Comprehensive approach integrating remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems.

The current study integrates remote sensing, machine learning, and physicochemical parameters to detect hydrodynamic conditions and groundwater quality deterioration in non-rechargeable aquifer systems. Fifty-two water samples were collected from all water resources in Siwa Oasis and analyzed for physical (pH, T°C, EC, and TDS) chemical (SO4 2-, HCO3 -, NO3 -, Cl-, CO3 2-, SiO2, Mg2+, Na+, Ca2+, and K+), and trace metals (AL, Fe, Sr, Ba, B, and Mn). A digital elevation model supported by machine learning was used to predict the change in the land cover (surface lake area, soil salinity, and water logging) and its effect on water quality deterioration. The groundwater circulation and interaction between the deep aquifer (NSSA) and shallow aquifer (TCA) were detected from the pressure-depth profile of 27 production wells penetrating NSSA. The chemical facies evolution in the aquifer systems were (Ca-Mg-HCO3) in the first stage (freshwater of NSSA) and changed to (Na-Cl) type in the last stage (brackish water of TCA and springs). Support vector machine successfully predicted the rapid increase of the hypersaline lake area from 22.6 km2 to 60.6 km2 within 30 years, which deteriorated a large part of the cultivated land, reflecting the environmental risk of over-extraction of water for irrigation of agricultural land by flooding technique and lack of suitable drainage network. The waterlogging in the study was due to a reduction in the infiltration rate (low permeability) of the soil and quaternary aquifer. The cause of this issue could be a complete saturation of agricultural water with chrysotile, calcite, talc, dolomite, gibbsite, chlorite, Ca-montmorillonite, illite, hematite, kaolinite and K-mica (saturation index >1), giving the chance of these minerals to precipitate in the pore spaces of the soil and decrease the infiltration rate. The NSSA is appropriate for irrigation, whereas TCA is inappropriate due to potential salinity and magnesium risks. The best way to manage water resources in Siwa Oasis could be to use underground drip irrigation and combine water with TCA and NSSA.

Hydrogeochemical characteristics and air quality risks associated with gold mining operations in Egypt using geochemical modeling and risk indices.

The success of industrial operations depends on the effective identification, appraisal, and mitigation of possible hazards and associated environmental concerns. This report provides a complete review of environmental management techniques at the Sukari Gold Mine (SGM), located in the southeastern desert of Egypt. Extensive environmental measurements were taken to assess air and water quality, identify hazards, and analyze risks on the SGM premises. Air quality and noise intensity levels were measured at 39 places around the mine's working region. The findings found noncompliance with the Egyptian Environmental Law's (EEL4/94) noise exposure limitations, with the Power Generator House having the maximum noise levels at 107 dB. Remedial measures such as personal protective equipment (PPE) and exposure limit reduction strategies are being considered to address elevated noise levels. Measurements of particulate matter (PM10) and noxious gases (e.g., CO, SO2, NO2, HCN, and NH3) were conducted in workplace and ambient environments. Elevated PM10 concentrations were particularly concerning in underground regions, forcing the deployment of water depression techniques and improved PPE measures. While gas emissions from most activities remained under regulatory limits, select zones showed hydrogen cyanide (HCN) levels that exceeded permitted thresholds, necessitating specific control actions. Using hazard index (HI) and risk rating assessments, this study found different risk profiles across SGM's workplaces, focusing on high-risk regions for focused intervention. Additionally, a water assessment near a Tailing Storage Facility (TSF) was conducted to monitor the impact of mining activities on groundwater quality. The study revealed that groundwater in the region belongs to the Na-K-Cl-SO4 and Ca-Mg-Cl-SO4 water classes, with potential degradation attributed to high mineralization processes induced by aquifer materials and seawater intrusion. The findings underscore the importance of ongoing monitoring, control measures, and implementation of programs to ensure environmental sustainability and minimize risks associated with mining activities in the Sukari Gold Mines. This research highlights the imperative of continuous monitoring, proactive control measures, and the implementation of environmental initiatives to ensure the sustainability of mining operations within the Sukari Gold Mines.

DNMT inhibitor, 5-aza-2'-deoxycytidine mitigates di(2-ethylhexyl) phthalate-induced aggravation of psoriasiform inflammation in mice via reduction in global DNA methylation in dermal and peripheral compartments.

Psoriasis is classified as an autoimmune disorder characterized by abnormal immune response leading to the development of chronic dermal inflammation. Most individuals have a genetic vulnerability that may be further influenced by epigenetic changes occurring due to multiple variables such as pollutant exposure. Epigenetic modifications such as DNA methylation possess a dynamic nature, enabling cellular differentiation and adaptation by controlling gene expression. Di(2-ethylhexyl) phthalate (DEHP) and psoriatic inflammation are known to cause modification of DNA methylation via DNA methyltransferase (DNMT). However, it is not known whether DEHP, a ubiquitous plasticizer affects psoriatic inflammation via DNMT modulation. Therefore, this study investigated the effect of DNMT inhibitor, 5-aza-2'-deoxycytidine (AZA) on DEHP-induced changes in the expression of DNMT1, global DNA methylation, and anti-/inflammatory parameters (p-STAT3, IL-17A, IL-6, iNOS, IL-10, Foxp3, Nrf2, HO-1) in the skin and the peripheral adaptive/ myeloid immune cells (CD4+ T cells/CD11b+ cells) in imiquimod (IMQ) model of psoriasiform inflammation. Further, psoriasis-associated clinical/histopathological features (ear thickness, ear weight, ear PASI score, MPO activity, and H&E staining of the ear and the back skin) were also analyzed in IMQ model. Our data show that IMQ-treated mice with DEHP exposure had increased DNMT1 expression and DNA methylation which was associated with elevated inflammatory (p-STAT3, IL-17A, IL-6, iNOS) and downregulated anti-inflammatory mediators (IL-10, Foxp3, Nrf2, HO-1) in the peripheral immune cells (CD4+ T cells/CD11b+ cells) and the skin as compared to IMQ-treated mice. Treatment with DNMT1 inhibitor caused reduction in inflammatory and elevation in anti-inflammatory parameters with significant improvement in clinical/histopathological symptoms in both IMQ-treated and DEHP-exposed IMQ-treated mice. In conclusion, our study shows strong evidence indicating that DNMT1 plays an important role in DEHP-induced exacerbation of psoriasiform inflammation in mice through hypermethylation of DNA.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis.

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

Enhanced Retention of Cd(II) by Exfoliated Bentonite and Its Methoxy Form: Steric and Energetic Studies.

Synergistic studies were conducted to evaluate the retention potentiality of exfoliating bentonite (EXBEN) as well as its methanol hybridization derivative (Mth/EXBEN) toward Cd(II) ions to be able to verify the effects of the transformation processes. The adsorption characteristics were established by considering the steric and energetic aspects of the implemented advanced equilibrium simulation, specifically the monolayer model with a single energy level. Throughout the full saturation states, the adsorption characteristics of Cd(II) increased substantially to 363.7 mg/g following the methanol hybridized treatment in comparison to EXBEN (293.2 mg/g) as well as raw bentonite (BEN) (187.3 mg/g). The steric analysis indicated a significant rise in the levels of the active sites following the exfoliation procedure [retention site density (Nm) = 162.96 mg/g] and the chemical modification with methanol [retention site density (Nm) = 157.1 mg/g]. These findings clarify the improvement in the potential of Mth/EXBEN to eliminate Cd(II). Furthermore, each open site of Mth/EXBEN has the capacity to bind approximately three ions of Cd(II) in a vertically aligned manner. The energetic investigations, encompassing the Gaussian energy (less than 8 kJ/mol) plus the adsorption energy (less than 40 kJ/mol), provide evidence of the physical sequestration of Cd(II). This process may involve the collaborative impacts of dipole binding forces (ranging from 2 to 29 kJ/mol) and hydrogen binding (less than 30 kJ/mol). The measurable thermodynamic functions, particularly entropy, internal energy, and free enthalpy, corroborate the exothermic and spontaneous nature of Cd(II) retention by Mth/EXBEN, as opposed to those by EXBEN and BE.

Advanced Equilibrium Studies for the Synergetic Impact of Polyaniline on the Adsorption of Rhodamine B Dye by Polyaniline/Coal Composite.

The synergetic improvement effect of the polyaniline (PANI) hybridization process on the adsorption of rhodamine B dye (RB) by PANI/coal hybrid material (PANI/C) has been evaluated using both traditional equilibrium modeling and advanced isotherm investigations. The composite was prepared by polymerizing polyaniline in the presence of coal fractions with a surface area of 27.7 m2/g. The PANI/C hybrid has an improved capacity to adsorb RB dye (423.5 mg/g) in comparison to coal particles (254.3 mg/g). The maintained increase in the elimination properties of PANI/C has been illustrated using the steric characteristics of active site density (Nm) as well as the total number of adsorbed RB on a single active site (n). However, the incorporation of PANI did not yield any substantial impact on the existing active sites' quantity, but the hybridization processes greatly influenced the selectivity and affinity of each active site, in addition to the aggregation characteristics of the dye as it interacts with the composite's surface. Whereas raw coal can only adsorb three molecules of RB, each active site throughout the PANI/C surface can adsorb approximately eight RB molecules. This is also evidence of RB dye adsorption in a vertical arrangement, which involves multimolecular processes. The Gaussian energy (4.01-5.59 kJ/mol) and adsorption energy (-4.34-4.68 kJ/mol) revealed the controllable impact of physical mechanisms. These mechanisms may include van der Waals forces, dipole-dipole interactions, and hydrogen bonds (<30 kJ/mol). The thermodynamic functions, such as enthalpy, internal energy, and entropy, that have been assessed provide evidence supporting the exothermic and spontaneous nature of the RB uptake processes by PANI/C.

Steric and Energetic Studies on the Synergetic Enhancement Effect of Integrated Polyaniline on the Adsorption Properties of Toxic Basic and Acidic Dyes by Polyaniline/Zeolite-A Composite.

The synergetic enhancement effect of the polyaniline (PANI) integration process on the adsorption properties of the PANI/zeolite-A composite (PANI/ZA) as an adsorbent for malachite green and Congo red synthetic dyes was evaluated based on classic equilibrium modelling in addition to the steric and energetic parameters of advanced isotherm studies. The PANI/ZA composite displays enhanced adsorption capacities for both methylene blue (270.9 mg/g) and Congo red (235.5 mg/g) as compared to ZA particles (methylene blue (179.6 mg/g) and Congo red (140.3 mg/g)). The reported enhancement was illustrated based on the steric parameters of active site density (Nm) and the number of adsorbed dyes per active site (n). The integration of PANI strongly induced the quantities of the existing active sites that have enhanced affinities towards both methylene blue (109.2 mg/g) and Congo red (92.9 mg/g) as compared to the present sites on the surface of ZA. Every site on the surface of PANI/ZA can adsorb about four methylene blue molecules and five Congo red molecules, signifying the vertical orientation of their adsorbed ions and their uptake by multi-molecular mechanisms. The energetic investigation of the methylene blue (-10.26 to -16.8 kJ/mol) and Congo red (-9.38 to -16.49 kJ/mol) adsorption reactions by PANI/ZA suggested the operation of physical mechanisms during their uptake by PANI/ZA. These mechanisms might involve van der Waals forces, dipole bonding forces, and hydrogen bonding (<30 kJ/mol). The evaluated thermodynamic functions, including enthalpy, internal energy, and entropy, validate the exothermic and spontaneous behaviours of the methylene blue and Congo red uptake processes by PANI/ZA.

Synthesis and Characterization of Iron-Rich Glauconite Nanorods by a Facile Sonochemical Method for Instantaneous and Eco-friendly Elimination of Malachite Green Dye from Aquatic Environments.

Novel glauconite nanorods (GNRs) were synthesized by the sonication-induced chemical expansion and scrolling process of natural glauconite. The synthetic nanostructure was characterized by different analytical techniques as a superior adsorbent for the malachite green dye (MG). The synthetic GNRs were detected as porous nanorods with an average length of 150 nm to 5 µm, an average diameter of 25 to 200 nm, and a specific surface area of 123.7 m2/g. As an adsorbent for MG, the synthetic GNRs showed superior uptake capacity up to 1265.6 mg/g at the saturation stage, which is higher than most of the recently developed highly adsorbent dyes. The adsorption behavior and mechanistic properties were depicted by using modern and traditional equilibrium modeling. The kinetic assumption of the pseudo-first-order model (R2 > 0.94) and the classic isotherm of the Langmuir equilibrium model (R2 > 0.97) were used to describe the adsorption reactions. The steric investigation demonstrates that each active site on the surface of GNRs can adsorb up to three MG molecules (n = 2.19-2.48) in vertical orientation involving multimolecular mechanisms. Also, the determined active site density (577.89 mg/g) demonstrates the enrichment of the surface of GNRs with numerous adsorption receptors with strong affinity for the MG dye. The energetic study, including Gaussian energy (6.27-7.97 kJ/mol) and adsorption energy (9.45-10.43 kJ/mol), revealed that GNRs had physically adsorbed the dye, which might involve electrostatic attraction, hydrogen bonding, van der Waals forces, and dipole forces. The internal energy, enthalpy, and entropy determined the exothermic and spontaneous uptake of MG.

Intelligent Modeling and Multi-Response Optimization of AWJC on Fiber Intermetallic Laminates through a Hybrid ANFIS-Salp Swarm Algorithm.

The attainment of intricate part profiles for composite laminates for end-use applications is one of the tedious tasks carried out through conventional machining processes. Therefore, the present work emphasized hybrid intelligent modeling and multi-response optimization of abrasive waterjet cutting (AWJC) of a novel fiber intermetallic laminate (FIL) fabricated through carbon/aramid fiber, reinforced with varying wt% of reduced graphene oxide (r-GO) filled epoxy resin and Nitinol shape memory alloy as the skin material. The AWJC experiments were performed by varying the wt% of r-GO (0, 1, and 2%), traverse speed (400, 500, and 600 mm/min), waterjet pressure (200, 250, and 300 MPa), and stand-off distance (2, 3, and 4 mm) as the input parameters, whereas kerf taper (Kt) and surface roughness (Ra) were considered as the quality responses. A hybrid approach of a parametric optimized adaptive neuro-fuzzy inference system (ANFIS) was adopted through three different metaheuristic algorithms such as particle swarm optimization, moth flame optimization, and dragonfly optimization. The prediction efficiency of the ANFIS network has been found to be significantly improved through the moth flame optimization algorithms in terms of minimized prediction errors, such as mean absolute percentage error and root mean square error. Further, multi-response optimization has been performed for optimized ANFIS response models through the salp swarm optimization (SSO) algorithm to identify the optimal AWJC parameters. The optimal set of parameters, such as 1.004 wt% of r-GO, 600 mm/min of traverse speed, 214 MPa of waterjet pressure, and 4 mm of stand-off distance, were obtained for improved quality characteristics. Moreover, the confirmation experiment results show that an average prediction error of 3.38% for Kt and 3.77% for Ra, respectively, were obtained for SSO, which demonstrates the prediction capability of the proposed optimization algorithm.

Enhanced Congo Red Adsorption and Photo-Fenton Oxidation over an Iron-Impeded Geopolymer from Ferruginous Kaolinite: Steric, Energetic, Oxidation, and Synergetic Studies.

An iron-impeded geopolymer (Fe/GP) was synthesized from natural ferruginous kaolinite and optical waste for enhanced decontamination of Congo red (CR) dye. The adsorption properties of Fe/GP were assessed using an advanced monolayer equilibrium model of one energy (R 2 > 0.99). Fe/GP possessed an active site density of 391.3 mg/g, which induced an adsorption capacity of 634 mg/g at the saturation state. The number of adsorbed CR molecules per site (n = 1.56-1.62) reflected the possible uptake of two molecules per site via a multimolecular mechanism. The adsorption energy (5.12-5.7 kJ/mol) reflected the physical adsorption of the CR molecules via hydrogen bonding and/or van der Waals forces. As a catalyst, notable activity toward photo-Fenton oxidation was achieved even at high CR concentrations. Complete oxidation was observed after 30 (CR concentration: 10 mg/L), 50 (20 mg/L), 80 (30 mg/L), 120 (40 mg/L), and 140 min (50 mg/L). High oxidation efficiency was achieved using 0.1 g/L Fe/GP, 0.1 mL of hydrogen peroxide (H2O2), and a visible light source. Increasing the Fe/GP dosage to 0.3 g/L resulted in complete oxidation of CR (100 mg/L) after 220 min. Therefore, synthetic Fe/GP can be used as a low-cost and superior catalyst and adsorbent for the removal of CR-based contaminants via adsorption or advanced oxidation processes.

Detection of Cardiovascular Disease Based on PPG Signals Using Machine Learning with Cloud Computing.

Hypertension is the main cause of blood pressure (BP), which further causes various cardiovascular diseases (CVDs). The recent COVID-19 pandemic raised the burden on the healthcare system and also limits the resources to these patients only. The treatment of chronic patients, especially those who suffer from CVD, has fallen behind, resulting in increased deaths from CVD around the world. Regular monitoring of BP is crucial to prevent CVDs as it can be controlled and diagnosed through constant monitoring. To find an effective and convenient procedure for the early diagnosis of CVDs, photoplethysmography (PPG) is recognized as a low-cost technology. Through PPG technology, various cardiovascular parameters, including blood pressure, heart rate, blood oxygen saturation, etc., are detected. Merging the healthcare domain with information technology (IT) is a demanding area to reduce the rehospitalization of CVD patients. In the proposed model, PPG signals from the Internet of things (IoT)-enabled wearable patient monitoring (WPM) devices are used to monitor the heart rate (HR), etc., of the patients remotely. This article investigates various machine learning techniques such as decision tree (DT), naïve Bayes (NB), and support vector machine (SVM) and the deep learning model one-dimensional convolutional neural network-long short-term memory (1D CNN-LSTM) to develop a system that assists physicians during continuous monitoring, which achieved an accuracy of 99.5% using PPG-BP data set. The proposed system provides cost-effective, efficient, and fully connected monitoring systems for cardiac patients.

Effective desalination of brackish groundwater using zeolitized diatomite/kaolinite geopolymer as low-cost inorganic membrane; Siwa Oasis in Egypt as a realistic case study.

The salinization of the groundwater wells in Siwa oasis, Egypt represents a critical environmental and economic issue. Developing low-cost, effective, and self-supported inorganic membranes were suggested as suitable desalination techniques. Zeolite/geopolymer (Z/GP) membrane was synthesized as a potential low-cost membrane for effective desalination of brackish groundwater in Siwa Oasis, Egypt. The membrane was synthesized by simple geopolymerization for natural kaolinite and diatomite at room temperature. This was followed by hydrothermal growth of zeolite at 100 °C for 24 h to produce zeolitized geopolymer as potential inorganic membrane. After that, the prepared membrane was incorporated in the pervaporation desalination system considering the effect of the membrane thickness and the temperature. The results demonstrated water flux values of 8.34 kg.m-2.h-1, 7.63 kg.m-2.h-1, and 7.05 kg.m-2.h-1 for the tested membrane at thicknesses of 1 mm, 2 mm, and 3 mm, respectively. This associated with significant salt rejection prearranges 95.8% (1 mm), 97.6% (2 mm), and 99.4% (3 mm). Moreover, the high-temperature value (90 °C) is of strong positive impact on the water flux (7.82 kg.m-2.h-1) and a slight impact on the salt rejection (99.6%). The membrane is of significant stability considering the obtained water flux (7.51 kg.m-2.h-1) and salt rejection (99.57%) after 130 h. The reusability properties of the Z/GP membrane demonstrated its suitability to be used in the desalination process for five runs. Therefore, the synthetic Z/GP membrane is a highly recommended product for simple, effective, low cost, and available desalination technique brackish groundwater in Siwa Oasis.

Sulfonation of Natural Carbonaceous Bentonite as a Low-Cost Acidic Catalyst for Effective Transesterification of Used Sunflower Oil into Diesel; Statistical Modeling and Kinetic Properties.

Bentonite sample enriched in organic matters (oil shale) was functionalized with -SO3H sulfonated carbonaceous bentonite (S-CB) by sulfonation process as a low-cost and effective acidic catalyst for the transesterification spent sunflower oil (SFO). The sulfonation effect was followed by several analytic techniques including X-ray diffraction, Fourier transform infrared, and scanning electron microscopy analysis. The catalytic performance of the sulfonated product was evaluated based on a statistical design which was built according to the response surface methodology and the central composite rotatable design. Using the S-CB acidic catalyst in the transesterification of spent SFO resulted in an actual biodiesel yield of 96% at studied conditions of 85 min at reaction interval, 50 °C as temperature,15:1 as methanol/oil ratio, and 3.5 wt % as S-CB loading. Moreover, the optimization function suggested enhancement to obtained yield up to 97.9% by selecting the values of temperature at 62 °C, the time at 98.5 min, the methanol/SFO ratio at 14.4:1, and S-CB loading at 3.4 wt %. The technical evaluation of the SFO biodiesel reflected the suitability of the product to be used as biofuels according to international standards. The kinetic behavior of the SFO transesterification reaction over S-CB is of pseudo-first order properties and of low activation energy. Finally, the synthetic S-CB as a solid acidic catalyst is of significant reusability and was reused five times with remarkable biodiesel yields.

A Novel Hybrid Approach Based on Deep CNN Features to Detect Knee Osteoarthritis.

In the recent era, various diseases have severely affected the lifestyle of individuals, especially adults. Among these, bone diseases, including Knee Osteoarthritis (KOA), have a great impact on quality of life. KOA is a knee joint problem mainly produced due to decreased Articular Cartilage between femur and tibia bones, producing severe joint pain, effusion, joint movement constraints and gait anomalies. To address these issues, this study presents a novel KOA detection at early stages using deep learning-based feature extraction and classification. Firstly, the input X-ray images are preprocessed, and then the Region of Interest (ROI) is extracted through segmentation. Secondly, features are extracted from preprocessed X-ray images containing knee joint space width using hybrid feature descriptors such as Convolutional Neural Network (CNN) through Local Binary Patterns (LBP) and CNN using Histogram of oriented gradient (HOG). Low-level features are computed by HOG, while texture features are computed employing the LBP descriptor. Lastly, multi-class classifiers, that is, Support Vector Machine (SVM), Random Forest (RF), and K-Nearest Neighbour (KNN), are used for the classification of KOA according to the Kellgren-Lawrence (KL) system. The Kellgren-Lawrence system consists of Grade I, Grade II, Grade III, and Grade IV. Experimental evaluation is performed on various combinations of the proposed framework. The experimental results show that the HOG features descriptor provides approximately 97% accuracy for the early detection and classification of KOA for all four grades of KL.

Instantaneous Adsorption of Synthetic Dyes from an Aqueous Environment Using Kaolinite Nanotubes: Equilibrium and Thermodynamic Studies.

Innovative kaolinite nanotubes (KNTs) are synthesized utilizing a simple technique involving a sonication-induced exfoliation process, followed by chemical scrolling reactions. The KNTs as a material have high reactivity and promising surface area to be used in the purification of water from cationic dyes (safranin (SF) and malachite green (MG)) and anionic dyes (methyl orange (MO) and Congo red (CR)). The kinetic studies of the four dyes SF, MG, CR, and MO show an equilibration time interval of 240 min. The SF, MG, CR, and MO dyes' uptake reactions are in agreement with the kinetic behavior of the pseudo-first-order model and the equilibrium properties of the Langmuir model. Such modeling results, in addition to the Gaussian energies from the Dubinin-Radushkevich (D-R) model (SF (1.01 kJ/mol), MG (1.08 kJ/mol), CR (1.11 kJ/mol), and MO (1.65 kJ/mol)), hypothesize monolayer adsorption of the four dyes by physical reactions. The KNTs show theoretical q max values of 431.6, 489.9, 626.2, and 675.5 (mg/g) for SF, MG, CR, and MO, respectively. The thermodynamic examination of SF, MG, CR, and MO adsorption reactions using KNTs verifies their adsorption by exothermic and spontaneous reactions. The KNT adsorbents achieve promising adsorption results in the presence of different coexisting ions and show significant recyclability properties. Therefore, the production of KNTs from kaolinite shows a strong effect on inducing the textural, physicochemical, and adsorption properties of clay layers as well as their affinity for different species of synthetic dyes.

Sonocogreen Decoration of Clinoptilolite by CaO Nanorods as Ecofriendly Catalysts in the Transesterification of Castor Oil into Biodiesel; Response Surface Studies.

A CaO/clinoptilolite green nanocomposite (CaO/Clino) was synthesized by a green modification technique using calcium nitrate and green tea extract. The CaO/Clino nanocomposite promises a total basicity of 4.82 mmol OH/g, surface area of 252.4 m2/g, and ion exchange capacity of 134.3 mequiv/100 g, which qualifies the product as an effective catalyst in the transesterification of castor oil. The transesterification performance of the CaO/Clino catalyst was addressed statistically based on the response surface methodology and central composite rotatable design, considering the essential experimental parameters. Based on the interaction effect between the studied variables, the CaO/Clino catalyst can achieve an experimental biodiesel yield of 93.8% after 2.5 h at 120 °C with 3.5 wt % catalyst loading and 15:1 ethanol/castor oil molar ratio. The optimization function of the design suggested enhancement in the performance of the CaO/Clino catalyst to achieve a yield of 95.4% if the test time interval increased to 2.65 h and the ethanol content increased to 16:1 as a molar ratio to castor oil. The produced biodiesel over CaO/ClinO has acceptable technical qualifications according to the international requirements (EN 14214 and ASTM D-6751). The synthetic green CaO/Clino nanocomposite has better recyclability as a heterogeneous catalyst and higher activity than some investigated catalysts in literature.

Insights into the green doping of clinoptilolite with Na+ ions (Na+/Clino) as a nanocatalyst in the conversion of palm oil into biodiesel; optimization and mechanism.

The critical demand for eco-friendly, renewable, and safe energy resources is an essential issue encountered in the contemporary world. The catalytic transesterification of plant oils into biodiesel was assessed as promising a technique for providing new forms of clean and safe fuel. Natural clinoptilolite was doped with Na+ ions by green chemical reactions between sodium nitrite and green tea extract, producing a novel modified structure (Na+/Clino). The Na+/Clino product had an enhanced total basicity (6.41 mmol OH/g), ion exchange capacity (387 meq/100 g), and surface area (312.7 m2 g-1), which qualified it to be used as a potential basic catalyst for the transesterification of palm oil. Transesterification tests were statistically assessed using a response surface methodology and a central composite design. Considering the effect of how the significant factors interact with each other, the synthetic Na+/Clino achieved a 96.4% experimental biodiesel yield after 70 min at 100 °C in the presence of 2.75 wt% catalyst loading and a 12.5:1 methanol-to-palm-oil ratio. Based on the optimization function of the statistical model, the performance of Na+/Clino can theoretically be enhanced to increase the yield to 98.2% by expanding the test time to 85 min and the loading value to 3 wt%. The product yielded by the Na+/ClinO process is of adequate technical properties, considering the international levels for standard biodiesel (EN 14214 and ASTM D-6751). Finally, the prepared green Na+ doped clinoptilolite had excellent recyclability as a heterogeneous basic catalyst and displayed higher efficiency than several species of previously studied heterogeneous and homogenous catalysts.

Insight into carbohydrate polymers (chitosan and 2- hydroxyethyl methacrylate/methyl methacrylate) intercalated bentonite-based nanocomposites as multifunctional and environmental adsorbents for methyl parathion pesticide.

Two-hybrid products of bentonite intercalated carbohydrate polymers (chitosan (BE.P.CH) and 2- hydroxyethyl methacrylate/methyl methacrylate copolymer (BE/P.HEMA/MMA)) were synthesized as enhanced adsorbents for methyl parathion pesticide (MPP). The intercalation processes induced the affinity and the capacity of bentonite achieving the best value at pH 8. The maximum MPP adsorption capacities of BE (287.3 mg/g), BE/P.CH (634.5 mg/g), and BE/P.HEMA-MMA (868.5 mg/g) obtained after 300 min, 240 min, and 360 min, respectively. The kinetic properties of BE follow the Pseudo-second order behavior (R2 = 0.93) while BE/P.CH and BE/P.HEMA-MMA are of Pseudo-First order behavior (R2 > 0.92). Based on the equilibrium studies, the three products are of Freundlich isotherm behavior (R2 > 0.9) and the uptake is of multilayer forms on heterogeneous surfaces. The Gaussian energies (>8 KJ/mol), Gibbs free energies (>20 to <40 KJ/mol), and enthalpies (>40 to <80 KJ/mol) give an indication about adsorption mechanism involved chemical and physical reactions. The thermodynamics of MPP uptake reactions by the three products are of endothermic and spontaneous behaviors. The MPP uptake in the presence of NH+4, PO4-3, Mn+2, and Pb+2 competitive ions reflects enhancement in the affinity of BE after the integration between it and the selected polymers.

Enhancing the removal of organic and inorganic selenium ions using an exfoliated kaolinite/cellulose fibres nanocomposite.

Exfoliated kaolinite sheets/cellulose fibres nanocomposite (EXK/CF) was synthesised as a novel hybrid of materials of enhanced surface area and adsorption capacities for inorganic-selenate [Se(VI)] and selenite [Se(IV)]-and organic selenium pollutants-selenomethionine (SeMt). The adsorption reactions of the addressed selenium forms followed pseudo-first-order as a kinetic model and Langmuir as an isotherm model. The fitting results and the calculated Gaussian energies-Se (VI) at 2.0 KJ/mol, Se (IV) at 2.2 KJ/mol, and SeMt at 1.7 KJ/mol-suggested physisorption uptake in a monolayer and homogeneous form. The theoretical maximum selenium uptake capacity (qmax) for Se (VI), Se (IV), and SeMt was 137.5 mg/g, 161.4 mg/g, and 95.4 mg/g, respectively. The thermodynamic investigation verified spontaneous and exothermic properties of the selenium uptake reactions by the EXK/CF composite.

Enhanced Adsorption of Toxic and Biologically Active Levofloxacin Residuals from Wastewater Using Clay Nanotubes as a Novel Fixed Bed: Column Performance and Optimization.

Kaolinite nanotube particles (KNTs) were synthesized by a chemical exfoliation and scrolling process in the existence of sonication waves. The KNT product was identified as a mesoporous material (12 nm in pore diameter) with high surface area (105 m2/g) and promising adsorption affinity for the levofloxacin antibiotic (LVOX) residuals in wastewater. The KNT particles were used as a fixed bed in the continuous adsorption column system for LVOX considering the essential variables. The investigation of the KNT fixed bed in a continuous column for 1800 min verified its suitability to reduce the LVOX content in 9 L of polluted solutions by 80.4%. This was recognized after using the KNT bed of 4 cm in height, a flow rate of 5 mL/min, a pH value of 8, a total flow interval of 1800 min, and an LVOX concentration of 10 mg/L. The regeneration study of the bed declared effective recyclability properties for the KNT particles in the LVOX adsorption column system. The dynamic properties of the KNT bed-based column system were explained based on Thomas, Adams-Bohart, and the Yoon-Nelson kinetic models. The LVOX adsorption reaction by KNTs follows Langmuir behavior with homogeneous and monolayer uptake form. The Gaussian energy (2.05 kJ/mol) and the thermodynamic parameters emphasized physical, spontaneous, and exothermic adsorption reactions for LVOX by KNTs.