Controllable defluorination of fluorinated graphene and weakening of C–F bonding under the action of nucleophilic dipolar solvent
Bibliographic record
Abstract
The effect of solvent on the chemical structure and properties of fluorinated graphene (FG) was particularly investigated in this work. It is found that the reduction of FG and the weakening of strong covalent C-F bonding take place under the action of some dipolar solvents even at room temperature. The rate of the C-F bond rupture reaction is positively influenced by the dipole moment of solvent and fluorine coverage of FG sheets. Meanwhile, defluorination of FG is controllable through the time and temperature of solvent treatment. These solvents function as the nucleophilic catalysts, promoting chemical transformation, which leads to a series of changes in the structure and properties of FG, such as a decline of fluorine concentration of about 40% and the reduction of thermal stability and band gap from 3 to 2 eV. After the treatment with dipolar solvent N-methyl-2-pyrrolidinone, FG maintained a capacity of 255 mA h g(-1) and a power density of 2986 W kg(-1) at a high discharge rate, while the pristine FG could not be discharged at all. This is called the "solvent activation" effect on the electrochemical performance of FG. The finding may draw attention to the effect of various external factors on the chemical structure and properties of FG, which is of great importance for the realization of the FG's potential.
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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) | 0.000 | 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 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".