Applied Chemical Engineering

Abstract The paper explained and estimated collision probability for chemical reaction modeling in detail. To define the risk of a chemical process, it is vital to determine the probability of an event occurrence. Characterize the collision probability that a molecule may encounter along its lifetime. This characterization is intended to define a methodology to compute the associated collision.  Analyzing the characteristics of data sets by the statistical method in regards to the main aspects relates to collision probability. An investigation of the impact of these features addressed. Approaches based on Minitab presented and status of particle collision probability determined mathematically. Results showed the status of collision probability based on the correlation of kinetic energy and rate of reaction associated with envisaged encounters. A comparison was made to define the best fit model. The study recommended accepted levels of collision probability for the occurrence of the chemical reaction.

Abstract This study presents for the first time the novel synthesis and in vitro-in silico bio-evaluation of seven coumarin derivatives linearly conjugated with a 1,4-dioxane ring. The primary goal was to develop accessible and modifiable coumarin-based scaffolds with a broad spectrum of biological activities. The structural identities of the synthesized compounds were confirmed using various spectroscopic techniques, including 1 H-NMR, 13 C-NMR, and FTIR analyses. The biological potential of the synthesized fused structures was systematically evaluated through a series of in vitro  assays. Notably,  DFC4  emerged as a promising candidate with strong antioxidative stress activity. In terms of antidiabetic potential, DFC2  demonstrated significant inhibition of both α-glucosidase and α-amylase enzymes, suggesting its usefulness in managing hyperglycemia. DFC5  exhibited potent antibacterial effects, comparable to those of ciprofloxacin, against all tested aerobic bacterial strains. In addition, DFC1  showed superior antifungal activity, outperforming nystatin. The same fused structure also displayed noteworthy anti-inflammatory properties, likely through a cyclooxygenase-dependent mechanism. Regarding anticancer properties, DFC4  again stood out by exhibiting effective cytotoxicity toward cancer cells while maintaining biosafety toward non-carcinogenic cells. All synthesized fused structures, especially  DFC5 , demonstrated favorable biosafety profiles when tested against commensal bacterial strains. To complement the in vitro  findings, computational tools were used to predict the toxicity and pharmacokinetic profiles of the structures under evaluation. The results indicated that the synthesized fused structures possess desirable biosafety thresholds and oral bioavailability characteristics. Collectively, these findings suggest that the newly synthesized fused structures hold significant potential as multifunctional therapeutic agents for future drug development.

Abstract The materials of this article are dedicated to studying the relationships between the structural and mechanical properties of porous polymeric materials for their control. The research revealed that as the pore diameter of the polymeric material decreases, the Young's modulus decreases, while the yield strength increases. With an increase in the thickness of cell walls, the Young's modulus decreases, and the yield strength increases. A higher Young's modulus was found in samples with lower density, while the highest yield strength was observed in samples with the highest density.

Abstract With the increasing demand for automation and precision in chemical engineering processes, robotic arms play a crucial role in enhancing production efficiency and product quality. Traditional control methods often struggle to cope with the complex dynamic environments and unpredictable disturbances inherent in chemical engineering applications. This study presents an improved Nonlinear Active Disturbance Rejection Control (NLADRC) method for dynamic trajectory tracking of chemical engineering robotic arms. Leveraging the support of the Yunnan Province Major Science and Technology Project, the proposed NLADRC framework integrates an enhanced disturbance observer and adaptive control strategies to effectively mitigate unknown disturbances and parameter variations. Experimental results demonstrate that the NLADRC method significantly outperforms traditional PID and standard ADRC controllers in terms of tracking accuracy, response speed, and robustness. The findings provide a robust theoretical foundation and practical guidelines for the deployment of advanced control strategies in chemical engineering robotic systems.

Abstract The generation of plastic waste has global concern due to its negative effects on the environment. So, plastic waste (PW) management has emerged as a significantly challenges now global faced.  Pyrolysis is a promising method to reduce pollution from plastic waste by converting waste plastic into char, pyrolytic oil and syngas. In this paper, we designed pyrolytic reactor to minimize medical plastic waste samples to pyrolytic product. This experiment also run through Aspen to compare the result with experimental value.  Syringe and Glucose Bottle material was used for the experiment and simulation with the help of Redlich-Kwong model to simulate the thermal degradation process on LDPE and PP. It was found that solid, oil and Gases formation separate at first stage from the reactor and separation of gas and liquid from vapour formation with the aid of water tube condenser. The key pyrolysis conditions like temperature ranges (e.g., 3°C –400°C), operating pressure (e.g.,1.01bar to 10.13 bar) are added. The study results in the reduction in medical plastic waste collected from Medical Waste Collection centre and which we can use 15 % oil to produce Energy as a fuel in Vehicles. We can use 2% solid char for construction material and also use of 80% gas for as a fuel for burners. The work aims to optimize the pyrolysis of medical plastics for sustainable waste management, energy recovery, and safe disposal of hazardous materials. obtained results demonstrated that a conversion of Low-density polyethylene into liquid fuel up to 15% has been optimum value.

Abstract This study investigates the catalytic transfer hydrogenolysis of 5-hydroxymethylfurfural (HMF) to produce the valuable biofuel 2,5-dimethylfuran (DMF) using a Ni-Co/C catalyst. HMF conversion into DMF offers a promising alternative to fossil fuels, leveraging biomass-derived feedstocks. Key process parameters—temperature (150–270 °C), reaction time (6–10 hours), hydrogen pressure (0.5–2.5 MPa), and reaction speed (150–750 rpm)—were systematically evaluated for their influence on DMF yield. A central composite design and response surface methodology were utilized to optimize these variables, allowing precise control over the reaction conditions. Findings indicated that temperature and reaction speed significantly impacted DMF yield, with the highest yield of 96.5% achieved at an optimal temperature of 210 °C and a reaction speed of 450 rpm under self-generated pressure, reducing dependency on external hydrogen sources. This study proposes a novel reaction pathway where 5-methylfurfural (5-MF) serves as an intermediate, diverging from conventional methods using 2,5-bis(hydroxymethyl)furan (BHMF). The approach substitutes formic acid as a hydrogen donor instead of H₂, contributing to a more sustainable and efficient conversion process. Additionally, the influence of formic acid dosage on HMF conversion and DMF yield was assessed, further refining the conditions for high-yield DMF production. Beyond HMF, the methodology was effective in converting furfural to 2-methylfuran, expanding its applicability to other biomass-derived chemicals. These findings advance the catalytic hydrogenolysis of HMF, presenting a viable pathway for sustainable biofuel production, reducing reliance on fossil fuels, and contributing to the development of green chemistry solutions.

Abstract For rational use of natural resources and address environmental issues, it is necessary to create new or modernize existing technological processes One such resource is bentonite clay, but there is practically no information on its use in water treatment processes. It should be noted that bentonites differ from each other depending on their deposit. Therefore, when using them, it is necessary to conduct research according to their field of application. Studied was the process of sorption by bentonite clay (on the example of Ascanite clay of Georgia), which showed that it can be used as an ion exchanger. Established was the total exchange capacity of clay, which was 500 g-eq/m3 (wet cation exchanger) (100 meq/100g of clay). Determined was experimentally the exchange coefficient of Ca-Na ions on clay, which amounted to 1.59 m3/m3 (wet cation exchanger/water). To check the confidence of the obtained value of the ion exchange coefficient, the Pearson criterion was used. It showed that the obtained value describes the ongoing technological process with more than 95% reliability. When calculating the Pearson criterion, a developed new method for determining the number and length of grouping intervals was used.

Abstract Global warming has become a controversial topic world-wide. It is becoming even more important for researchers to define vital tools and environmental solutions to alleviate the industrial release effects. This paper highlights environmental issues in an industrial area in Saudi Arabia. For the sake of clarity, the industrial environmental issues specified into air and water. Additionally, the pollutants impact into the environment by industrial manufacturing activities around the Jubail Industrial City (JIC) is the main reason of growing pollution problem. The data constitute the results of monitoring campaign of chemical concentration levels in JIC located. A daily averages atmospheric concentrations of gases were measured at different sites located around the city. The result showed that the measurement of gases concentrations consistently correlated to each other through the season (i.e. CO2). However, the research indicates that direct traffic emissions have an important contribution to increase the gas level such as Particulate Matters (PM’s) and suspended material.

Abstract Predicting the thermal performance of buildings is a key research area in the context of improving energy efficiency and reducing environmental impacts. Several approaches have been developed to model and predict thermal performance. Among these approaches, machine learning techniques are distinguished by their ability to exploit large amounts of data and model complex systems, but their effectiveness remains to be demonstrated in different contexts. This work therefore explores the application of hybrid machine learning models. Six different models, including ANN-LR, ANN-RR, ANN-RF, ANN-GB, ANN-DT, and ANN-ELM were evaluated and compared to the standalone model (ANN) based on the statistical metrics. Using the TRNSYS tool, the dynamic simulation of a building enhanced with PCM into the roof was performed to generate the data. The findings proved the effectiveness of the hybrid machine learning techniques, with ANN-LR and ANN-GB emerging as the most reliable hybrid approaches for accurate prediction, showcasing their robustness and suitability for complex prediction tasks, while ANN-RR model proved to be the least effective. Furthermore, the performance of the models varied considerably depending on the target, with total energy consumption appearing more complex and challenging for prediction.

Abstract In this study, we report the design, synthesis, and comprehensive biological evaluation of a novel series of linear 1,3-dioxolane–coumarin hybrids ( DCH1 – DCH7 ) as promising multi-target therapeutic agents. The synthesis involved an optimized multi-step approach beginning with a Pechmann condensation followed by selective esterification reactions, yielding high-purity compounds confirmed via FTIR, 1 H-NMR, and 13 C-NMR analyses. The hybrids were systematically screened for their antioxidant, anticancer, anti-inflammatory, antidiabetic, and antimicrobial activities. In addition, their biocompatibility was assessed using non-cancerous human cell lines and commensal bacterial strains. Among the synthesized hybrids, DCH4  exhibited remarkable antioxidant and anticancer properties, while DCH1  showed superior anti-inflammatory and antifungal activity. DCH2  demonstrated potent antidiabetic and anti-anaerobic bacterial efficacy, and DCH5  emerged as the most active against aerobic gram-negative bacteria. These bioactivities were closely linked to specific structural modifications, as revealed through structure–activity relationship analyses. In silico  evaluations using ProTox-II, PreADME, and SwissADME tools predicted favorable drug-likeness, low toxicity, high oral bioavailability, and acceptable pharmacokinetic profiles, further supporting their therapeutic relevance. Importantly, all hybrids displayed minimal cytotoxic effects on non-cancerous cells and exhibited selective antimicrobial actions, sparing beneficial gut microbiota. This highlights their potential as safer alternatives to conventional therapies. The introduction of the 1,3-dioxolane moiety into the coumarin scaffold contributed significantly to the observed bioactivities, suggesting this hybrid framework as a versatile platform for future drug development. Overall, our findings establish these 1,3-dioxolane–coumarin hybrids as promising multifunctional drug candidates with broad-spectrum pharmacological potential and a strong safety profile, warranting further investigation and development.

Abstract Accurate prediction of Protein Secondary Structure (PSS) plays a crucial role in understanding the functional mechanisms of proteins. This study focuses on predicting the secondary structure of the quorum-sensing control repressor protein (QscR) using a UNet based deep learning model. The UNet architecture, known for its exceptional performance is adapted to predict structural features of proteins by learning from sequence based data. The proposed model was trained and validated using benchmark protein datasets to ensure generalizability and accuracy. Comparative analysis with traditional approaches demonstrated that the UNet model achieved superior performance in terms of prediction accuracy and computational efficiency. The findings suggest that the UNet model is a robust tool for SS prediction and can provide deeper insights into quorum-sensing mechanisms, aiding in the design of novel antibacterial strategies.

Abstract This study investigates the combustion processes of methane in an oxygen and carbon dioxide environment within oxygen-fuel energy complexes (OFC). The unique operating conditions, characterized by high pressures (up to 300 atm) and the use of CO2 as a diluent, necessitate a thorough understanding of the combustion dynamics, which differ significantly from traditional gas turbine units (GTU). An experimental setup, inspired by existing literature, is proposed to evaluate the combustion characteristics of methane under these conditions. Key objectives include establishing similarity criteria for hydrodynamic, thermal, and mass transfer processes to ensure the validity of experimental results. The analysis identifies critical parameters such as Reynolds, Euler, Boltzmann, Prandtl, and Damköhler numbers, which serve as benchmarks for achieving operational similarity between model and natural combustion scenarios. The findings indicate that while complete similarity across all criteria is unattainable, satisfactory levels can be achieved for specific processes under controlled conditions. The proposed experimental stand is designed to replicate the conditions of OFC combustion chambers, incorporating advanced measurement systems for accurate monitoring of temperature, pressure, and flow rates. The study emphasizes the importance of conducting separate tests for mass transfer processes to ensure comprehensive evaluations of combustion dynamics. This research provides valuable insights for the design and optimization of burner devices in OFC applications, contributing to the advancement of cleaner and more efficient energy production technologies. The established methodologies and criteria can guide future experimental studies, enhancing the understanding of combustion processes in high-pressure environments.

Abstract Noise negatively impacts human health and the environment, making it a pressing issue in the context of industrial development and urbanization. The objectives of this research include the production of durable panels from waste materials with gypsum as the binder, the study of their acoustic properties and sound absorption coefficient compared to traditional materials, as well as the analysis of mechanical properties and resistance to compressive forces. To conduct acoustic and mechanical studies, 36 special samples in the form of panels were manufactured from a mixture of gypsum with rubber and cork waste in various combinations, with the addition of polymer material and maintaining a specific water-to-powder ratio. Acoustic properties were determined using a device operating on the principle of "transmitter-receiver," while mechanical properties were assessed through compression testing. The results showed that all samples containing rubber and cork waste had a sound absorption coefficient higher than 0.35, which increased with frequency and decreased with increasing material density. An increase in the proportion of waste contributed to greater porosity and, consequently, improved sound absorption. Mechanical testing of the samples under compression demonstrated that their failure limit was reached at loads of up to 15 kN. Comparison with benchmark studies confirmed the effectiveness of utilizing recycled materials.

Abstract Monitoring soil contaminants is crucial in addressing sustainability issues. This study directly addresses the environmental sustainability issue of soil contamination with heavy metals (HMs) due to anthropogenic activities, particularly in soil surrounding the Al-Diwaniyah power plant, which is one of Iraq's electricity sources. Samples were obtained at the power station's three sites: right (R), left (L), and direction (D) (term "direction" refers to sampling site located directly in front of power plant, aligned with prevailing wind path). Soil samples were collected from different locations throughout 2024, with two separate soil sub-samples from the same site. Samples were obtained at the power station's three sites: right (R), left (L), direction. The concentrations of HMs chromium, nickel, cadmium, and lead were evaluated using an atomic absorption spectrometry (AAS) and results were expressed in milligrams of metal per kilogram of dry soil (mg/kg). The findings showed that the total concentrations of HMs were Ni> Pb> Cr> Cd, with values of 17.32 -41.27, 33.61-0.32 -6.07, and 12.77 -46.89 mg/kg for Cr, Ni, Cd), and Pb, respectively. The Contamination Factor (CF) and Ecological Risk Index (Er) calculation showed that the soil samples were heavily polluted. Furthermore, HMs concentrations were usually high throughout the autumn season at all three investigated sites. The elevated concentrations observed during autumn season may be due to reduced rainfall and limited leaching, resulting in increased accumulation of HMs in upper soil layers. It may be concluded that human activities have an influence on soil health, and these findings might emphasize the need of preserving soil health and sustainability from HMs contamination induced by neighboring activities such as electricity generation facilities.

Abstract As competition for water demand increases in all life sections, the agricultural sector has observed a gradual decrease in water consumption. In order to sustain or enhance agricultural productivity, innovative irrigation methods, like surface and subsurface drip irrigation systems, enhance the efficiency of water utilization compared to conventional systems. Multiple models have been established to forecast the dimensions of moisture distribution, which have significance for constructing an efficient drip irrigation system. This study evaluates the performance of surface and subsurface drip irrigation systems using the HYDRUS-2D model to predict soil moisture distribution under varying conditions of time, emitter spacing, and emitter depth. The results indicate a high level of agreement between simulated and observed moisture distributions, demonstrating the reliability of HYDRUS-2D as a predictive tool for modeling soil water dynamics. The study demonstrates the effectiveness of HYDRUS-2D in simulating soil moisture distribution for surface and subsurface drip irrigation systems under varying conditions of time, emitter spacing, and depth. Subsurface irrigation at 20 cm depth showed the highest simulation accuracy, with RMSE as low as 0.008798 and R² up to 0.9839, particularly at shorter intervals. Closer emitter spacing (20 cm) provided more uniform moisture distribution, while increased spacing (40 cm) led to less consistent patterns. Emitters placed at 20 cm depth achieved the optimal balance between precision and efficiency by minimizing evaporation and effectively targeting the root zone. These findings underline the utility of HYDRUS-2D as a reliable tool for optimizing drip irrigation design, improving water-use efficiency, and supporting sustainable agricultural practices in water-scarce regions.

Abstract The greater development in the computational tools and the presence of a huge number of established chemical libraries emerge the inquiry about the ability to integrate the computational techniques in the drug discovery process. This review emphasizes the role of computational tools and software to accelerate the steps in the drug discovery pipeline. The data were gathered from the trendiest research articles and reviews that were indexed in the well-established scholarly search engines. Different vital techniques, such as virtual screening, pharmacophore modeling, molecular docking, molecular dynamic simulations, and quantitative structure activity relationship, are widely employed for target recognition, lead refining, and forecasting binding behavior at the atomic level. The advancement in artificial cognitive computing software significantly expanded the capabilities for rapid analysis of substantial data sets and the generation of toxicity and bioactivity predictive models of drugs. The contributions of these software tools tackled global problems, like drug repurposing during the coronavirus outbreak, and the direction toward personalized medicaments highlight their crucial role in the swift discovery of new treatments. In addition, computational tools are widely employed to enhance the drug formulations, which leads to discovering drugs with optimum pharmacokinetic and toxicity profiles. The difficulties in analyzing the multi-target binding behaviors and the limited success rates of the generated candidates in the experimental validations were the main existing limitations. However, the steep development in the field of artificial intelligence and the hybrid biochemical-computational approaches provided promising horizons to tackle these limitations. Chemical computational tools significantly affect the future of pharmaceutical research by boosting the drug discovery process toward affordable and efficient medicaments.

Abstract Coumarin and its derivatives are intriguing to researchers in both chemical and pharmaceutical fields because they have a unique benzopyrone scaffold and a lot of bioactive properties. This review delves into the significance of the coumarin scaffold in the design and engineering of bioactive molecules, offering insights into its chemical, biological, and pharmacological roles. Coumarins are praised for their many medical uses, such as their ability to fight cancer, reduce inflammation, kill microbes, stop blood clots, and act as an antioxidant. This is possible because they have a special chemical structure with α,β-unsaturated α-lactones and electron-rich aromatic rings. The flexibility of this scaffold is amazing; it can be changed chemically in many ways, which lets different derivatives with different biological activities be made. The historical significance of coumarins is underpinned by their natural occurrence in various plants and their pivotal role in therapeutic applications since the 19th century. Their synthetic versatility has led to advancements in drug development, particularly in creating anticoagulants, antivirals, and neuroprotective agents. In addition, coumarins have been shown to work well in cosmetic formulations, cardiovascular health, and diabetes treatments, showing that they can be used for many things. The efficient synthesis, purification, and functionalization of coumarins still face problems. This shows the need for new methods to get around problems like harsh reaction conditions and high costs. New computer techniques, such as 3D-QSAR and pharmacophore modeling, have made it easier and faster to study compounds that are built on a coumarin scaffold. These techniques have also made these compounds more therapeutically useful. This narrative review underscores the coumarin scaffold's prominence in medicinal chemistry and its future prospects as a platform for developing novel bioactive molecules. Coumarins are important for advancing science and health because they can change chemical forms easily and have a wide range of biological effects.

Abstract This review provides a comprehensive overview of heavy metal and lead analysis in environmental, drug, cosmetic, and food pollution samples, focusing on research conducted in Iraq. The explore of the co-occurrence of heavy metals and analytical chemistry techniques in published studies  over the last ten years was achieved by using  bibliometric analysis (VOS viewer) based on database from the Scopus.  For determining heavy metal concentrations in   different environmental samples, various analytical chemistry techniques were assesses in this review such as Inductively Coupled Plasma Mass Spectrometry (ICP-MS), Atomic Absorption Spectrometry (AAS), and X-ray fluorescence (XRF). This study confirms the importance of development of efficient, selective, and sensitive techniques for heavy metal detection in Iraq to reduce their harmful impacts on human health and the environment.

Abstract Coumarin-derived compounds have garnered extensive interest due to their diverse applications in medicinal chemistry, pharmacology, food, and cosmetics. There are pros and cons to both in vitro and in silico methods used in coumarin-based research. The focus is on their roles, pros, and cons in finding out the biomedical properties of these compounds. In vitro studies, conducted in controlled environments, enable detailed investigations into cellular mechanisms, enzyme interactions, and cytotoxic effects. These studies are valuable for elucidating coumarin’s biological activity and therapeutic potential. Although these studies are accurate, morally acceptable, and repeatable, their inability to fully replicate complex biological systems necessitates extrapolation to real-life situations. In contrast, in silico studies leverage computational tools to model molecular interactions, predict pharmacokinetic behaviors, and simulate biological pathways. These techniques are time- and cost-efficient, capable of high-throughput screening, and useful for hypothesis generation. However, their reliability depends on the accuracy of input data and assumptions, which can limit their predictive power in real-world scenarios. Integrating these two study types provides a synergistic framework that enhances coumarin-based research. In silico models can guide the design of experiments, while in vitro assays validate computational predictions. Emerging technologies, such as machine learning, organ-on-a-chip systems, and 3D cell cultures, promise to further refine this integration, enabling faster, more accurate, and ethical research. We conducted an investigation and a literature review, utilizing PubMed data and limiting the publication period from 2000 to mid-2024. This study demonstrates the effective combination of in vitro and in silico methods to advance coumarin-based research and unlock its full therapeutic potential.

Abstract In this study, 2- [2-(5-Chloro carboxy phenyl) azo] 1-methyl imidazole (1-MeCPAI) was synthesized and used in developing hydrogels via free radical polymerization with acrylic acid (AA) and N, N-methylene bisacrylamide. The synthesized P(AA-co-1-MeCPAI) hydrogels were evaluated for their effectiveness in adsorbing R6G dye from aqueous solutions. The characterization of these hydrogels included techniques as Fourier Transform Infrared Spectroscopy (FTIR), Thermal Gravimetric Analysis (TGA), BET (Brunauer-Emmett-Teller) and BJH (Barrett-Joyner-Halenda) analysis, X-ray diffraction (XRD), and Field Emission Scanning Electron Microscopy (FESEM). The study revealed that adsorption efficiency is influenced by pH, temperature, contact time, and adsorbent dose, with adsorption following a pseudo-second-order kinetic model and best fitting the Temkin isotherm, indicating a multilayer adsorption process. Thermodynamic analysis confirmed that the process is exothermic and spontaneous, underscoring the potential of P(AA-co-1-MeCPAI) hydrogels as effective adsorbents for dye removal.

Abstract Switchgrass is a highly promising bioenergy feedstock due to high biomass yield and ability to thrive on marginal lands. Enhancing switchgrass for biofuel production through data-driven breeding and selection is essential to meeting the growing need for sustainable and renewable energy sources. This review critically analyses current approaches and future directions in identifying key phenotypic traits, exploring genetic diversity, and developing predictive models to improve switchgrass. It underscores the importance of high-throughput phenotyping technologies and standardized protocols in pinpointing traits that enhance biofuel yield and conversion efficiency. The review discusses the necessity of comprehensive genotyping and sequencing to understand genetic diversity better and utilize beneficial traits in breeding programs. Moreover, the study highlights the potential of advanced machine learning algorithms and multi-dimensional data integration in creating strong predictive models for breeding decisions. This review provides a roadmap for future research and practical breeding strategies to optimize switchgrass as a bioenergy feedstock.

Abstract This research explored the creation and application of a composite material, SA-g-poly (AAc-IA)/bentonite, for removing Norfloxacin (NOR) from water. Synthesized through copolymerization, the composite's properties were characterized using FTIR, TGA, FESEM, and AFM. FTIR analysis confirmed essential functional groups, with shifts observed in the 1500–1750 cm⁻¹ range post-NOR adsorption. FESEM images showed a porous surface, with particle sizes between 50–200 nm, that became less porous after adsorption. TGA analysis indicated thermal stability up to 300°C, followed by significant decomposition between 300–550°C due to organic component breakdown. Adsorption studies revealed equilibrium within 120 minutes, with a maximum adsorption capacity of 9.99 mg/g. Thermodynamic analysis confirmed a spontaneous and endothermic process, with ΔH of +23.76 kJ/mol. Kinetic modeling showed a pseudo-second-order mechanism, indicating chemisorption. These findings suggest the SA-g-poly (AAc-IA)/bentonite composite is a potentially efficient and cost-effective adsorbent for pharmaceutical wastewater treatment.

Abstract As the global population continues to grow, the need for freshwater is becoming increasingly urgent. It has increased the acceptance of desalination technology, with solar stills emerging as popular, low-cost, and low-maintenance options. However, solar energy still suffers from low freshwater production, and hence, there is a need to evaluate different approaches utilised by researchers to increase productivity. This study presents a systematic review of various active and passive solar stills and the modifications incorporated in their designs to increase productivity. Furthermore, this study also explores the significant advancements in the context of nanoparticles and condensers used to improve the performance of solar stills. To ensure a huge spread in the adoption of solar still-applied science, ongoing research is necessary. The findings of the research work offer findings and data on the continuing state of Solar still applications and suggest possible areas for future research. Finally, this study can aid in the creation of economical and sustainable solutions to expanding freshwater needs.