Assessment of the Metallicity of Single-Wall Carbon Nanotube Ensembles at High Purities
Bibliographic record
Abstract
The purity of single-wall carbon nanotube (SWCNT) ensembles is critical, but assessing the purity in terms of metal vs semiconductor content is challenging at high purities. We describe possible bulk Raman spectroscopy based procedures to assess this metallicity and compare it to absorption procedures. A simple metric for metallicity is the G + band peak intensity ratio at two appropriate wavelengths. Related metallic G – band area derived metrics extend to higher purities. The G band signal scales linearly over orders of magnitude in concentration. However, the ratio-derived metrics may still be nonlinear because of resonance. Therefore, they break down if the tube diameter distributions differ greatly between samples, and other complications are possible. Additionally, the absolute Raman cross sections for each type of SWCNT can be estimated, and the abundance of semiconductors and metals can be tracked independently. Raman-derived metallicity figures of merit can meaningfully evaluate samples of purities which are not otherwise easily accessible, but there are important limitations to this simple approach to metallicity assessment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".