Carbon Nanotubes Modified with Fluorine
Bibliographic record
Abstract
One of the most remarkable properties of carbon is the ability of its atoms to combine with other elements allowing a great diversity of nanostructures, e.g., carbon nanotubes (CNTs). The unique properties of these materials, such as good electrical conductivity, chemical stability, light weight, and ease of handling make them suitable materials for electrochemical applications. Recently, the insertion of heteroatoms in the carbon structure have been used in order to modify and enhance the CNTs physical and chemical properties. The present study shows the synthesis and characterization of carbon nanotubes modified with fluorine (CNTs/F). The materials were synthesized by a modified chemical vapor deposition using toluene as carbon source and ferrocene as metal catalyst for the nanotubes growth. Fluorobenzene was used as a precursor of fluorine. During the process, parameters such as synthesis temperature (900°C - 1000°C) and fluorobenzene concentration in the toluene solution (20 - 80 g/L) were varied. The effects of these factors were investigated using high-resolution scanning electron microscopy and x-ray microanalysis by energy dispersive spectroscopy (SEM-EDS), transmission electron microscopy (TEM), x-ray diffraction (XRD), Raman spectroscopy, and x-ray photoelectron spectroscopy (XPS). The results showed that the morphological and physical properties of CNTs/F, such as wall thickness and defects changed in comparison to those of pristine CNTs. According to the Raman spectroscopy results, the composite materials showed major defects in the structure. These changes can be explained by the integration of the fluorine atoms in the structure of the nanotubes, which increase the disorder of the graphitic network. Morphology, elemental composition and chemical state of the carbon-fluorine bonds will be discussed as well as their effects in the electrochemical applications in lithium-ion batteries.
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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) | 0.000 | 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 | 0.000 | 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".