scholarly journals Ultrathin g-C3N4 nanosheets with an extended visible-light-responsive range for significant enhancement of photocatalysis

RSC Advances ◽  
2017 ◽  
Vol 7 (4) ◽  
pp. 2333-2341 ◽  
Author(s):  
Yanfang Yang ◽  
Jingjing Chen ◽  
Zhiyong Mao ◽  
Na An ◽  
Dajian Wang ◽  
...  
Keyword(s):  
Visible Light ◽  
Surface Area ◽  
Carbon Nitride ◽  
Light Response ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Significant Enhancement ◽  
Visible Light Response ◽  
Extended Region ◽  
Enhanced Surface

Ultrathin graphitic carbon nitride (UGCN) nanosheets with an extended region of visible light response and enhanced surface area were constructed for a significant enhancement in photocatalysis.

Preparation of Nitrogen-Deficient Graphitic Carbon Nitride with a Large Specific Surface Area by Melt Pretreatment and Its Photocatalytic Performance

NANO ◽  
2020 ◽  
Vol 15 (06) ◽  
pp. 2050079
Author(s):  
Xuelei Li ◽  
Jinfeng Bai ◽  
Jiaqi Li ◽  
Chao Li ◽  
Junru Zhang ◽  
...  
Keyword(s):  
Visible Light ◽  
Specific Surface ◽  
Surface Area ◽  
Specific Surface Area ◽  
Carbon Nitride ◽  
Mesoporous Structure ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Large Specific Surface Area ◽  
X Ray

In this study, nitrogen-deficient graphitic carbon nitride (M-LS-g-C3N4) with a mesoporous structure and a large specific surface area was obtained by calcination after melt pretreatment using urea as a precursor. X-ray diffraction (XRD), transmission electron microscopy (TEM), N2 adsorption, X-ray photoelectron spectroscopy (XPS), UV-Vis, ESR and photoluminescence (PL) were used to characterize the structure, morphology and optical performance of the samples. The TEM results showed the formation of a mesoporous structure on the 0.1[Formula: see text]M-LS-g-C3N4 surface. The porous structure led to an increase in the specific surface area from 41.5[Formula: see text]m2/g to 124.3[Formula: see text]m2/g. The UV-Vis results showed that nitrogen vacancies generated during the modification process reduced the band gap of g-C3N4 and improved the visible light absorption. The PL spectra showed that the nitrogen defects promoted the separation of photogenerated electron–hole pairs. In the visible light degradation of methyl orange (MO), the reaction rate constant of 0.1[Formula: see text]M-LS-g-C3N4 reached 0.0086[Formula: see text][Formula: see text], which was 5.05 times that of pure g-C3N4. Superoxide radicals and photogenerated holes were found to be the main active species in the reaction system. This study provides an efficient, green and convenient means of preparing graphitic carbon nitride with a large specific surface area.

Carbon nanodot decorated graphitic carbon nitride: new insights into the enhanced photocatalytic water splitting from ab initio studies

2015 ◽  
Vol 17 (46) ◽  
pp. 31140-31144 ◽  
Author(s):  
Guoping Gao ◽  
Yan Jiao ◽  
Fengxian Ma ◽  
Yalong Jiao ◽  
Eric Waclawik ◽  
...  
Keyword(s):  
Density Functional ◽  
Carbon Nitride ◽  
Light Response ◽  
Density Functional Theory Calculations ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Carbon Nanodots ◽  
Functional Theory ◽  
Visible Light Response ◽  
Hybrid Carbon

Density functional theory calculations reveal that hybrid carbon nanodots and graphitic carbon nitride can form a type-II van der Waals heterojunction, leading to significant reduction of band gap and enhanced visible light response.

scholarly journals Prediction of Band Gap Energy of Doped Graphitic Carbon Nitride Using Genetic Algorithm-Based Support Vector Regression and Extreme Learning Machine

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 411
Author(s):  
Taoreed O. Owolabi ◽  
Mohd Amiruddin Abd Rahman
Keyword(s):  
Genetic Algorithm ◽  
Visible Light ◽  
Band Gap ◽  
Carbon Nitride ◽  
Activation Function ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Support Vector ◽  
Learning Machine ◽  
Photocatalytic Applications

Graphitic carbon nitride is a stable and distinct two dimensional carbon-based polymeric semiconductor with remarkable potentials in organic pollutants degradation, chemical sensors, the reduction of CO2, water splitting and other photocatalytic applications. Efficient utilization of this material is hampered by the nature of its band gap and the rapid recombination of electron-hole pairs. Heteroatom incorporation due to doping alters the symmetry of the semiconductor and has been among the adopted strategies to tailor the band gap for enhancing the visible-light harvesting capacity of the material. Electron modulation and enhancement of reaction active sites due to doping as evident from the change in specific surface area of doped graphitic carbon nitride is employed in this work for modeling the associated band gap using hybrid genetic algorithm-based support vector regression (GSVR) and extreme learning machine (ELM). The developed GSVR performs better than ELM-SINE (with sine activation function), ELM-TRANBAS (with triangular basis activation function) and ELM-SIG (with sigmoid activation function) model with performance enhancement of 69.92%, 73.59% and 73.67%, respectively, on the basis of root mean square error as a measure of performance. The four developed models are also compared using correlation coefficient and mean absolute error while the developed GSVR demonstrates a high degree of precision and robustness. The excellent generalization and predictive strength of the developed models would ultimately facilitate quick determination of the band gap of doped graphitic carbon nitride and enhance its visible-light harvesting capacity for various photocatalytic applications.

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