Adsorption of the azo dyes from wastewater media by a renewable nanocomposite based on the graphene sheets and hydroxyapatite/ZnO nanoparticles

Synthesis and characterization of cellulose fibers modified zinc phosphate/hydroxyapatite core-shell as enhanced carrier of cisplatin: Loading, release, and cytotoxicity

The uncontrolled administration of the cisplatin drug (CPTN) resulted in numerous drawbacks. Therefore, effective, affordable, and biocompatible delivery systems were suggested to regulate the loading, release, and therapeutic effect of CPTN. Zinc phosphate/hydroxyapatite hybrid form (ZP/HP) and core-shell nano-rod morphology, as well as its functionalized derivative with cellulose (CF@ZP/HP), were synthesized by the facile dissolution precipitation method followed by mixing with cellulose fibers, respectively. The developed CF@ZP/HP displayed remarkable enhanced CPTN loading properties (418.2 mg/g) as compared to ZP/HP (259.8 mg/g). The CPTN loading behaviors into CF@ZP/HP follow the Langmuir isotherm properties (R2 > 0.98) in addition to the kinetic activities of the pseudo-first-order model (R2 > 0.96). The steric assessment validates the notable increase in the existing loading receptors after the functionalization of ZP/HP with CF from 57.7 mg/g (ZP/HP) to 90.5 mg/g. The functionalization also impacted the capacity of each existing receptor to be able to ensure 5 CPTN molecules. This, in addition to the loading energies (<40 kJ/mol), donates the loading of CPTN by physical multi-molecular processes and in vertical orientation. The CPTN releasing patterns of CF@ZP/HP exhibit slow and controlled properties (95.7 % after 200 h at pH 7.4 and 100 % after 120 h at pH 5.5), but faster than the properties of ZP/HP. The kinetic modeling of the release activities together with the diffusion exponent (>0.45) reflected the release of CPTN according to both erosion and diffusion mechanisms. The loading of CPTN into both ZP/HP and CF@ZP/HP also resulted in a marked enhancement in the anticancer activity of CPTN against human cervical epithelial malignancies (HeLa) (cell viability = 5.6 % (CPTN), 3.2 % (CPTN loaded ZP/HP), and 1.12 % (CPTN loaded CF@ZP/HP)).

Artificial neural network modeling for the prediction, estimation, and treatment of diverse wastewaters: A comprehensive review and future perspective

The application of artificial neural networks (ANNs) in the treatment of wastewater has achieved increasing attention, as it enhances the efficiency and sustainability of wastewater treatment plants (WWTPs). This paper explores the application of ANN-based models in WWTPs, focusing on the latest published research work, by presenting the effectiveness of ANNs in predicting, estimating, and treatment of diverse types of wastewater. Furthermore, this review comprehensively examines the applicability of the ANNs in various processes and methods used for wastewater treatment, including membrane and membrane bioreactors, coagulation/flocculation, UV-disinfection processes, and biological treatment systems. Additionally, it provides a detailed analysis of pollutants viz organic and inorganic substances, nutrients, pharmaceuticals, drugs, pesticides, dyes, etc., from wastewater, utilizing both ANN and ANN-based models. Moreover, it assesses the techno-economic value of ANNs, provides cost estimation and energy analysis, and outlines promising future research directions of ANNs in wastewater treatment. AI-based techniques are used to predict parameters such as chemical oxygen demand (COD) and biological oxygen demand (BOD) in WWTP influent. ANNs have been formed for the estimation of the removal efficiency of pollutants such as total nitrogen (TN), total phosphorus (TP), BOD, and total suspended solids (TSS) in the effluent of WWTPs. The literature also discloses the use of AI techniques in WWT is an economical and energy-effective method. AI enhances the efficiency of the pumping system, leading to energy conservation with an impressive average savings of approximately 10%. The system can achieve a maximum energy savings state of 25%, accompanied by a notable reduction in costs of up to 30%.

Status and future trends in wastewater management strategies using artificial intelligence and machine learning techniques

The two main things needed to fulfill the world's impending need for water in the face of the widespread water crisis are collecting water and recycling. To do this, the present study has placed a greater focus on water management strategies used in a variety of contexts areas. To distribute water effectively, save it, and satisfy water quality requirements for a variety of uses, it is imperative to apply intelligent water management mechanisms while keeping in mind the population density index. The present review unveiled the latest trends in water and wastewater recycling, utilizing several Artificial Intelligence (AI) and machine learning (ML) techniques for distribution, rainfall collection, and control of irrigation models. The data collected for these purposes are unique and comes in different forms. An efficient water management system could be developed with the use of AI, Deep Learning (DL), and the Internet of Things (IoT) structure. This study has investigated several water management methodologies using AI, DL and IoT with case studies and sample statistical assessment, to provide an efficient framework for water management.

Photocatalytic activity of ZnO doped Nano hydroxyapatite/GO derived from waste oyster shells for removal of Methylene blue

Hydroxyapatite (HAp) stands as an inorganic compound, recognized as a non-toxic, bioactive ceramic, and its composition closely resembles that of bone material. In this study, nHAp was prepared from waste oyster shells, which are biowaste rich in calcium carbonate. nHAp with its unique catalytic property can be used as an adsorbent in various fields, including wastewater treatment. nHAp with an exceptional surface adsorbent with excellent chemical stability, enabling its catalytic function. Nano hydroxyapatite doped with Zinc oxide (ZnO) by wet chemical precipitation and made into a composite with Graphene oxide (GO) by modified hummers method followed by grinding, which was taken as 9:1 ratio (nHAp/ZnO and GO) of weight, enhances its tensile and mechanical strength. The energy band gap of nHAp photocatalyst was evaluated as 3.39 eV and that of the in nHAp/ZnO/GO photocatalyst was narrowed to 1.77 eV. The ternary nanocomposites are very efficient in generating the photogenerated electrons and holes, thereby improving the degradation potential of dye effluents to by-products such as CO2 and H2O. The nanocomposites photocatalyst were characterized by FTIR, XRD, SEM, TEM, EDS, XPS, DRS, and BET techniques. The UV-visible study shows the complete dye degradation efficiency of the prepared nanocomposites photocatalyst. In this study, the prepared nanocomposites nHAp/ZnO/GO have studied their efficiency for the removal of MB dye in a batch process by varying the dosage from 0.1 to 0.5 g, and the effects of dosage variations, pH, kinetic, scavenger study were evaluated at a time interval of 30 min. The removal of dye was found to be 99% at 150 min of 0.3 g dosage and pH = 12 is most favorable as it reached the same percentage at 90 min. The as-prepared nanocomposite nHAp/ZnO/GO fits the kinetic rate constant equation and shows a pseudo-first-order reaction model. This study indicates the suitability for dye removal due to the synergistic effect and electrostatic interaction of the synthesized ternary nanocomposite, which shows the potential, socially active, low-cost-effective, eco-friendly, and safe for photocatalytic degradation of MB from wastewater.

Development of a magnetic nanocomposite sorbent (NiCoMn/Fe3O4@C) for efficient extraction of methylene blue and Auramine O

A rapid and efficient method for the simultaneous monitoring and recovery of Auramine O (AO) and Methylene Blue (MB) dyes from water samples is presented. This method, named ultrasound-assisted dispersive-magnetic nanocomposites-solid-phase microextraction (UA-DMN-μSPE), utilizes NiCoMn/Fe3O4@C composite sorbents. Response surface methodology (RSM) combined with artificial neural networks (ANN) and generalized regression artificial neural network (GRNN) under central composite design (CCD) was employed to optimize various parameters for efficient extraction, followed by further refinement using desirability function analysis (DFA) and genetic algorithms (GA). Under optimized conditions, the method achieved exceptional recovery rates (99.5 ± 1.2% for AO and 99.8 ± 1.1% for MB) with acetone as the eluent. Additionally, a high preconcentration factor of 45.50 and 47.30 for AO and MB, respectively, was obtained. Low detection limits of 0.45 ng mL⁻1 (AO) and 1.80 ng mL⁻1 (MB) were achieved with wide linear response ranges (5-1000 and 5-2000 ng mL⁻1 for AO and MB, respectively). The method exhibited good stability with RSDs below 3% for five recycling runs, and minimal interference from various ions was observed. This UA-DMN-μSPE-UV/Vis method offers significant advantages in terms of efficiency, preconcentration, and detection limits, making it a valuable tool for the analysis of AO and MB in water samples.

Efficient removal of ciprofloxacin and ofloxacin from aqueous solutions using a novel nano-scale adsorbent: Modeling, optimization, and characterization

In the fascinating realm of water purification, our study unveils the remarkable potential of a cutting-edge nano-scale adsorbent-combining graphene oxide (GO), chitosan (CS), and polydopamine (PDA)-in efficiently remove ciprofloxacin (CPF) and ofloxacin (OFL) from aqueous solutions. Our exploration delves deep into the adsorbent's character, utilizing a range of analytical techniques including SEM, RAMAN, FTIR, TGA, BET, XRD, and Zeta potential analyses provided insights into the adsorbent's properties. Modeling the adsorption process with Response Surface Methodology (RSM), Artificial Neural Network (ANN) and General Regression Neural Network (GRNN) indicated excellent predictions by GRNN, with RMSE = 0.0200 and 0.0166, MAE = 0.0082 and 0.0092, as well as AAD = 0.0002 and 0.0006, highlighting its modeling power. Optimization using genetic algorithm (GA) revealed maximum CPF removal efficiency of approximately 95.20% under pH = 6.3, sonication time = 9.0 min, adsorbent dosage = 2.10 g L⁻1, temperature = 45 °C and initial CPF concentration = 90.0 mg L⁻1. Similarly, OFL removal reached about 95.50% under pH = 6.30, sonication time = 8.0 min, adsorbent dosage = 2.0 g L⁻1, temperature = 45 °C and OFL concentration = 115.0 mg L⁻1. RSM optimization closely aligned with GA results. Pseudo-second-order (PSO) kinetic model and Langmuir isotherm model best fitted the experimental data for both antibiotics. Thermodynamic analysis indicated a favorable and spontaneous adsorption process for CPF and OFL. The study concludes that the proposed adsorbents show effectiveness in removing CPF and OFL at lower doses and shorter sonication times compared to various reported adsorbents.

Steric and energetic studies on the influence of cellulose on the adsorption effectiveness of Mg trapped hydroxyapatite for enhanced remediation of chlorpyrifos and omethoate pesticides

Magnesium-trapped hydroxyapatite (Mg.HP) was hybridized with cellulose fiber to produce a bio-composite (CLF/HP) with enhanced adsorption affinities for two types of toxic pesticides (chlorpyrifos (CF) and omethoate (OM)). The enhancement influence of the hybridized cellulose on the adsorption performances of Mg.HP was illustrated based on the determined steric and energetic factors. The computed CF and OM adsorption performances of CLF/HP during the saturation phases are 279.8 mg/g and 317.9 mg/g, respectively, which are significantly higher than the determined values using Mg/HP (143.4 mg/g (CF) and 145.3 mg/g (OM)). The steric analysis demonstrates a strong impact of the hybridization process on the reactivity of the surface of the composite. While CLF/HP reflects effective uptake site densities (Nm) of 93.3 mg/g (CF) and 135.3 mg/g (OM), the estimated values for Mg.HP are 51.2 mg/g (CF) and 46.11 mg/g (OM), which explain the reported enhancement in the adsorption performances of the composite. The capacity of each uptake site to be occupied with more than one molecule (n (CF) = 3-3.74 and n (OM) = 2.35-3.54) suggests multimolecular uptake. The energetic factors suggested physical mechanistic processes of spontaneous and exothermic behaviors either during the uptake of CF or OM.

Double ionic liquid reinforced g-CN nanocomposite for an enhanced adsorption of methylparaben: Mechanism, modeling, and optimization

The efficient removal of organic pollutants, especially pharmaceuticals, from aquatic environments has attracted great attentions. Application of green, multipurpose, and inexpensive compounds is being extensively favorite as adsorbent instead of the traditional chemicals or materials. In this study, sulfonated graphitic carbon nitride was modified with two ionic liquids of polyethyleneimine and choline chloride to create a novel nanocomposite (Sg-CN@IL2 NC) and to use for removal of methylparaben (MeP) from aqueous media. After confirmation of the successful synthesized using different methods, the effective parameters for MeP removal, such as initial MeP concentration, adsorbent dose, sonication time, and temperature, as well as their interactions, were experimentally examined and modeled using response surface methodology (RSM), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN). The models were then optimized using desirability function analysis (DF) and genetic algorithm (GA). The results showed that MeP adsorption: a) can be explained more accurate and reliable using GRNN (AARD% = 11.67, MAE = 15.31, RAE % = 45.42, RRSE % = 55.18, MSE = 435.86, RMSE = 20.70, and R2 = 0.995) than the others; b) reached equilibrium within 7.0 min with a maximum uptake of 267.2 mg/g at a temperature of 45 °C and a neutral pH; c) followed from Freundlich (R2 = 0.999) isotherm and PSO kinetic (R2 = 0.95) models; d) is endothermic and spontaneous; e) is mainly due to π-π stacking, electrostatic and hydrogen bonding interactions. Moreover, Sg-CN@IL2 NC showed an appropriate reusability for up to five cycles. These findings demonstrate the potential of as-prepared NC as an excellent adsorbent for removal of MeP from aqueous media.

Synthesis and characterization of chitosan hybridized zinc phosphate/hydroxyapatite core shell nanostructure and its potentiality as delivery system of oxaliplatin drug

An advanced form of zinc phosphate/hydroxyapatite nanorods with a core-shell structure (ZPh/HPANRs) was made and then hybridized with chitosan polymeric chains to make a safe biocomposite (CH@ZPh/HPANRs) that improves the delivery structure of traditional oxaliplatin (OXPN) chemotherapy during the treatment of colorectal cancer cells. The qualifications of CH@ZPh/HPANRs in comparison with ZPh/HPANRs as a carrier for OXPN were followed based on loading, release, and cytotoxicity. CH@ZPh/HPANRs composite exhibits a notably higher OXPN loading capacity (321.75 mg/g) than ZPh/HPANRs (127.2 mg/g). The OXPN encapsulation processes into CH@ZPh/HPANRs display the isotherm behavior of the Langmuir model (R2 = 0.99) and the kinetic assumptions of pseudo-first-order kinetics (R2 > 0.89). The steric studies reflect a strong increment in the quantities of the free sites after the chitosan hybridization steps (Nm = 34.6 mg/g) as compared to pure ZPh/HPANRs (Nm = 18.7 mg/g). Also, the capacity of each site was enhanced to be loaded by 10 OXPN molecules (n = 9.3) in a vertical orientation. The OXPN loading energy into CH@ZPh/HPANRs (<40 KJ/mol) reflects physical loading reactions involving van der Waals forces and hydrogen bonding. The OXPN release profiles of CH@ZPh/HPANRs exhibit slow and controlled properties for about 140 h at pH 7.4 and 80 h at pH 5.5. The release kinetics and diffusion exponent (>0.45) signify non-Fickian transport and a complex erosion/diffusion release mechanism. The free CH@ZPh/HPANRs particles display a considerable cytotoxic effect on the HCT-116 cancer cells (9.53 % cell viability), and their OXPN-loaded product shows a strong cytotoxic effect (1.83 % cell viability).

Synthesis and characterization of Mg-hydroxyapatite and its cellulose hybridized structure as enhanced bio-carrier of oxaliplatin drug; equilibrium and release kinetics

An advanced form of magnesium-doped hydroxyapatite (Mg HAP) was synthesized and hybridized with cellulose fibers, producing a safe biocomposite (CF/Mg HAP) as an enhanced delivery structure of traditional oxaliplatin (OXPN) chemotherapy drug during the treatment stages of colorectal cancer. The qualifications of CF/Mg HAP as a carrier for OXPN were followed based on loading, release, and cytotoxicity as compared to Mg HAP. The CF/Mg HAP composite exhibits a notably higher OXPN encapsulation capacity (256.2 mg g-1) than the Mg HAP phase (148.9 mg g-1). The OXPN encapsulation process into CF/Mg HAP displays the isotherm behavior of the Langmuir model (R2 = 0.99) and the kinetic assumptions of pseudo-first-order kinetics (R2 > 0.95). The steric studies reflect a strong increment in the quantities of the free sites after the cellulose hybridization steps (Nm = 178.58 mg g-1) as compared to pure Mg HAP (Nm = 69.39 mg g-1). Also, the capacity of each site was enhanced to be loaded by 2 OXPN molecules (n = 1.43) in a vertical orientation. The OXPN encapsulation energy into CF/Mg HAP (<40 kJ mol-1) reflects physical encapsulation reactions involving van der Waals forces and hydrogen bonding. The OXPN release profiles of CF/Mg HAP exhibit slow and controlled properties for about 100 h, either at pH 5.5 or pH 7.4. The release kinetics and diffusion exponent (>0.45) signify non-Fickian transport and a complex erosion/diffusion release mechanism. The free CF/Mg HAP particles display a considerable cytotoxic effect on the HCT-116 cancer cells (21.82% cell viability), and their OXPN-loaded product shows a strong cytotoxic effect (1.85% cell viability).

Synthesis and Characterization of Mg-Hydroxyapatite and Its β-Cyclodextrin Composite as Enhanced Bio-Carrier of 5-Fluorouracil Drug; Equilibrium and Release Kinetics

An advanced form of magnesium-doped hydroxyapatite (Mg·HAP) was integrated in composite with β-cyclodextrin producing a safe biocomposite (β-CD/HAP) as an enhanced delivery structure of traditional 5-fluorouracil (5-FU) chemotherapy during the treatment stages of colorectal cancer cells. The qualifications of β-CD/HAP as a carrier for 5-FU were followed based on the loading, release, and cytotoxicity as compared to Mg·HAP. β-CD/HAP composite exhibits notably higher 5-FU encapsulation capacity (272.3 mg/g) than Mg·HAP phase (164.9 mg/g). The 5-FU encapsulation processes into β-CD/HAP display the isotherm behavior of the Freundlich model (R2 = 0.99) and kinetic assumptions of pseudo-first order kinetic (R2 > 0.95). The steric studies reflect a strong increment in the quantities of the free sites after the β-CD integration steps (Nm = 61.2 mg/g) as compared to pure Mg·HAP (Nm = 42.4 mg/g). Also, the capacity of each site was enhanced to be loaded by 5 of 5-FU molecules (n = 4.45) in a vertical orientation. The 5-FU encapsulation energy into β-CD/HAP (<40 kJ/mol) reflects physical encapsulation reactions involving van der Waals forces and hydrogen bonding. The 5-FU release profiles of β-CD/HAP exhibit slow and controlled properties for about 80 h either in gastric fluid (pH 1.2) or in intestinal fluid (pH 7.4). The release kinetics and diffusion exponent (>0.45) signify non-Fickian transport and complex erosion/diffusion release mechanism. The free β-CD/HAP particles display a considerable cytotoxic effect on the HCT-116 cancer cells (33.62% cell viability) and its 5-FU-loaded product shows a strong cytotoxic effect (2.91% cell viability).

β-cyclodextrin grafted multi-walled carbon nanotubes/chitosan (MWCNT/Cs/CD) nanocomposite for treatment of methylene blue-containing aqueous solutions

β-cyclodextrin (CD) was grafted with multi-walled carbon nanotubes/chitosan (MWCNTs/Cs) to obtain MWCNTs/Cs/CD nanocomposite (NC) for methylene blue (MB) adsorption from aqueous media. TEM, XRD, TGA, Raman spectra, and BET & BJH analyses were utilized to characterize and confirm the successful synthesis of as-prepared NC. MB capture was investigated by considering the parameters of pH (1.9-9.0), temperature (∼16-63 °C), sonication time (∼5-15 min), MB concentration (∼1.2-48 mg/L), and NC dose (0.03-0.26 mg). The obtained responses were then modelled using CCD, generalized regression neural network (GRNN), and least squares support vector machine (LS-SVM), of which the latter found to provide most reliable and accurate results (RMSE = 0.0235, MAE = 0.020, AAD = 0.0047, and R2 = 0.999). Moreover, the genetic algorithm-based optimization results showed that under the respective values of 7.05, 45.5 °C, 10 min, 23 mg/L, 0.12 g, MWCNTs/Cs/CD NC would be able to remove 96.75% of MB with an adsorption capacity of 603 mg/g, through different mechanisms mainly electrostatic interactions. Following from Dubinin-Radushkevich (D-R) isotherm (qs = 460.66 ± 8.9 and R2 > 0.99) and intraparticle diffusion kinetic (R2 = 0.75-0.90) models indicated a chemical adsorption mechanism. Besides, thermodynamic parameters (ΔH◦ = -66.9 kJ/mol, ΔG◦ = between -3.77 kJ/mol and -8.52 kJ/mol, and ΔS◦ = 237.1818 J/mol K) confirmed an endothermic and spontaneous nature for the adsorption. These findings along with appropriate recyclability (five times), turn the as prepared NC to a promising material in removing MB from aqueous solutions.

MgO-La2O3 mixed metal oxides heterostructure catalysts for photodegradation of dyes pollutant: synthesis, characterization and artificial intelligence modelling

In the present work, we prepared MgO-La₂O₃-mixed-metal oxides (MMO) as efficient photocatalysts for degradation of organic pollutants. First, a series of MgAl-%La-CO₃-layered double hydroxide (LDH) precursors with different contents of La (5, 10, and 20 wt%) were synthesized by the co-precipitation process and then calcined at 600 °C. The prepared materials were characterized by XRD, SEM-EDX, FTIR, TGA, ICP, and UV-vis diffuse reflectance spectroscopy. XRD indicated that MgO, La₂O₃, and MgAl₂O₄ phases were found to coexist in the calcined materials. Also, XRD confirms the orthorhombic-tetragonal phases of MgO-La₂O₃. The samples exhibited a small band gap of 3.0-3.22 eV based on DRS. The photocatalytic activity of the catalysts was assessed for the degradation of two dyes, namely, tartrazine (TZ) and patent blue (PB) as model organic pollutants in aqueous mediums under UV-visible light. Detailed photocatalytic tests that focused on the impacts of dopant amount of La, catalyst dose, initial pH of the solution, irradiation time, dye concentration, and reuse were carried out and discussed in this research. The experimental findings reveal that the highest photocatalytic activity was achieved with the MgO-La₂O₃-10% MMO with photocatalysts with a degradation efficiency of 97.4% and 93.87% for TZ and PB, respectively, within 150 min of irradiation. The addition of La to the sample was responsible for its highest photocatalytic activity. Response surface methodology (RSM) and gradient boosting regressor (GBR), as artificial intelligence techniques, were employed to assess individual and interactive influences of initial dye concentration, catalyst dose, initial pH, and irradiation time on the degradation performance. The GBR technique predicts the degradation efficiency results with R² = 0.98 for both TZ and PB. Moreover, ANOVA analysis employing CCD-RSM reveals a high agreement between the quadratic model predictions and the experimental results for TZ and PB (R² = 0.9327 and Adj-R² = 0.8699, R² = 0.9574 and Adj-R² = 0.8704, respectively). Optimization outcomes indicated that maximum degradation efficiency was attained under the following optimum conditions: catalyst dose 0.3 g/L, initial dye concentration 20 mg/L, pH 4, and reaction time 150 min. On the whole, this study confirms that the proposed artificial intelligence (AI) techniques constituted reliable and robust computer techniques for monitoring and modeling the photodegradation of organic pollutants from aqueous mediums by MgO-La₂O₃-MMO heterostructure catalysts.

Efficient removal of levofloxacin by a magnetic NiFe-LDH/N-MWCNTs nanocomposite: Characterization, response surface methodology, and mechanism

Antibiotic pollutants in water bodies, was studied to remove using an oxidized, nitrogen-doped, and Fe₃O₄ and NiFe-LDH decorated MWCNT (magnetic NiFe-LDH/N-MWCNTs) nanocomposite (NC). The novel, engineered NC was characterized by different techniques of SEM, XRD, TEM, EDX, and XPS and then examined under different main effective parameters of NC dose, levofloxacin (LVX) concentration, pH, time, and temprature. The experimentally obtained data then evaluated using the modeling approaches of RSM, GRNN, and ANFIS. The as prepared adsorbent showed an excellent adsorption performance (removal efficiency = 95.28% and adsorption capacity = 344.83-454.55 mg/g) under the respective values of the mentioned parameters of 0.152 g, 23.01 mg/L, 12.00 min, and 37.5 °C, respectively. The comparison of the models showed that although all of them accurately predicted the removal efficiency, ANFIS presented the best capability with R², RMSE, MSE, MAE, as well as AAD of 0.9998, 0.0082, -0.0004, 0.0069, 0.1322, respectively. The adsorption by the NC followed Freundlich isotherm (R² = 0.9993) and PSO kinetic (>0.998) models, confirming a heterogenous chemisorption process. The thermodynamic parameters showed an endothermic and spontaneous nature for LVX removal by magnetic NiFe-LDH/N-MWCNTs NC. A high-performance efficiency, appropriate reusability (five times without loss of efficiency), as well as easy separation due to magnetic properties, makes the NC to a promising option in removing LVX from water.

Synthesis, characterization, and application of graphene oxide/layered double hydroxide /poly acrylic acid nanocomposite (LDH-rGO-PAA NC) for tetracycline removal: A comprehensive chemometric study

Tetracycline (TC), as the second produced and used antibiotic worldwide, is difficult to be entirely metabolized not only in the body, but also in the treatment processes of water and/or wastewater. Therefore, special attention needs to be paid on defining or developing new options for removing such contaminant. Herein, a reduced graphene oxide (GO) was integrated with Ni-Al layered double hydroxide (LDH) as well poly acrylic acid (LDH-rGO-PAA) and examined to reduce TC -as a model antibiotic-in water media under different operational parameters of TC initial concentration, pH, NC dose, and time. The governed behaviour in the adsorption process was investigated using three model methods of response surface methodology (RSM), artificial neural networks (ANN), and general regression neural network (GRNN) after confirming the physico-chemical properties of LDH-rGO-PAA nanocomposite (NC) using different techniques. The LDH-rGO-PAA NC displayed a good performance as either removal efficiency (R = 94.87 ± 0.25%) or adsorption capacity (q_e = 887.5 mg/g) with the respective values of 110 mg/L, 6.3, 20 mg, and 18.50 min for the mentioned factors (TC initial concentration, pH, NC dose, and time, respectively), which was higher than that of reported for the similar adsorbents until now.