Raman microscopy mapping for the purity assessment of chirality enriched carbon nanotube networks in thin-film transistors
Bibliographic record
Abstract
With recent improvements in carbon nanotube separation methods, the accurate determination of residual metallic carbon nanotubes in a purified nanotube sample is important, particularly for those interested in using semiconducting single-walled carbon nanotubes (SWCNTs) in electronic device applications such as thin-film transistors (TFTs). This work demonstrates that Raman microscopy mapping is a powerful characterization tool for quantifying residual metallic carbon nanotubes present in highly enriched semiconducting nanotube networks. Raman mapping correlates well with absorption spectroscopy, yet it provides greater differentiation in purity. Electrical data from TFTs with channel lengths of 2.5 and 5 µm demonstrate the utility of the method. By comparing samples with nominal purities of 99.0% and 99.8%, a clear differentiation can be made when evaluating the current on/off ratio as a function of channel length, and thus the Raman mapping method provides a means to guide device fabrication by correlating SWCNT network density and purity with TFT channel scaling.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".