Construction of functional nanonetwork-structured carbon nitride with Au nanoparticle yolks for highly efficient photocatalytic applications

2018 ◽  
Vol 54 (52) ◽  
pp. 7159-7162 ◽  
Author(s):  
Kunyi Leng ◽  
Weicong Mai ◽  
Xingcai Zhang ◽  
Ruliang Liu ◽  
Xidong Lin ◽  
...  
Keyword(s):  
Carbon Nitride ◽  
Au Nanoparticle ◽  
Highly Efficient ◽  
Photocatalytic Performances ◽  
Photocatalytic Applications

Functional nanonetwork-structured carbon nitride with Au nanoparticle yolks was successfully fabricated, and demonstrated excellent photocatalytic performances.

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 Carbon nitride nanotubes with in situ grafted hydroxyl groups for highly efficient spontaneous H2O2 production

2021 ◽  
Vol 288 ◽  
pp. 119993
Author(s):  
Liang Zhou ◽  
Juying Lei ◽  
Fuchen Wang ◽  
Lingzhi Wang ◽  
Michael R. Hoffmann ◽  
...  
Keyword(s):  
Carbon Nitride ◽  
H2o2 Production ◽  
Hydroxyl Groups ◽  
Highly Efficient

scholarly journals Band Engineering and Morphology Control of Oxygen-Incorporated Graphitic Carbon Nitride Porous Nanosheets for Highly Efficient Photocatalytic Hydrogen Evolution

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Yunyan Wu ◽  
Pan Xiong ◽  
Jianchun Wu ◽  
Zengliang Huang ◽  
Jingwen Sun ◽  
...  
Keyword(s):  
Surface Area ◽  
Hydrogen Evolution ◽  
Carbon Nitride ◽  
Separation Efficiency ◽  
Physical Adsorption ◽  
Graphitic Carbon ◽  
Graphitic Carbon Nitride ◽  
Photocatalytic Hydrogen Evolution ◽  
Band Engineering ◽  
Highly Efficient

AbstractGraphitic carbon nitride (g-C3N4)-based photocatalysts have shown great potential in the splitting of water. However, the intrinsic drawbacks of g-C3N4, such as low surface area, poor diffusion, and charge separation efficiency, remain as the bottleneck to achieve highly efficient hydrogen evolution. Here, a hollow oxygen-incorporated g-C3N4 nanosheet (OCN) with an improved surface area of 148.5 m2 g−1 is fabricated by the multiple thermal treatments under the N2/O2 atmosphere, wherein the C–O bonds are formed through two ways of physical adsorption and doping. The physical characterization and theoretical calculation indicate that the O-adsorption can promote the generation of defects, leading to the formation of hollow morphology, while the O-doping results in reduced band gap of g-C3N4. The optimized OCN shows an excellent photocatalytic hydrogen evolution activity of 3519.6 μmol g−1 h−1 for ~ 20 h, which is over four times higher than that of g-C3N4 (850.1 μmol g−1 h−1) and outperforms most of the reported g-C3N4 catalysts.

Surface activated carbon nitride nanosheets with optimized electro-optical properties for highly efficient photocatalytic hydrogen production

2016 ◽  
Vol 4 (7) ◽  
pp. 2445-2452 ◽  
Author(s):  
Mohammad Ziaur Rahman ◽  
Jingrun Ran ◽  
Youhong Tang ◽  
Mietek Jaroniec ◽  
Shi Zhang Qiao
Keyword(s):  
Optical Properties ◽  
Activated Carbon ◽  
Hydrogen Production ◽  
Carbon Nitride ◽  
Surface Activation ◽  
Step Method ◽  
Highly Efficient ◽  
Thin Carbon ◽  
Carbon Nitride Nanosheets ◽  
First Time

We introduce a three-step method (co-polymerization, surface activation and exfoliation) for the first time to synthesize sub-nanometer-thin carbon nitride nanosheets as highly efficient hydrogen evolution photocatalysts.

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