Carbon-based metal oxide nanocomposites for water treatment by photocatalytic processes

The increasing contamination of water by emerging contaminants and the need for more efficient and sustainable treatment methods have prompted the exploration of advanced materials and technologies, with a particular focus on photocatalysis. Carbon-based metal oxide nanocomposites are a promising solution for the treatment of polluted water. This paper aims to review the current state of research on the application of these nanocomposites as photocatalysts for complete water treatment, describing breakthroughs in contaminant removal from 2019 through 2024 and milestones in water disinfection from 2016 through 2024. It includes discussion on the utilization of nanocomposites of Metal Oxides (MOs) with carbon materials to improve photocatalytic efficiency and addresses the advantages and drawbacks of these materials, including electron-hole recombination and agglomeration. The review focuses on the photocatalytic mechanisms of these nanocomposites and highlights the importance of heterostructures formed between metal oxides and carbon materials (e.g., graphene, carbon nanotubes, and carbon quantum dots), which enhance light absorption and hydroxyl radical generation, thereby increasing the efficiency of pollutant degradation and water disinfection. The review describes the properties of different MOs (n-type and p-type), exploring synergies between MOs and carbon materials and discussing the benefits and challenges of their application in wastewater treatment and pathogen inactivation. The review ends with a scientometric analysis of research trends in this field.

Evaluating the efficacy of recyclable nanostructured adsorbents for rapid removal of methylparaben from aqueous solutions

In this study, we evaluate the efficiency of two novel nanostructured adsorbents - chitosan-graphitic carbon nitride@magnetite (CS-g-CN@Fe3O4) and graphitic carbon nitride@copper/zinc nanocomposite (g-CN@Cu/Zn NC) - for the rapid removal of methylparaben (MPB) from water. Our characterization methods, aimed at understanding the adsorbents' structures and surface areas, informed our systematic examination of influential parameters including sonication time, adsorbent dosage, initial MPB concentration, and temperature. We applied advanced modeling techniques, such as response surface methodology (RSM), generalized regression neural network (GRNN), and radial basis function neural network (RBFNN), to evaluate the adsorption process. The adsorbents proved highly effective, achieving maximum adsorption capacities of 255 mg g-1 for CS-g-CN@Fe3O4 and 218 mg g-1 for g-CN@Cu/Zn NC. Through genetic algorithm (GA) optimization, we identified the optimal conditions for the highest MPB removal efficiency: a sonication period of 12.00 min and an adsorbent dose of 0.010 g for CS-g-CN@Fe3O4 NC, with an MPB concentration of 17.20 mg L-1 at 42.85 °C; and a sonication time of 10.25 min and a 0.011 g dose for g-CN@Cu/Zn NC, with an MPB concentration of 13.45 mg L-1 at 36.50 °C. The predictive accuracy of the RBFNN and GRNN models was confirmed to be satisfactory. Our findings demonstrate the significant capabilities of these synthesized adsorbents in effectively removing MPB from water, paving the way for optimized applications in water purification.

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.

Bio-inspired, high, and fast adsorption of tetracycline from aqueous media using Fe3O4-g-CN@PEI-β-CD nanocomposite: Modeling by response surface methodology (RSM), boosted regression tree (BRT), and general regression neural network (GRNN)

Because antibiotic-containing wastewaters are able to contaminate all environmental matrices (e.g. water bodies, soil, etc.), a special attention should be paid on developing appropriate materials for their remediation. Herein, the novel nanocomposite (NC) of Fe₃O₄-g-CN@PEI-β-CD was synthesized and employed effectively for the adsorptive removal of tetracycline (TC), the second most produced and employed antibiotic around the world. The successful fabrication of the nanocomposite with a high specific surface area (57.12 m²/g) was confirmed using XRD, SEM, TEM, FTIR, TGA, EDX, and BET analyses. The Fe₃O₄-g-CN@PEI-β-CD NC exhibited fast adsorption rates towards TC and maximum adsorption capacity on the basis of the Langmuir model reached 833.33 mg g^-1, much higher than that reported by different carbon- and/or nano-based materials. The adsorption process was modeled using the approaches of central composite design (CCD), boosted regression tree (BRT), and general regression neural network (GRNN) under various operational conditions of initial TC concentration, pH, adsorbent dose, tempreature, and time. The comparison of the models indicated good predictions of all, however, the BRT model was more accurate compared to the others, with R² = 0.9992, RMSE = 0.0026, MAE = 0.0014, and AAD = 0.0028, proving that it is a powerful approach for modeling TC adsorption by Fe₃O₄-g-CN@PEI-β-CD nanocomposite. The results showed that the order of the variables' effectiveness is as follow: pH > dose > TC concentration. The high adsorption capacity along with high efficiency (98 % in the optimized conditions by GA) ensures the potential of the as-prepared nanocomposite for in situ remediation of antibiotic-containing wastewaters.

Ultrasound-assisted sorption of Pb(ii) on multi-walled carbon nanotube in presence of natural organic matter: an insight into main and interaction effects using modelling approaches of RSM and BRT

In real-scale applications, where NPs are injected into the aqueous environment for remediation, they may interact with natural organic matter (NOM). This interaction can alter nanoparticles' (NPs) physicochemical properties, sorption behavior, and even ecological effects. This study aimed to investigate sorption of Pb(ii) onto multi-walled carbon nanotube (MWCNT) in presence of NOM. The predominant behavior of the process was examined comparatively using response surface methodology (RSM) and boosted regression tree (BRT)-based models. The influence of four main effective parameters, namely Pb(ii) and humic acid (HA) concentrations (mg L⁻¹), pH, and time (min) on Pb removal (%) was evaluated by contributing factor importance rankings (BRT) and analysis of variance (RSM). The applicability of the BRT and RSM models for description of the predominant behavior in the design space was checked and compared using statistics of absolute average deviation (AAD), mean absolute error (MAE), root mean square error (RMSE), and multiple correlation coefficient (R²). The results showed that although both approaches exhibited good performance, the BRT model was more precise, indicating that it could be a powerful method for the modeling of NOM-presence studies. Importance rankings of BRT displayed that the effectiveness order of the studied parameters is pH > time > Pb(ii) concentration > HA concentration. Although HA concentration showed the least effect in comparison with three other studied parameters theoretically, the experimental results revealed that Pb(ii) removal is enhanced in presence of HA (73% vs. 81.77%), which was confirmed by SEM/EDX analyses. Hence, maximum removal (R% = 81.77) was attained at an initial Pb(ii) concentration of 9.91 mg L⁻¹, HA concentration of 0.3 mg L⁻¹, pH of 4.9, and time of 55.2 min.

The proposed mechanism for effect of HA on Pb(ii) removal using MWCNTs.

Sonochemistry: a good, fast and clean method to promote the removal of Cu(ii) and Cr(vi) by MWCNT/CoFe2O4@PEI nanocomposites: optimization study

In this study, branched polyethylenimine (PEI) loaded on magnetic multiwalled carbon nanotubes (MWCNT/CoFe₂O₄) was synthesized and characterized by transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis and Fourier transform infrared spectroscopy (FTIR).