Herein, it is aimed to develop a high-performance monolithic adsorbent to be utilized in methyl orange (MO) adsorption. Therefore, amino-functionalized three-dimensional graphene networks (3D-GNf) fulfilling the requirements of reusability and high capacity have been fabricated via hydrothermal self-assembly approach followed by a double-crosslinking strategy. The potential utilization of 3D-GNf as an adsorbent for removal MO has been assessed using both batch-adsorption studies and an artificial neural network (ANN) approach. Graphene oxide sheets have been amino-functionalized and cross-linked, by ethylenediamine (EDA) during hydrothermal treatment, following the glutaraldehyde has used as a double-crosslinking agent to facilitate the crosslinking of architecture. The successful fabrication of 3D-GNf has been confirmed by field-emission scanning electron microscopy (FESEM), Fourier transform infrared (FT-IR), Raman and X-ray photoelectron spectroscopy (XPS). Moreover, N2 adsorption/desorption isotherms have revealed the high specific surface area (1015 m2 g-1) with high pore volume (1.054 cm3 g-1) and hierarchical porous structure of 3D-GNf. The effect of initial concentration, contact time, and temperature on adsorption capacity have been thoroughly studied, and the kinetics, isotherms, and thermodynamics of MO adsorption have been modelled. The MO adsorption has been well defined by the pseudo-second-order kinetic model and Langmuir isotherm model with a monolayer adsorption capacity of 270.27 mg g-1 at 25 °C. The thermodynamic findings have revealed MO adsorption has occurred spontaneously with an endothermic process. The Levenberg-Marquardt backpropagation algorithm has been implemented to train the ANN model, which has used the activation functions of tansig and purelin functions at the hidden and output layers, respectively. An optimum ANN model with high-performance metrics (coefficient of determination, R2 = 0.9995; mean squared error, MSE = 0.0008) composed of three hidden layers with 5 neurons in each layer was constructed to forecast MO adsorption. The findings have shown that experimental results are consistent with ANN-based data, implying that the suggested ANN model may be used to forecast cationic dye adsorption.
Herein, it was aimed to optimize, model, and forecast the biosorption of Congo Red onto biomass-derived biosorbent. Therefore, the waste-orange-peels were processed to fabricate biomass-derived carbon, which was activated by ZnCl2 and modified with cetyltrimethylammonium bromide. The physicochemical properties of the biosorbents were explored by scanning electron microscopy and N2 adsorption/desorption isotherms. The effects of pH, initial dye concentration, temperature, and contact duration on the biosorption capacity were investigated and optimized by batch experimental process, followed by the kinetics, equilibrium, and thermodynamics of biosorption were modeled. Furthermore, various artificial neural network (ANN) architectures were applied to experimental data to optimize the ANN model. The kinetic modeling of the biosorption offered that biosorption was in accordance both with the pseudo-second-order and saturation-type kinetic model, and the monolayer biosorption capacity was calculated as 666.67 mg g-1 at 25 °C according to Langmuir isotherm model. According to equilibrium modeling, the Freundlich isotherm model was better fitted to the experimental data than the Langmuir isotherm model. Moreover, the thermodynamic modeling revealed biosorption took place spontaneously as an exothermic process. The findings revealed that the best ANN architecture trained with trainlm as the backpropagation algorithm, with tansig-purelin transfer functions, and 14 neurons in the single hidden layer with the highest coefficient of determination (R2 = 0.9996) and the lowest mean-squared-error (MSE = 0.0002). The well-agreement between the experimental and ANN-forecasted data demonstrated that the optimized ANN model can predict the behavior of the anionic dye biosorption onto biomass-derived modified carbon materials under various operation conditions.
Heavy metal pollution remains a global environmental challenge that poses a significant threat to human life. Various methods have been explored to eliminate heavy metal pollutants from the environment. However, most methods are constrained by high expenses, processing duration, geological problems, and political issues. The immobilization of metals, phytoextraction, and biological methods have proven practical in treating metal contaminants from the soil. This review focuses on the general status of heavy metal contamination of soils, including the excessive heavy metal concentrations in crops. The assessment of the recent advanced technologies and future challenges were reviewed. Molecular and genetic mechanisms that allow microbes and plants to collect and tolerate heavy metals were elaborated. Tremendous efforts to remediate contaminated soils have generated several challenges, including the need for remediation methodologies, degrees of soil contamination, site conditions, widespread adoptions and various possibilities occurring at different stages of remediation are discussed in detail.
Graphene-based nanomaterials with remarkable properties, such as good biocompatibility, strong mechanical strength, and outstanding electrical conductivity, have dramatically shown excellent potential in various applications. Increasing surface area and porosity percentage, improvement of adsorption capacities, reduction of adsorption energy barrier, and also prevention of agglomeration of graphene layers are the main advantages of functionalized graphene nanocomposites. On the other hand, Cerium nanostructures with remarkable properties have received a great deal of attention in a wide range of fields; however, in some cases low conductivity limits their application in different applications. Therefore, the combination of cerium structures and graphene networks has been widely invesitaged to improve properties of the composite. In order to have a comprehensive information of these nanonetworks, this research reviews the recent developments in cerium functionalized graphene derivatives (graphene oxide (GO), reduced graphene oxide (RGO), and graphene quantum dot (GQD) and their industrial applications. The applications of functionalized graphene derivatives have also been successfully summarized. This systematic review study of graphene networks decorated with different structure of Cerium have potential to pave the way for scientific research not only in field of material science but also in fluorescent sensing, electrochemical sensing, supercapacitors, and catalyst as a new candidate.
The paper evaluates the routes towards the evaluation of membranes using ZIF-62 metal organic framework (MOF) nano-hybrid dots for environmental remediation. Optimization of interaction of operating parameters over the rooted membrane is challenging issue. Subsequently, the interaction of operating parameters including temperature, pressure and CO2 gas concentration over the resultant rooted membranes are evaluated and optimized using response surface methodology for environmental remediation. In addition, the stability and effect of hydrocarbons on the performance of the resulting membrane during the gas mixture separation are evaluated at optimum conditions to meet the industrial requirements. The characterization results verified the fabrication of the ZIF-62 MOF rooted composite membrane. The permeation results demonstrated that the CO2 permeability and CO2/CH4 selectivity of the composite membrane was increased from 15.8 to 84.8 Barrer and 12.2 to 35.3 upon integration of ZIF-62 nano-glass into cellulose acetate (CA) polymer. Subsequently, the optimum conditions have been found at a temperature of 30 °C, the pressure of 12.6 bar and CO2 feed concentration of 53.3 vol%. These optimum conditions revealed the highest CO2 permeability, CH4 permeability and CO2/CH4 separation factor of 47.9 Barrer, 0.2 Barrer and 26.8. The presence of hydrocarbons in gas mixture dropped the CO2 permeability of 56.5% and separation factor of 46.4% during 206 h of testing. The separation performance of the composite membrane remained stable without the presence of hydrocarbons for 206 h.
This study explores the role of renewable energy (RE) penetration in Malaysia's energy security (ES) and its implications for the country's target of 20% capacity in the energy mix by 2025. Renewable energy (RE) is a critical driver of long-term energy security. In 2018, the share of renewable energy in Malaysia's energy mix was 9%, falling far short of the national target of 20% penetration by 2025. This study employs a system dynamics approach to investigate the relationship between RE penetration and correlated indicators from energy security (ES) dimensions: energy availability, environmental sustainability, and socio-economics. The causal relationships between the three-dimensional indicators of ES have been established using causal and stock and flow logic. Simulated results show that energy consumption has increased sharply, while energy efficiency and economic growth have only increased by a small margin with an increase in RE from 2015 to 2020. The energy intensity is expected to rise slightly by the end of the fifth year. As a result, the overall impact is positive for Malaysia's environmental sustainability while reducing its reliance on energy imports and meeting national economic growth demands.