To Dope or Not To Dope: The Effect of Sonicating Single-Wall Carbon Nanotubes in Common Laboratory Solvents on Their Electronic Structure
Bibliographic record
Abstract
Single-wall carbon nanotubes (SWCNTs) are commonly dispersed via sonication in a solvent prior to functionalization. We show that solvents such as dichloromethane, chloroform, 1,2-dichloroethane, and o-dichlorobenzene lead to an upward shift in the Raman response of the SWCNTs. We have used o-dichlorobenzene as a model molecule to explain this effect, and an upward shift of 9 cm(-1) is observed in the D* band. This blue shift is associated with p-type doping and is triggered only when the nanotubes are sonicated in the solvent. Sonication decomposes the chlorinated solvents, and new species (Cl2 and HCl(g)) are formed. The catalytic Fe nanoparticles inherently present in the nanotubes are etched by chlorine and hydrogen chloride to form iron chlorides during sonication in the solvent. The dopant was identified by X-ray photoelectron spectroscopy. With such knowledge of doping, the choice of solvent becomes crucial for any chemical reaction and can be intentionally tuned to produce SWCNTs films for electronics applications.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".