Sonophotocatalysis with Photoactive Nanomaterials for Wastewater Treatment and Bacteria Disinfection.

Sonophotocatalysis is described as a combination of two individual processes of photocatalysis and sonocatalysis. It has proven to be highly promising in degrading dissolved contaminants in wastewaters as well as bacteria disinfection applications. It eliminates some of the main disadvantages observed in each individual technique such as high costs, sluggish activity, and prolonged reaction times. The review has accomplished a critical analysis of sonophotocatalytic reaction mechanisms and the effect of the nanostructured catalyst and process modification techniques on the sonophotocatalytic performance. The synergistic effect between the mentioned processes, reactor design, and the electrical energy consumption has been discussed due to their importance when implementing this novel technology in practical applications, such as real industrial or municipal wastewater treatment plants. The utilization of sonophotocatalysis in disinfection and inactivation of bacteria has also been reviewed. In addition, we further suggest improvements to promote this technology from the lab-scale to large-scale applications. We hope this up-to-date review will advance future research in this field and push this technology toward widespread adoption and commercialization.

Forecasting and Optimizing Dual Media Filter Performance via Machine Learning.

Four different machine learning algorithms, including Decision Tree (DT), Random Forest (RF), Multivariable Linear Regression (MLR), Support Vector Regressions (SVR), and Gaussian Process Regressions (GPR), were applied to predict the performance of a multi-media filter operating as a function of raw water quality and plant operating variables. The models were trained using data collected over a seven year period covering water quality and operating variables, including true colour, turbidity, plant flow, and chemical dose for chlorine, KMnO4, FeCl3, and Cationic Polymer (PolyDADMAC). The machine learning algorithms have shown that the best prediction is at a 1-day time lag between input variables and unit filter run volume (UFRV). Furthermore, the RF algorithm with grid search using the input metrics mentioned above with a 1-day time lag has provided the highest reliability in predicting UFRV with a RMSE and R2 of 31.58 and 0.98, respectively. Similarly, RF with grid search has shown the shortest training time, prediction accuracy, and forecasting events using a ROC-AUC curve analysis (AUC over 0.8) in extreme wet weather events. Therefore, Random Forest with grid search and a 1-day time lag is an effective and robust machine learning algorithm that can predict the filter performance to aid water treatment operators in their decision makings by providing real-time warning of the potential turbidity breakthrough from the filters.

Intestinal colonization with multidrug-resistant Enterobacterales: screening, epidemiology, clinical impact, and strategies to decolonize carriers.

The clinical impact of infections due to extended-spectrum ß-lactamase (ESBL)- and/or carbapenemase-producing Enterobacterales (Ent) has reached dramatic levels worldwide. Infections due to these multidrug-resistant (MDR) pathogens-especially Escherichia coli and Klebsiella pneumoniae-may originate from a prior asymptomatic intestinal colonization that could also favor transmission to other subjects. It is therefore desirable that gut carriers are rapidly identified to try preventing both the occurrence of serious endogenous infections and potential transmission. Together with the infection prevention and control countermeasures, any strategy capable of effectively eradicating the MDR-Ent from the intestinal tract would be desirable. In this narrative review, we present a summary of the different aspects linked to the intestinal colonization due to MDR-Ent. In particular, culture- and molecular-based screening techniques to identify carriers, data on prevalence and risk factors in different populations, clinical impact, length of colonization, and contribution to transmission in various settings will be overviewed. We will also discuss the standard strategies (selective digestive decontamination, fecal microbiota transplant) and those still in development (bacteriophages, probiotics, microcins, and CRISPR-Cas-based) that might be used to decolonize MDR-Ent carriers.

Biotechnological advancements towards water, food and medical healthcare: A review.

The global health status is highly affected by the growing pace of urbanization, new lifestyles, climate changes, and resource exploitation. Modern technologies pave a promising way to deal with severe concerns toward sustainable development. Herein, we provided a comprehensive review of some popular biotechnological advancements regarding the progress achieved in water, food, and medicine, as the most substantial fields related to public health. The emergence of novel organic/inorganic materials has brought about significant improvement in conventional water treatment techniques, anti-fouling approaches, anti-microbial agents, food processing, biosensors, drug delivery systems, and implants. Particularly, a growing interest has been devoted to nanomaterials and their application for developing novel structures or improving the characteristics of standard components. Also, bioinspired materials have been widely used to improve the performance, efficiency, accuracy, stability, safety, and cost-effectiveness of traditional systems. On the other side, the fabrication of innovative devices for precisely monitoring and managing various ecosystem and human health issues is of great importance. Above all, exceptional advancements in designing ion-selective electrodes (ISEs), microelectromechanical systems (MEMs), and implantable medical devices have altered the future landscape of environmental and biomedical research. This review paper aimed to shed light on the wide-ranging materials and devices that have been developed for health applications and mainly focused on the impact of nanotechnology in this field.

A novel, Z-scheme ZnO@AC@FeO photocatalyst, suitable for the intensification of photo-mediated peroxymonosulfate activation: Performance, reactivity and bisphenol A degradation pathways.

In this study, the intensification of a UVC-based PMS activation treatment is performed by a novel photocatalyst. Using ZnO nanoparticles coupled with activated carbon (AC), impregnated by ferroferric oxides (FO, magnetite), as an effective Z-scheme photocatalyst (ZACFO), the effective Bisphenol A (BP-A) removal was attained. Several techniques were applied for the characterization of the as-prepared catalyst and proved the successful preparation of ZACFO. The photocatalytic activity of pristine ZnO was significantly improved after its combination with ACFO. It was found that the fabrication of ZACFO heterostructures could inhibit the charge carriers recombination and also accelerate the charge separation of photo-induced e-/h+ pairs. Under this UVC-based photocatalysis-mediated PMS activation system, ZACFO showed an excellent potential as compared to the single constituent catalysts. The complete degradation of 20 mg/L concentration of BP-A was attained in just 20 min with excellent reaction rate constant of 27.3 × 10-2 min-1. Besides, over 60% of TOC was eliminated by the integrated ZACFO/PMS/UV system within 60 min of reaction. The minor inhibition by most matrix components, the high recycling capability with minor metals' leaching and the effectiveness in complex matrices, constitute this composite method an efficient and promising process for treating real wastewater samples. Finally, based on the photo-produced reactive intermediates and by-products identified, the Z-scheme photocatalytic mechanism and the plausible pathway of BP-A degradation were proposed comprehensively. The presence and role of radical and non-radical pathways in the decontamination process of BP-A over ZACFO/PMS/UV system was confirmed.

Chloramine demand estimation using surrogate chemical and microbiological parameters.

A model is developed to enable estimation of chloramine demand in full scale drinking water supplies based on chemical and microbiological factors that affect chloramine decay rate via nonlinear regression analysis method. The model is based on organic character (specific ultraviolet absorbance (SUVA)) of the water samples and a laboratory measure of the microbiological (Fm) decay of chloramine. The applicability of the model for estimation of chloramine residual (and hence chloramine demand) was tested on several waters from different water treatment plants in Australia through statistical test analysis between the experimental and predicted data. Results showed that the model was able to simulate and estimate chloramine demand at various times in real drinking water systems. To elucidate the loss of chloramine over the wide variation of water quality used in this study, the model incorporates both the fast and slow chloramine decay pathways. The significance of estimated fast and slow decay rate constants as the kinetic parameters of the model for three water sources in Australia was discussed. It was found that with the same water source, the kinetic parameters remain the same. This modelling approach has the potential to be used by water treatment operators as a decision support tool in order to manage chloramine disinfection.

Developing a chloramine decay index to understand nitrification: A case study of two chloraminated drinking water distribution systems.

The management of chloramine decay and the prevention of nitrification are some of the critical issues faced by water utilities that use chloramine as a disinfectant. In this study, potential association between high performance size exclusion chromatography (HPSEC) data obtained with multiple wavelength Ultraviolet (UV) detection from two drinking water distribution systems in Australia and nitrification occurrence was investigated. An increase in the absorbance signal of HPSEC profiles with UV detection at λ=230nm between apparent molecular weights of 200 to 1000Da was observed at sampling sites that experienced rapid chloramine decay and nitrification while its absorbance signal at λ=254nm decreased. A chloramine decay index (C.D.I) defined as the ratio of area beneath the HPSEC spectra at two different wavelengths of 230 and 254nm, was used in assessing chloramine decay occurrences. The C.D.Is of waters at locations that experienced nitrification were consistently higher than locations not experiencing nitrification. A simulated laboratory study showed that the formation of nitrite/nitrate and/or soluble microbial products and/or the release of extracellular polymeric substances (EPS) during nitrification may contribute to the C.D.I. increase. These findings suggest that C.D.I derived from HPSEC with multiple wavelength UV detection could be an informative index to track the occurrence of rapid chloramine decay and nitrification.