Ambiguity in the Characterization of Chemically Modified Single-Walled Carbon Nanotubes: A Raman and Ultraviolet−Visible−Near-Infrared Study
Bibliographic record
Abstract
Single-walled carbon nanotubes (SWCNTs) sonicated in o -dichlorobenzene and benzyl chloride show anomalous behavior when characterized with a Raman microscope and ultraviolet−visible−near-infrared spectroscopy. SWCNTs treated with the aforementioned solvents lead to a small but distinct increase in the Raman D peak, when irradiated with laser power higher than 0.12 mW/μm 2 . This can be mistakenly interpreted as covalent functionalization, but we have correlated this increase in the D peak to the charring of polymeric material, which is formed during sonication of the aforementioned solvents. At a temperature estimated to be 280 °C, corresponding to a laser power of 0.31 mW/μm 2, the polymers are charred, resulting in an increase in amorphous material. This behavior is in contrast to that of the covalently functionalized SWCNTs, which show a decrease in the D peak as the laser power is increased. These samples also show a depletion in the spectral intensity of the optical absorption spectra of the SWCNTs, which is again a result commonly associated with covalent functionalization. However, by using a washing protocol, we find the Raman and optical spectra of the resulting SWCNTs no longer show features associated with functionalization. Species formed during sonication can drastically affect data interpretation. Our results provide an unambiguous assessment of the cause and effect of wet chemical processing and its impact on characterization.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".