Copper Nanoparticle Loading and F Doping of Graphene Aerogel Enhance Its Adsorption of Aqueous Perfluorooctanoic Acid

Adsorption of phosphate onto agricultural waste biochars with ferrite/manganese modified-ball-milled treatment and its reuse in saline soil

Agricultural waste biochar was widely used to absorb phosphorus (P) from eutrophicated water and soil remediation. However, the research on the reuse of the sorbed P on biochar in infertile saline soil is insufficient. Biochars derived from four kinds of agricultural wastes (cotton straws from two origins, maize stalk, and rice husk) were modified and applied to adsorb phosphate in waste water and then be reused in saline soil in this study. The co-modified method combining ball milling and metal coated treatment obtained the higher specific surface area (SSA) of ferrite/manganese modified-ball-milled biochars (Fe/Mn-BMBCs) (226.5-331.5 m2 g-1) than that of pristine biochars (14.02-30.35 m2 g-1) and ferrite/manganese modified biochar (Fe/Mn-BC) (223.7 m2 g-1), which could improve the pore structure of metal modified biochar. The phosphate adsorption capacity (qmax) of Fe/Mn-BMBCs with rich functional groups and high SSA were 44.0-53.8 mg g-1, which was 4.47-5.82 times higher than that of pristine biochars. Fe/Mn-BMBCs showed efficiently adsorption performance at low pH and high temperature. The application of BC to saline soil could promote the availability of P in saline soil. P-loaded biochars could afford P as a nutrient to promote the growth of lettuce (Lactuca sativa L.) in saline soil. The lettuce fresh weight in Fe/Mn-BMBC-P2 treated soil was 8.21 times higher than that grew in control check (CK) treatment. As a P element provider, P-loaded biochars not only improve saline soil fertility and crop productivity, but also convert the agricultural wastes and P in eutrophicated waters to the sustainable resource.

Unveiling nano-empowered catalytic mechanisms for PFAS sensing, removal and destruction in water

Per- and polyfluoroalkyl substances (PFAS) are organofluorine compounds used to manufacture various industrial and consumer goods. Due to their excellent physical and thermal stability ascribed to the strong CF bond, these are ubiquitously present globally and difficult to remediate. Extensive toxicological and epidemiological studies have confirmed these substances to cause adverse health effects. With the increasing literature on the environmental impact of PFAS, the regulations and research have also expanded. Researchers worldwide are working on the detection and remediation of PFAS. Many methods have been developed for their sensing, removal, and destruction. Amongst these methods, nanotechnology has emerged as a sustainable and affordable solution due to its tunable surface properties, high sorption capacities, and excellent reactivities. This review comprehensively discusses the recently developed nanoengineered materials used for detecting, sequestering, and destroying PFAS from aqueous matrices. Innovative designs of nanocomposites and their efficiency for the sensing, removal, and degradation of these persistent pollutants are reviewed, and key insights are analyzed. The mechanistic details and evidence available to support the cleavage of the CF bond during the treatment of PFAS in water are critically examined. Moreover, it highlights the challenges during PFAS quantification and analysis, including the analysis of intermediates in transitioning nanotechnologies from the laboratory to the field.

2D Fluorinated Graphene Oxide (FGO)-Polyethyleneimine (PEI) Based 3D Porous Nanoplatform for Effective Removal of Forever Toxic Chemicals, Pharmaceutical Toxins, and Waterborne Pathogens from Environmental Water Samples

Although water is essential for life, as per the United Nations, around 2 billion people in this world lack access to safely managed drinking water services at home. Herein we report the development of a two-dimensional (2D) fluorinated graphene oxide (FGO) and polyethylenimine (PEI) based three-dimensional (3D) porous nanoplatform for the effective removal of polyfluoroalkyl substances (PFAS), pharmaceutical toxins, and waterborne pathogens from contaminated water. Experimental data show that the FGO-PEI based nanoplatform has an estimated adsorption capacity (qm) of ∼219 mg g-1 for perfluorononanoic acid (PFNA) and can be used for 99% removal of several short- and long-chain PFAS. A comparative PFNA capturing study using different types of nanoplatforms indicates that the qm value is in the order FGO-PEI > FGO > GO-PEI, which indicates that fluorophilic, electrostatic, and hydrophobic interactions play important roles for the removal of PFAS. Reported data show that the FGO-PEI based nanoplatform has a capability for 100% removal of moxifloxacin antibiotics with an estimated qm of ∼299 mg g-1. Furthermore, because the pore size of the nanoplatform is much smaller than the size of pathogens, it has a capability for 100% removal of Salmonella and Escherichia coli from water. Moreover, reported data show around 96% removal of PFAS, pharmaceutical toxins, and pathogens simultaneously from spiked river, lake, and tap water samples using the nanoplatform.

A Review of the Modeling of Adsorption of Organic and Inorganic Pollutants from Water Using Artificial Neural Networks

The application of artificial neural networks on adsorption modeling has significantly increased during the last decades. These artificial intelligence models have been utilized to correlate and predict kinetics, isotherms, and breakthrough curves of a wide spectrum of adsorbents and adsorbates in the context of water purification. Artificial neural networks allow to overcome some drawbacks of traditional adsorption models especially in terms of providing better predictions at different operating conditions. However, these surrogate models have been applied mainly in adsorption systems with only one pollutant thus indicating the importance of extending their application for the prediction and simulation of adsorption systems with several adsorbates (i.e., multicomponent adsorption). This review analyzes and describes the data modeling of adsorption of organic and inorganic pollutants from water with artificial neural networks. The main developments and contributions on this topic have been discussed considering the results of a detailed search and interpretation of more than 250 papers published on Web of Science ® database. Therefore, a general overview of the training methods, input and output data, and numerical performance of artificial neural networks and related models utilized for adsorption data simulation is provided in this document. Some remarks for the reliable application and implementation of artificial neural networks on the adsorption modeling are also discussed. Overall, the studies on adsorption modeling with artificial neural networks have focused mainly on the analysis of batch processes (87%) in comparison to dynamic systems (13%) like packed bed columns. Multicomponent adsorption has not been extensively analyzed with artificial neural network models where this literature review indicated that 87% of references published on this topic covered adsorption systems with only one adsorbate. Results reported in several studies indicated that this artificial intelligence tool has a significant potential to develop reliable models for multicomponent adsorption systems where antagonistic, synergistic, and noninteraction adsorption behaviors can occur simultaneously. The development of reliable artificial neural networks for the modeling of multicomponent adsorption in batch and dynamic systems is fundamental to improve the process engineering in water treatment and purification.