scholarly journals Mesoporous Graphitic Carbon Nitride-Based Nanospheres as Visible-Light Active Chemical Warfare Agents Decontaminant

ChemNanoMat ◽  
2016 ◽  
Vol 2 (4) ◽  
pp. 268-272 ◽  
Author(s):  
Dimitrios A. Giannakoudakis ◽  
Mykola Seredych ◽  
Enrique Rodríguez-Castellón ◽  
Teresa J. Bandosz
Keyword(s):  
Visible Light ◽  
Carbon Nitride ◽  
Chemical Warfare Agents ◽  
Chemical Warfare ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Active Chemical ◽  
Visible Light Active ◽  
Warfare Agents

scholarly journals Investigation of Fe-Doped Graphitic Carbon Nitride-Silver Tungstate as a Ternary Visible Light Active Photocatalyst

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Eid H. Alosaimi ◽  
Nadia Azeem ◽  
Noor Tahir ◽  
Asim Jilani ◽  
Muhammad Zahid ◽  
...  
Keyword(s):  
Wastewater Treatment ◽  
Visible Light ◽  
Metal Oxides ◽  
Advanced Oxidation Processes ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Oxidation Processes ◽  
Visible Light Active ◽  
Silver Tungstate

The rapid population growth and economic development have largely contributed to environmental pollution. Various advanced oxidation processes have been used as the most viable solution for the reduction of recalcitrant pollutants and wastewater treatment. Heterogeneous photocatalysis is one of the broadly used technologies for wastewater treatment among all advanced oxidation processes. Graphitic carbon nitride alone or in combination with various other semiconductor metal oxide materials acts as a competent visible light active photocatalyst for the removal of recalcitrant organic pollutants from wastewater. Rational designing of an environment-friendly photocatalyst through a facile synthetic approach encounters various challenges in photocatalytic technologies dealing with semiconductor metal oxides. Doping in g-C3N4 and subsequent coupling with metal oxides have shown remarkable enhancement in the photodegradation activity of g-C3N4-based nanocomposites owing to the modulation in g-C3N4 bandgap structuring and surface area. In the current study, a novel ternary Fe-doped g-C3N4/Ag2WO4 visible light active photocatalyst was fabricated through an ultrasonic-assisted facile hydrothermal method. Characterization analysis included SEM analysis, FTIR, XRD, XPS, and UV-Visible techniques to elucidate the morphology and chemical structuring of the as-prepared heterostructure. The bandgap energies were assessed using the Tauc plot. The ternary nanocomposite (Fe-CN-AW) showed increased photodegradation efficiency (97%) within 120 minutes, at optimal conditions of pH = 8, catalyst dose = 50 mg/100 ml, an initial RhB concentration of 10 ppm, and oxidant dose 5 mM under sunlight irradiation. The enhanced photodegradation of rhodamine B dye by ternary Fe-CN-AW was credited to multielectron transfer pathways due to insertion of a Fe dopant in graphitic carbon nitride and subsequent coupling with silver tungstate. The data were statistically assessed by the response surface methodology.

Graphitic carbon nitride/Na2Ti3O7/V2O5 nanocomposite as a visible light active photocatalyst

2020 ◽  
Vol 46 (11) ◽  
pp. 18287-18296 ◽  
Author(s):  
S.V. Prabhakar Vattikuti ◽  
Nguyen Dang Nam ◽  
Jaesool Shim
Keyword(s):  
Visible Light ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Visible Light Active

Metal-free g-C3N4 nanosheets as a highly visible-light-active photocatalyst for thiol–ene reactions

Nanoscale ◽  
2021 ◽  
Vol 13 (6) ◽  
pp. 3493-3499
Author(s):  
Shouren Zhang ◽  
Wenjing Yi ◽  
Yanzhen Guo ◽  
Ruoqi Ai ◽  
Zhichao Yuan ◽  
...  
Keyword(s):  
Visible Light ◽  
Carbon Nitride ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Ene Reactions ◽  
Metal Free ◽  
Visible Light Active ◽  
Carbon Nitride Nanosheets

Graphitic carbon nitride nanosheets are synthesized as photocatalysts for thiol–ene reactions. They are metal-free, highly visible-light active, recyclable and scalable.

Constructing graphite-like carbon nitride modified hierarchical yolk–shell TiO2 spheres for water pollution treatment and hydrogen production

2016 ◽  
Vol 4 (5) ◽  
pp. 1806-1818 ◽  
Author(s):  
Zhifeng Jiang ◽  
Chengzhang Zhu ◽  
Weiming Wan ◽  
Kun Qian ◽  
Jimin Xie
Keyword(s):  
Water Pollution ◽  
Hydrogen Production ◽  
Visible Light ◽  
Carbon Nitride ◽  
Porous Graphitic Carbon ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Pollution Treatment ◽  
Visible Light Active ◽  
Active Composite

Here we present a new visible light active composite based on porous graphitic carbon nitride decorated hierarchical yolk–shell TiO2 spheres for water pollution treatment and H2 evolution.

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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