(Invited) Reaction of Elemental Sulfur at the Surfaces of Carbon Nanotubes and Graphene
Bibliographic record
Abstract
The chemical functionalization of nanocarbons is key for ensuring their processability in solution and to derive new surface functionalities for applications. For this respect, the oxidation reactions of graphene and carbon nanotubes by strong oxidants is nowadays among the most significant to both clean the raw materials and add functional groups, such as what is present at the surfaces of the graphene oxides (e.g. GO and RGO) and oxidized carbon nanotubes. Because of the harsh conditions (e.g. strong acids), the oxidation process gives rise to a great variability of surface groups (e.g. epoxide, hydroxyl, carbonyl) from which the chemical behavior is rather complex. The reaction also leaves acidic residues in quantities that can be environmentally hazardous. Here, we explored an alternative approach to functionalize carbon nanotubes and graphene using a direct reaction with elemental sulfur. Depending on the conditions, we find that the sulfur atoms can modify irreversibly the surfaces with a covalent episulfide functions. The sulfur reaction is extensive, and it is therefore presented here as a green alternative for the chemical processing of nanocarbon materials.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.030 | 0.011 |
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".