Zeolites in wastewater treatment: A comprehensive review on scientometric analysis, adsorption mechanisms, and future prospects.

Zeolites possess a microporous crystalline structure, a large surface area, and a uniform pore size. Natural or synthetic zeolites are commonly utilized for adsorbing organic and inorganic compounds from wastewater because of their unique physicochemical properties and cost-effectiveness. The present review work comprehensively revealed the application of zeolites in removing a diverse range of wastewater contaminates, such as dyes, heavy metal ions, and phenolic compounds, within the framework of contemporary research. The present review work offers a summary of the existing literature about the chemical composition of zeolites and their synthesis by different methods. Subsequently, the article provides a wide range of factors to examine the adsorption mechanisms of both inorganic and organic pollutants using natural zeolites and modified zeolites. This review explores the different mechanisms through which zeolites effectively eliminate pollutants from aquatic matrices. Additionally, this review explores that the Langmuir and pseudo-second-order models are the predominant models used in investigating isothermal and kinetic adsorption and also evaluates the research gap on zeolite through scientometric analysis. The prospective efficacy of zeolite materials in future wastewater treatment may be assessed by a comparative analysis of their capacity to adsorb toxic inorganic and organic contaminates from wastewater, with other adsorbents.

AI-enhanced adsorption modeling: Challenges, applications, and bibliographic analysis.

Inorganic and organic contaminants, such as fertilisers, heavy metals, and dyes, are the primary causes of water pollution. The field of artificial intelligence (AI) has received significant interest due to its capacity to address challenges across various fields. The use of AI techniques in water treatment and desalination has recently shown useful for optimising processes and dealing with the challenges of water pollution and scarcity. The utilization of AI in the water treatment industry is anticipated to result in a reduction in operational expenditures through the lowering of procedure costs and the optimisation of chemical utilization. The predictive capabilities of artificial intelligence models have accurately assessed the efficacy of different adsorbents in removing contaminants from wastewater. This article provides an overview of the various AI techniques and how they can be used in the adsorption of contaminants during the water treatment process. The reviewed publications were analysed for their diversity in journal type, publication year, research methodology, and initial study context. Citation network analysis, an objective method, and tools like VOSviewer are used to find these groups. The primary issues that need to be addressed include the availability and selection of data, low reproducibility, and little proof of uses in real water treatment. The provision of challenges is essential to ensure the prospective success of AI associated with technologies. The brief overview holds importance to everyone involved in the field of water, encompassing scientists, engineers, students, and stakeholders.

Fabrication and characterization of high-performance forward-osmosis membrane by introducing manganese oxide incited graphene quantum dots.

Forward osmosis (FO) is the futuristic membrane desalination technology as it transcends the disadvantages of other pressure-driven techniques. But, there still remain critical challenges like fabrication of highly permeable membrane with ideal structures maintaining high rejection rates that need to be addressed for implementation as a practical technology. In this work, novel thin-film composite (TFC) membranes were fabricated by means of incorporating manganese oxide (MnO2) incited graphene quantum dots (GQDs) nanocomposite into a cellulose acetate (CA) suspension followed by phase inversion (PI) for enhanced FO performance. The surface morphology and chemical structure of fabricated membranes were studied using various characterization techniques like XRD, FT-IR, SEM-EDS, Mapping, AFM, and TGA. The structural parameters, water flux, reverse salt flux and salt rejection was estimated on the basis of data obtained from four varying initial draw solution concentrations. At high nanocomposites stacking, the hydrophilicity of the casting blend increase, and subsequently, the PI exchange rate additionally increases, which brings about noticeable difference in the surface morphology. The membrane with 0.5 wt% nanocomposite exhibited superior FO separation performance with osmotic water flux of 18.89, 34.49, 41.76 and 42.34 in L.m-2.h-1 with variable concentrations of NaCl salt solution (0.25M, 0.5M, 1M, and 2M), respectively. Also, the porosity of the membrane was increased to 47.23% with 96.87% salt rejection. The results indicate that the hydrophilicity of the nanocomposite drives them to the interface among CA and water during PI process leading to solid hydrogen bonding to achieve high water permeability.

Integrating artificial neural networks and response surface methodology for predictive modeling and mechanistic insights into the detoxification of hazardous MB and CV dyes using Saccharum officinarum L. biomass.

The presence of dye pollutants in industrial wastewater poses significant environmental and health risks, necessitating effective treatment methods. The optimal adsorption treatment of methylene blue (MB) and crystal violet (CV) dye-simulated wastewater utilising Saccharum officinarum L presents a key challenge in the selection of appropriate modelling approaches. While RSM and ANN models are frequently used, there is a noticeable knowledge gap when it comes to evaluating their relative strengths and weaknesses in this context. The study compared the predictive abilities of response surface methodology (RSM) and artificial neural network (ANN) for the adsorption treatment of MB and CV dye-simulated wastewater using Saccharum officinarum L. The process experimental variables were modelled and predicted using a three-layer artificial neural network trained using the Levenberg-Marquard backpropagation algorithm and 30 central composite designs (CCD). The adsorption study used a specific mechanism, which led to noteworthy maximum removals of 98.3% and 98.2% for dyes (MB and CV), respectively. The RSM model achieved an impressive R2 of 0.9417, while the ANN model achieved 0.9236 in MB. Adsorption is commonly used to remove colour from many different materials. Saccharum officinarum L., a byproduct of sugarcane processing, has shown potential as an efficient and ecological adsorbent in this environment. The purpose of this study is to evaluate sugarcane bagasse's potential as an adsorbent for the removal of dyes MB and CV from industrial wastewater, providing a long-term strategy for reducing dye pollution. Due to its beneficial economic and environmental characteristics, the Saccharum officinarum L. adsorbent has prompted research into sustainable resources with low pollutant indices.