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Record W2200094663 · doi:10.1039/c5cp06914a

Controllable defluorination of fluorinated graphene and weakening of C–F bonding under the action of nucleophilic dipolar solvent

2015· article· en· W2200094663 on OpenAlexaff
Xu Wang, Weimiao Wang, Yang Liu, Mengmeng Ren, Huining Xiao, Xiangyang Liu

Bibliographic record

VenuePhysical Chemistry Chemical Physics · 2015
Typearticle
Languageen
FieldMaterials Science
TopicGraphene research and applications
Canadian institutionsUniversity of FrederictonUniversity of New Brunswick
Fundersnot available
KeywordsSolventNucleophileChemistryFluorineGrapheneDipoleSolvent effectsCovalent bondChemical bondComputational chemistryPhotochemistryPhysical chemistryCatalysisOrganic chemistryMaterials scienceNanotechnology

Abstract

fetched live from OpenAlex

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.

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 categoriesnone
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 score0.052
Threshold uncertainty score0.373

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)0.0000.000

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 head0.039
GPT teacher head0.296
Teacher spread0.257 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations55
Published2015
Admission routes1
Has abstractyes

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