Evaluation of the Adsorption Efficiency of Graphene Oxide Hydrogels in Wastewater Dye Removal: Application of Principal Component Analysis

A graphene oxide based composite granule for methylene blue separation from aqueous solution: Adsorption, kinetics and thermodynamic studies

Graphene oxide and chitosan composite material using as a high-efficiency and low-cost granular adsorbent for methylene blue removal was fabricated via self-assembling method. The effects of pH value, contact time, initial concentration, adsorbent dose, temperature, and recyclic stability on the adsorption performance of methylene blue in aqueous solution were investigated in detail. Desorption process with the effects of solvents, contact time, and temperature were also conducted carefully in this study. The adsorption kinetics and adsorption isotherm of dye adsorption process showed that dye adsorption process was fitted to the pseudo-second-order kinetic model and the Freundlich adsorption isotherm, indicating a physical adsorption process with multilayer adsorption. The intra-particle diffusion model indicated that the dye adsorption by the granular adsorbent was strongly happened during the first 4 h. The thermodynamic study showed that the adsorption was a spontaneous and exothermic process and dye ions were condensed onto the surface of adsorbent. The maximum adsorption capacity of dye on the granular adsorbent was calculated as 951.35 mg/g and the adsorbent could maintain its adsorption performance after six cycles. In general, this study provided an efficient, cost-effective, and recyclable the granular adsorbent for dye separation from aqueous solution.

Preparation and Characterization of Fe3O4/Poly(HEMA-co-IA) Magnetic Hydrogels for Removal of Methylene Blue from Aqueous Solution

In the present study, Fe3O4/poly(2-hydroxyethyl methacrylate-co-itaconic acid) magnetic hydrogels (MHGs) were prepared by in situ synthesis of Fe3O4 magnetic particles in hydrogels (HGs). The resulting magnetic hydrogels were characterized by Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), thermogravimetric analysis (TGA), a vibrating sample magnetometer (VSM), scanning electron microscopy (SEM), and N2 adsorption-desorption. The effect of Fe3O4 on the swelling behavior and adsorption of methylene blue (MB) dye of the prepared hydrogel was studied. Parameters such as the dose, pH, contact time, and MB initial concentration were investigated. The results show that 75% (HG) and 91% (MHG) of MB (200 mg/L) were removed at doses of 2 mg/mL and 1 mg/mL, respectively, under a pH of 6.8 and a contact time of 10 min. The adsorption behavior followed the Langmuir isotherm model, indicating that the adsorption process takes place in monolayers and on homogeneous surfaces. The Langmuir capacities for MB adsorption using the HGs and MHGs were 78 and 174 mg/g, respectively. The adsorption kinetics followed a pseudo-second-order kinetic model. In addition, thermodynamic studies carried out show that the adsorption process is spontaneous and endothermic. Adsorption-desorption studies indicate that the magnetic hydrogel can remove MB for four cycles with removal efficiencies above 90%. Therefore, a MHG is suitable as an alternative material for MB adsorption.

Application of Unsupervised Learning for the Evaluation of Aerogels' Efficiency towards Dye Removal-A Principal Component Analysis (PCA) Approach

Water scarcity is a growing global issue, particularly in areas with limited freshwater sources, urging for sustainable water management practices to insure equitable access for all people. One way to address this problem is to implement advanced methods for treating existing contaminated water to offer more clean water. Adsorption through membranes technology is an important water treatment technique, and nanocellulose (NC)-, chitosan (CS)-, and graphene (G)- based aerogels are considered good adsorbents. To estimate the efficiency of dye removal for the mentioned aerogels, we intend to use an unsupervised machine learning approach known as "Principal Component Analysis". PCA showed that the chitosan-based ones have the lowest regeneration efficiencies, along with a moderate number of regenerations. NC2, NC9, and G5 are preferred where there is high adsorption energy to the membrane, and high porosities could be tolerated, but this allows lower removal efficiencies of dye contaminants. NC3, NC5, NC6, and NC11 have high removal efficiencies even with low porosities and surface area. In brief, PCA presents a powerful tool to unravel the efficiency of aerogels towards dye removal. Hence, several conditions need to be considered when employing or even manufacturing the investigated aerogels.

Application of Unsupervised Machine Learning for the Evaluation of Aerogels’ Efficiency towards Ion Removal—A Principal Component Analysis (PCA) Approach

Water scarcity is a global problem affecting millions of people. It can lead to severe economic, social, and environmental consequences. It can also have several impacts on agriculture, industry, and households, leading to a decrease in human quality of life. To address water scarcity, governments, communities, and individuals must work in synergy for the sake of water resources conservation and the implementation of sustainable water management practices. Following this urge, the enhancement of water treatment processes and the development of novel ones is a must. Here, we have investigated the potential of the applicability of “Green Aerogels” in water treatment’s ion removal section. Three families of aerogels originating from nanocellulose (NC), chitosan (CS), and graphene (G) are investigated. In order to reveal the difference between aerogel samples in-hand, a “Principal Component Analysis” (PCA) has been performed on the physical/chemical properties of aerogels, from one side, and the adsorption features, from another side. Several approaches and data pre-treatments have been considered to overcome any bias of the statistical method. Following the different followed approaches, the aerogel samples were located in the center of the biplot and were surrounded by different physical/chemical and adsorption properties. This would probably indicate a similar efficiency in the ion removal of the aerogels in-hand, whether they were nanocellulose-based, chitosan-based, or even graphene-based. In brief, PCA has shown a similar efficiency of all the investigated aerogels towards ion removal. The advantage of this method is its capacity to engage and seek similarities/dissimilarities between multiple factors, with the elimination of the shortcomings for the tedious and time-consuming bidimensional data visualization.

Metal Oxide Hydrogel Composites for Remediation of Dye-Contaminated Wastewater: Principal Component Analysis

Water pollution is caused by multiple factors, such as industrial dye wastewater. Dye-contaminated water can be treated using hydrogels as adsorbent materials. Recently, composite hydrogels containing metal oxide nanoparticles (MONPs) have been used extensively in wastewater remediation. In this study, we use a statistical and artificial intelligence method, based on principal component analysis (PCA) with different applied parameters, to evaluate the adsorption efficiency of 27 different MONP composite hydrogels for wastewater dye treatment. PCA showed that the hydrogel composites CTS@Fe3O4, PAAm/TiO2, and PEGDMA-rGO/Fe3O4@cellulose should be used in situations involving high pH, time to reach equilibrium, and adsorption capacity. However, as the composites PAAm-co-AAc/TiO2, PVPA/Fe3O4@SiO2, PMOA/ATP/Fe3O4, and PVPA/Fe3O4@SiO2, are preferred when all physical and chemical properties investigated have low magnitudes. To conclude, PCA is a strong method for highlighting the essential factors affecting hydrogel composite selection for dye-contaminated water treatment.