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Tuning the reactivity of carbon surfaces with oxygen-containing functional groups
by
de Mello, Matheus Dorneles
, Anibal Boscoboinik, J.
, Kots, Pavel A.
, Shaw, Wendy J.
, Vlachos, Dionisios G.
, Yang, Piaoping
, Chen, Ying
, Zhou, Jiahua
, Quinn, Caitlin M.
, Cohen, Maximilian
, Caratzoulas, Stavros
, Zheng, Weiqing
in
119/118
/ 140/131
/ 140/133
/ 140/146
/ 639/301/299
/ 639/638/542
/ 639/638/563/979
/ 639/638/77/887
/ Acetonitrile
/ Aromatic compounds
/ Carbon
/ Catalysts
/ Density functional theory
/ Electron density
/ Functional groups
/ Graphitization
/ Heterogeneous catalysis
/ Humanities and Social Sciences
/ Learning algorithms
/ Machine learning
/ Materials for energy and catalysis
/ MATERIALS SCIENCE
/ multidisciplinary
/ NMR
/ Nuclear magnetic resonance
/ Oxygen
/ Phenols
/ Science
/ Science (multidisciplinary)
/ Spinning (materials)
/ Surface chemistry
/ Surface properties
/ Tuning
2023
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Tuning the reactivity of carbon surfaces with oxygen-containing functional groups
by
de Mello, Matheus Dorneles
, Anibal Boscoboinik, J.
, Kots, Pavel A.
, Shaw, Wendy J.
, Vlachos, Dionisios G.
, Yang, Piaoping
, Chen, Ying
, Zhou, Jiahua
, Quinn, Caitlin M.
, Cohen, Maximilian
, Caratzoulas, Stavros
, Zheng, Weiqing
in
119/118
/ 140/131
/ 140/133
/ 140/146
/ 639/301/299
/ 639/638/542
/ 639/638/563/979
/ 639/638/77/887
/ Acetonitrile
/ Aromatic compounds
/ Carbon
/ Catalysts
/ Density functional theory
/ Electron density
/ Functional groups
/ Graphitization
/ Heterogeneous catalysis
/ Humanities and Social Sciences
/ Learning algorithms
/ Machine learning
/ Materials for energy and catalysis
/ MATERIALS SCIENCE
/ multidisciplinary
/ NMR
/ Nuclear magnetic resonance
/ Oxygen
/ Phenols
/ Science
/ Science (multidisciplinary)
/ Spinning (materials)
/ Surface chemistry
/ Surface properties
/ Tuning
2023
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Do you wish to request the book?
Tuning the reactivity of carbon surfaces with oxygen-containing functional groups
by
de Mello, Matheus Dorneles
, Anibal Boscoboinik, J.
, Kots, Pavel A.
, Shaw, Wendy J.
, Vlachos, Dionisios G.
, Yang, Piaoping
, Chen, Ying
, Zhou, Jiahua
, Quinn, Caitlin M.
, Cohen, Maximilian
, Caratzoulas, Stavros
, Zheng, Weiqing
in
119/118
/ 140/131
/ 140/133
/ 140/146
/ 639/301/299
/ 639/638/542
/ 639/638/563/979
/ 639/638/77/887
/ Acetonitrile
/ Aromatic compounds
/ Carbon
/ Catalysts
/ Density functional theory
/ Electron density
/ Functional groups
/ Graphitization
/ Heterogeneous catalysis
/ Humanities and Social Sciences
/ Learning algorithms
/ Machine learning
/ Materials for energy and catalysis
/ MATERIALS SCIENCE
/ multidisciplinary
/ NMR
/ Nuclear magnetic resonance
/ Oxygen
/ Phenols
/ Science
/ Science (multidisciplinary)
/ Spinning (materials)
/ Surface chemistry
/ Surface properties
/ Tuning
2023
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Tuning the reactivity of carbon surfaces with oxygen-containing functional groups
Journal Article
Tuning the reactivity of carbon surfaces with oxygen-containing functional groups
2023
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Overview
Oxygen-containing carbons are promising supports and metal-free catalysts for many reactions. However, distinguishing the role of various oxygen functional groups and quantifying and tuning each functionality is still difficult. Here we investigate the role of Brønsted acidic oxygen-containing functional groups by synthesizing a diverse library of materials. By combining acid-catalyzed elimination probe chemistry, comprehensive surface characterizations,
15
N isotopically labeled acetonitrile adsorption coupled with magic-angle spinning nuclear magnetic resonance, machine learning, and density-functional theory calculations, we demonstrate that phenolic is the main acid site in gas-phase chemistries and unexpectedly carboxylic groups are much less acidic than phenolic groups in the graphitized mesoporous carbon due to electron density delocalization induced by the aromatic rings of graphitic carbon. The methodology can identify acidic sites in oxygenated carbon materials in solid acid catalyst-driven chemistry.
Distinguishing the influence of oxygen functional groups in carbon materials is important but elusive. Here, the authors combine experimental and machine learning techniques and reveal that phenolic groups are more acidic than carboxylic groups.
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