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Record W3203474760 · doi:10.1002/adma.202106084

Gas‐Phase Fluorination of Hexagonal Boron Nitride

2021· article· en· W3203474760 on OpenAlexaff
Meiyazhagan Ashokkumar, Peter Serles, Devashish Salpekar, Eliezer Fernando Oliveira, Lawrence B. Alemany, Riqiang Fu, Guanhui Gao, Taib Arif, Róbert Vajtai, V. Swaminathan, Douglas S. Galvão, Valéry N. Khabashesku, Tobin Filleter, Pulickel M. Ajayan

Bibliographic record

VenueAdvanced Materials · 2021
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsCanada Research ChairsUniversity of TorontoUniversity of New Brunswick
FundersFundação de Amparo à Pesquisa do Estado de São PauloConselho Nacional de Desenvolvimento Científico e TecnológicoBaker Hughes
KeywordsMaterials scienceHexagonal boron nitrideFluorineBoron nitrideNanotechnologySurface modificationNanoscopic scalePlanarChemical physicsChemical bondIonic bondingHexagonal phaseGrapheneChemical engineeringHexagonal crystal systemCrystallographyIonOrganic chemistry

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.017
GPT teacher head0.329
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations36
Published2021
Admission routes1
Has abstractyes

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