C F bonding in fluorinated N-Doped carbons
Bibliographic record
Abstract
Porous carbons are used in various applications for energy storage. Nitrogen doping of these carbons modifies their electrochemical and chemical properties and co-doping them with fluorine atoms, appears as a promising route to further tailor their physical and chemical properties. The present paper focuses on the gas/solid fluorination with molecular fluorine (F2) of various types of N-doped porous carbons. The consequences of the fluorination on the porosity of these materials were studied as well as their CF bonding type. Mild conditions avoid a huge decomposition in F2 gas of these materials and a drastic decrease of their specific surface area. Micropores, which are hosting most of the FeNx catalytic sites, are the most affected by fluorination, and a new N1s XPS peak assigned to pyridinic-N---CF has been identified, coinciding with that of the XPS binding energy of N1s in FeNx. However, molecular fluorine did not react directly with nitrogen atoms in these materials, whatever their type since no NF containing volatile products were evolved during the treatment. Finally, a dual CF bonding, characterized by the coexistence of CF bonds with weakened covalence and covalent CF, is evidenced in all fluorinated N-doped porous carbons.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".