Heavy metal (HM) pollution in waterways is a serious threat towards global water security, as high dosages of HM poisoning can significantly harm all living organisms. Researchers have developed promising methods to isolate, separate, or reduce these HMs from water bodies to overcome this. This includes techniques, such as adsorption, photocatalysis, and membrane removal. Nanomaterials play an integral role in all of these remediation techniques. Nanomaterials of different shapes have been atomically designed via various synthesis techniques, such as hydrothermal, wet chemical synthesis, and so on to develop unique nanomaterials with exceptional properties, including high surface area and porosity, modified surface charge, increment in active sites, enhanced photocatalytic efficiency, and improved HM removal selectivity. In this work, a comprehensive review on the role that nanomaterials play in removing HM from waterways. The unique characteristics of the nanomaterials, synthesis technique, and removal principles are presented. A detailed visualisation of HM removal performances and the mechanisms behind this improvement is also detailed. Finally, the future directions for the development of nanomaterials are highlighted.
The presence of conventional and emerging pollutants infiltrating into our water bodies is a course of concern as they have seriously threatened water security. Established techniques such as photocatalysis and membrane technology have proven to be promising in removing various persistent organic pollutants (POP) from wastewaters. The emergence of hybrid photocatalytic membrane which incorporates both photocatalysis and membrane technology has shown greater potential in treating POP laden wastewater based on their synergistic effects. This article provides an in-depth review on the roles of both photocatalysis and membrane technology in hybrid photocatalytic membranes for the treatment of POP containing wastewaters. A concise introduction on POP's in terms of examples, their origins and their effect on a multitude of organisms are critically reviewed. The fundamentals of photocatalytic mechanism, current directions in photocatalyst design and their employment to treat POP's are also discussed. Finally, the challenges and future direction in this field are presented.
In this study, fouling mechanism and modelling analysis of synthetic lignocellulose biomass and agricultural palm oil effluent was studied using polyethersulfone (PES) ultrafiltration (UF) 10 kDa membrane. The impact of process variables (transmembrane pressure (TMP), pH and concentration of feed solution) on lignocellulosic flux was analysed using pore blocking model. The feasible approaches on utilising deep learning artificial neural network (ANN) to predict smaller flux datasets are studied. Among the input variables, pH of lignin feed solution has significant control towards flux and lignin rejection coefficient for both lignin and lignocellulosic solution. Alteration in the structure of lignin at different pH conditions contributed in the improvement of lignin rejection coefficient to 0.98 at the feed pH of 9. A maximum steady state flux of 52.03 L/m2h was observed at the lower lignin concentration (0.25 g/L), TMP of 200 kPa and feed pH of 3. At high TMP and concentration, lignin rejection decreased due to enhancement of feed concentration on membrane surface. The mechanistic model exhibited that cake layer phenomena was dominant in both lignin and lignocellulosic solution. The proposed ANN model showed good correlation (R2-1.00) with experimental non-linear flux dynamic data of both lignin and synthetic lignocellulosic solution. In ANN analysis, activation function, algorithm and neuron effect have significant effect in design of accurate model for prediction of small flux datasets. Aerobically-treated palm oil mill filtration analysis also showed that cake layer phenomenon was dominant. A water recovery of 82 % was achieved even at low TMP under short durations.