Asian Journal of Green Chemistry

Journal Information
ISSN / EISSN : 2588-5839 / 2588-4328
Published by: Sami Publishing Company (10.33945)
Total articles ≅ 33
Current Coverage
DOAJ
Archived in
SHERPA/ROMEO
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Latest articles in this journal

Anbuvannan Mari, Ramesh Mookkaiah, Manikandan Elayaperumal
Asian Journal of Green Chemistry, Volume 3, pp 418-431; https://doi.org/10.33945/sami/ajgc.2019.4.1

Abstract:
ZnO nanoparticles have been synthesized via a simple green method using plant extract without the use of any other chemicals. The synthesized ZnO nanoparticles were characterized by UV–vis diffuse reflectance spectroscopy (UV-vis DRS), photoluminescence measurements (PL), X-ray diffraction (XRD), fourier transform infrared spectroscopy (FT-IR), field emission-scanning electron microscopy (FE-SEM) and transmission electron microscopy (TEM), respectively. Photocatalytic activities of ZnO nanoparticles were evaluated by degradation of methylene blue under UV radiation. Moreover, the antibacterial activity of synthesized ZnO nanoparticles against S. aureus, S. paratyphi, V. cholerae, and E. coli are also screened.
Mehdi Soleimani, , Mohammad R. Mofid, Ghadamali Khodarahmi, Farid Rahimpour
Asian Journal of Green Chemistry, Volume 3; https://doi.org/10.33945/sami/ajgc/2019.4.11

, Nasrin Sabourmoghaddam
Asian Journal of Green Chemistry, Volume 3; https://doi.org/10.33945/sami/ajgc.2019.4.12

Abstract:
In this work, TiO2-graphene/chitosan nanocomposite with high photocatalytic activity was successfully synthesized and characterized by various analyses such as XRD, TEM, SEM, EDX and DRS. The photocatalytic activity was tested vs. removal of methyl red as ananionic dye under black light radiation. Based on the results, TiO2-graphene/chitosan nanocomposite could effectively remove methyl red, and demonstrate an excellent photocatalytic enhancement over TiO2 and TiO2-graphene samples. The degradation reaction fit well to a Langmuir-Hinshelwood kinetic model implying that the reaction rate is depended on the initial adsorption step. An artificial neural network (ANN) comprising four input variables (TiO2-graphene/chitosan dosage, initial dye concentration, reaction time and temperature of the solution), eight neurons and an output variable (Removal efficiency %) was optimized, tested and validated for methyl red degradation by the prepared TiO2-graphene/chitosan nanocomposite. The results showed that the predicted data from the designed ANN model are in good agreement with the experimental data with a correlation coefficient (R2) of 0.9831. Based on the results, reaction time is the most influential variable and the temperature of solution is the less influential parameter in the removal efficiency of methyl red.
Sivakumar Matam, Prabakaran Kaliyan, Padmavathy Sethuramasamy, Seenivasa Perumal Muthu
Asian Journal of Green Chemistry, Volume 3, pp 508-517; https://doi.org/10.33945/sami/ajgc/2019.4.7

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