Tunable filter Raman spectroscopy of purified semiconducting and metallic carbon nanotubes
Bibliographic record
Abstract
Tunable filter Raman spectroscopy is used to efficiently produce Raman excitation maps of unpurified and type-purified single walled carbon nanotubes (SWCNTs). Maps with fine excitation resolution (1 nm) are created over a wide wavelength range (727 to 980 nm), extending from metallic to semiconducting resonances. At a given wavelength, the wide bandwidth (>3,000 cm–1) allows the comparison of the G band with the radial breathing mode (RBM), and shows the 2D band and other less prominent bands. Materials examined included unsorted powders, aqueous sorted semiconductors, aqueous sorted metals, and polyfluorene sorted semiconductors in toluene. The Raman excitation profiles of the G band are broad, relative to the RBM bands. The maps offer evidence of minority species contamination, except in the case of the polyfluorene sorted semiconductors. Tunable Raman spectroscopy data help validate the simpler fixed wavelength Raman spectroscopy approaches to purity assessment.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 teacher head, 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".