Gas‐Phase Fluorination of Hexagonal Boron Nitride
Bibliographic record
Abstract
Hexagonal boron nitride (hBN) has received much attention in recent years as a 2D dielectric material with potential applications ranging from catalysts to electronics. hBN is a stable covalent compound with a planar hexagonal lattice and is relatively unreactive to most chemical environments, making the chemical functionalization of hBN challenging. Here, a simple, scalable strategy to fluorinate hBN using a direct gas-phase fluorination technique is reported. The nature of fluorine bonding to the hBN lattice and their chemical coordination are described based on various characterization studies and theoretical models. The fluorine functionalized hBN shows a bandgap reduction and displays a semiconducting behavior due to the fluorination process. Additionally, the fluorinated hBN shows significant improvement in its thermal and friction properties, which could be substantial in applications such as lubricants and thermal fluids. Theory and simulations reveal that the enhanced friction properties of fluorinated hBN result from reduced inter-planar interaction energy by electrostatic repulsion of intercalated fluorine atoms between hBN layers without significant disruption of the in-plane lattice. This technique paves the way for the fluorination of several other 2D structures for various applications such as magnetism and functional nanoscale electronic devices.
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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".