Band gap-tunable potassium doped graphitic carbon nitride with enhanced mineralization ability

2015 ◽  
Vol 44 (3) ◽  
pp. 1084-1092 ◽  
Author(s):  
Shaozheng Hu ◽  
Fayun Li ◽  
Zhiping Fan ◽  
Fei Wang ◽  
Yanfeng Zhao ◽  
...  
Keyword(s):  
Optical Properties ◽  
Photocatalytic Degradation ◽  
Crystal Lattice ◽  
Band Gap ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Structural And Optical Properties

Potassium doped into the g-C3N4 crystal lattice can tune the positions of the CB and VB potentials, influence the structural and optical properties and thus improve the photocatalytic degradation and mineralization ability.

scholarly journals DFT study of superhalogen and superalkali doped graphitic carbon nitride and its non-linear optical properties

RSC Advances ◽  
2021 ◽  
Vol 11 (14) ◽  
pp. 7779-7789 ◽  
Author(s):  
Asmat Ullah Khan ◽  
Rasheed Ahmad Khera ◽  
Naveed Anjum ◽  
Rao Aqil Shehzad ◽  
Saleem Iqbal ◽  
...  
Keyword(s):  
Optical Properties ◽  
Dft Calculations ◽  
Nonlinear Optical ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Dft Study ◽  
Nlo Properties ◽  
Non Linear ◽  
Linear Optical Properties

DFT calculations are carried out to investigate nonlinear optical (NLO) properties of superhalogen (BCl4) and superalkali (NLi4) doped graphitic carbon nitride (GCN).

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.

Explosive thermal exfoliation of intercalated graphitic carbon nitride for enhanced photocatalytic degradation properties

2019 ◽  
Vol 45 (3) ◽  
pp. 3643-3647 ◽  
Author(s):  
Kaixing Zhu ◽  
Yang Lv ◽  
Jing Liu ◽  
Wenjun Wang ◽  
Chunping Wang ◽  
...  
Keyword(s):  
Photocatalytic Degradation ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Degradation Properties
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