Ionic Salt-Mediated Tuning of the Morphology and Band Structure of Graphitic Carbon Nitride for NO Removal under Visible Light

2021 ◽  
Vol 4 (3) ◽  
pp. 2828-2839
Author(s):  
Hassan R. S. Abdellatif ◽  
Guan Zhang ◽  
Deti Xie ◽  
Jiupai Ni ◽  
Chengsheng Ni
Keyword(s):  
Band Structure ◽  
Visible Light ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
No Removal ◽  
Ionic Salt

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.

scholarly journals Electrostatic interaction mechanism of visible light absorption broadening in ion-doped graphitic carbon nitride

RSC Advances ◽  
2021 ◽  
Vol 11 (37) ◽  
pp. 22652-22660
Author(s):  
Zengyu Cen ◽  
Yuna Kang ◽  
Rong Lu ◽  
Anchi Yu
Keyword(s):  
Visible Light ◽  
Electrostatic Interaction ◽  
Light Absorption ◽  
Carbon Nitride ◽  
Interaction Mechanism ◽  
Graphitic Carbon ◽  
Electrostatic Attraction ◽  
Graphitic Carbon Nitride ◽  
Visible Light Absorption

H2O2 treated K-doped graphitic carbon nitride presents an enhanced visible light absorption, which is due to the electrostatic attraction between K ions and OOH ions inside graphitic carbon nitride.

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