNNTs in applications are precise purity and quality control and assessment. In this talk I will present our recent advances in these areas. We have developed a large-scale purification process that raises the purity of as-produced BNNT above 90% in a single-step.4 The process relies on the use of pure or diluted chorine gas at elevated temperature. We have evaluated the process at various temperatures, up to 1050 °C, and have used a range of imaging and spectroscopic assessments to qualify the results. Quality (purity + defect density) assessment of bulk nanotube samples has been a pervasive challenge in the field of carbon nanotubes for more than 30 years. To help address this challenge for BNNTs, I will show how the specific and strong interfacial interaction between regiorandom poly(3-hexylthiophene) (rra-P3HT) and BNNTs leads to the emergence of structured absorption and emission bands that can be used to quantify the relative quality of BNNT samples.5-7 Augustine, T. Cheung, V. Gies, J. Boughton, M. Chen, Z. J. Jakubek, S. Walker, Y. Martinez-Rubi, B. Simard and S. Zou, Nanoscale Advances, 1, 1914-1923 (2019). S. Kim, C. T. Kingston, A. Hrdina, M. B. Jakubinek, J. Guan, M. Plunkett and B. Simard, ACS Nano, 8, 6211-6220 (2014). S. Kim, M. Couillard, H. Shin, M. Plunkett, D. Ruth, C. T. Kingston and B. Simard, ACS Nano,12, 884-893 (2018). Cho, S. Walker, M. Plunkett, D. Ruth, R. Iannitto, Y. Martinez Rubi, K. S. Kim, C. M. Homenick, A. Brinkmann, M. Couillard, S. Dénommée, J. Guan, M. B. Jakubinek, Z. J. Jakubek, C. T. Kingston, and B. Simard, Chem. Mater., 32, 3911−3921 (2020). Martinez-Rubi, Z. J. Jakubek, M. B. Jakubinek, K. S. Kim, F. Cheng, M. Couillard, C. Kingston and B. Simard, J. Phys. Chem., C, 119, 26605 (2015). Martinez Rubi, Z. Jakubek, M. Chen, S. Zou, and B. Simard, ACS Applied Nano, 2, 2054-2063 (2019). Z. J. Jakubek, M. Chen, Y. Martinez Rubi, B. Simard, and S. Zou, J. Phys. Chem. Lett., 11, 4179−4185 (2020). 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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.034 | 0.034 |
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".