Graphitic carbon nitride based new advanced materials for photocatalytic applications

2019 ◽  
Vol 16 ◽  
Author(s):  
Pankaj Raizada ◽  
Abhinandan Kumar ◽  
Pardeep Singh

Background: The Present Scenario Of Rapid Industrial And Population Growth Has Become A Serious Threat To Environmental And Energy Concerns. Extremely Noxious Pollutants Like Dyes, Heavy Metal Ions, Phenols, Antibiotics And Pesticides In Water Are The Reason Behind Deprived Water Quality Leading To Inadequate Access To Clean Water. Photocatalysis Is A Prominent Strategy For Environmental Remediation As Photocatalytic Materials Not Only Convert Solar Energy Into Usable Energy Expedient But Also Shows Potential Application In Pollutant Mitigation. An Effectual Photocatalytic System Must Possess Wide Visible Absorption Range, High Physio-Chemical Firmness, And Effective Space-Charge Separation Along With Strong Redox Ability. Polymeric Graphitic Carbon Nitride A Metal-Free Semiconductor Photocatalyst Has Outshined As A Robust Photocatalyst For Various Photocatalytic Applications. Method: Hybridizing Polymeric G-C3n4 With Other Semiconductor Photocatalysts Has Not Only Conquer The Limitations Related To Pristine G-C3n4 But Also Displayed Improved Photoactivity. Different Photocatalytic Systems Involving G-C3n4 Coupled Metal-Oxides, Metal-Free Systems And Complex Heterojunction Systems Are Reviewed. Moreover, An All-Embracing Study Based On G-C3n4 Based Nanocatalysts Is Explored Via Heterojunction Formation Taking G-C3n4 As One Component. Results: Photocatalytic Experiments Involving Photodegradation Of Pollutants, Revealed The Significance Of Metal-Free G-C3n4 In The Heterojunction System Which Remarkably Boost The Photoactivity Through Effective Separation And Migration Of Photocarriers. Moreover, From Recyclability Experiments, Exceptional Photostability Of G-C3n4 Based Photocatalysts Was Observed. Photocatalytic Pollutant Degradation Is A Complex Phenomenon Which Requires Significant Experimental Techniques To Support The Mechanism. With The Help Of Photoelectrochemical Analysis, The Mechanisms Behind Photodegradation Can Be Evaluated And Explored. Conclusion: Metal-Free Polymeric G-C3n4 Is A Potential Semiconductor Photocatalyst Which Can Be Optimally Utilized For Wastewater Treatment. Coupling G-C3n4 With Another Semiconductor Material With An Appropriate Band Edge Can Effectively Enhance The Photocatalytic Efficacy. Herein, G-C3n4 Derived Metal-Oxide, Metal-Free And Complex Heterojunction Systems Are Explored And Their Photocatalytic Efficiency Is Evaluated For Pollutant Degradation. However, More Effective Research Efforts Are Needed For Large-Scale Applications Of G-C3n4 Based Photocatalysts.

Author(s):  
Lu Liu ◽  
Luo-fu Min ◽  
Wen Zhang ◽  
Yu-xin Wang

As a metal-free photocatalyst and electrocatalyst, graphitic carbon nitride (g-C3N4) has been intensively researched in a variety of applications ranging from energy storage/conversion to environmental remediation. Herein, we report that...


Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 411
Author(s):  
Taoreed O. Owolabi ◽  
Mohd Amiruddin Abd Rahman

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.


Sign in / Sign up

Export Citation Format

Share Document