A state-of-the-art review of metal oxide nanoflowers for wastewater treatment: Dye removal.

Dye wastewater consists of high solids concentrations, heavy metals, minor contaminants, dissolved chemical oxygen demand, and microorganisms. Nanoflowers are nanoparticles that resemble flowers when viewed at a microscopic level. Inorganic metal oxide nanoflowers have been discovered to be a potential source for overcoming this situation. Their flower-like features give them a higher surface area to volume ratio and porosity structure, which can absorb a significant amount of dye. The metal oxide nanoflower synthesized from different synthesis methods is used to compare which one is cost-effective and capable of generating a large scale of nanoflower. This review has demonstrated outstanding dye removal efficiency by applying inorganic nanoflowers to dye removal. Since both adsorption and photocatalytic reactions enhance the dye degradation process, complete dye degradation could be achieved. Meanwhile, the inorganic metal oxide nanoflowers' exemplary reusability characteristics with negligible performance drop further prove that this approach is highly sustainable and may help to save costs. This review has proven the momentum of obtaining high dye removal efficiency in wastewater treatment to conclude that the metal oxide nanoflower study is worth researching.

Advanced growth of 2D MXene for electrochemical sensors.

Over the last few years, electroanalysis has made significant advancements, particularly in developing electrochemical sensors. Electrochemical sensors generally include emerging Photoelectrochemical and Electrochemiluminescence sensors, which combine optical techniques and traditional electrochemical bio/non-biosensors. Numerous EC-detecting methods have also been designed for commercial applications to detect biological and non-biological markers for various diseases. Analytical applications have recently focused significantly on one of the novel nanomaterials, the MXene. This material is being extensively investigated for applications in electrochemical sensors due to its unique mechanical, electronic, optical, active functional groups and thermal characteristics. This study extensively discusses the salient features of MXene-based electrochemical sensors, photoelectrochemical sensors, enzyme-based biosensors, immunosensors, aptasensors, electrochemiluminescence sensors, and electrochemical non-biosensors. In addition, their performance in detecting various substances and contaminants is thoroughly discussed. Furthermore, the challenges and prospects the MXene-based electrochemical sensors are elaborated.

Recent trends in MXene-based material for biomedical applications.

MXene is a magical class of 2D nanomaterials and emerging in many applications in diverse fields. Due to the multiple advantageous characteristics of its fundamental components, such as structural, physicochemical, optical, and occasionally even biological characteristics. However, it is limited in the biomedical industry due to poor physiological stability, decomposition rate, and lack of controlled and sustained drug release. These limitations can be overcome when MXene forms composites with other 2D materials. The efficiency of pure MXene in biomedicine is inferior to that of MXene-based composites. The availability of functionality on the exterior part of MXene has a key role in the modification of their surface and their characteristics. This review provides an extensive discussion on the synthesizing of MXene and the role of the surface functionalities on the efficiency of MXene. In addition, a detailed discussion of the biomedical applications of MXene, including antibacterial activity, regenerative medicine, CT scan capability, drug delivery, diagnostics, MRI and biosensing capability. Furthermore, an outline of the future problems and challenges of MXene-based materials for biomedical applications was narrated. Thus, these salient features showcase the potential of MXene-based material and will be a breakthrough in biomedical applications in the near future.

Insights into chitosan-based cellulose nanowhiskers reinforced nanocomposite material via deep eutectic solvent in green chemistry.

In the present work, the synthesis of cellulose nanowhiskers (CNW)/chitosan nanocomposite films via deep eutectic solvents (DES) changing the chemical structures were carried out. It was observed that a pure chitosan film has broadband at 3180-3400 cm-1, indicating amide and hydroxyl groups. Upon CNW incorporation, the peak gets sharper and stronger and shifts to a greater wavelength. Further, the addition of DES infuses more elements of amide into the nanocomposite films. Moreover, the mechanical properties incorporating CNW filler into a chitosan matrix show an enhancement in tensile strength (TS), Young's modulus (YM), and elongation at break. The TS and YM increase while the elongation decrease as the CNW concentration increases. The YM of biocomposite films is increased to 723 MPa at 25% CNW into chitosan films. Besides, the TS has enhanced to 11.48 MPa at 15% CNW concentration in the biocomposite films. The elongation at break has decreased to 11.7% at 25% CNW concentration. Hence, incorporating CNW into the chitosan matrix via DES can still improve the mechanical properties of the nanocomposite films. Therefore, the application of DES results in a lower YM and TS as the films are hygroscopic. In conclusion, DES can be considered the new green solvent media for synthesizing materials. It has the potential to replace ionic liquids due to its biodegradability and non-toxic properties while preserving the character of low-vapour pressure. Besides that, chitosan can be used as potential material for applications in process industries, such as the biomedical and pharmaceutical industries. Thus, DES can be used as a green solvent and aim to reduce the toxic effect of chemicals on the environment during chemical production.

Techno-economic assessment of hydrotreated vegetable oil as a renewable fuel from waste sludge palm oil.

To date, the development of renewable fuels has become a normal phenomenon to solve the problem of diesel fuel emissions and the scarcity of fossil fuels. Biodiesel production has some limitations, such as two-step processes requiring high free fatty acids (FFAs), oil feedstocks and gum formation. Hydrotreated vegetable oil (HVO) is a newly developed international renewable diesel that uses renewable feedstocks via the hydrotreatment process. Unlike FAME, FFAs percentage doesn't affect the HVO production and sustains a higher yield. The improved characteristics of HVO, such as a higher cetane value, better cold flow properties, lower emissions and excellent oxidation stability for storage, stand out from FAME biodiesel. Moreover, HVO is a hydrocarbon without oxygen content, but FAME is an ester with 11% oxygen content which makes it differ in oxidation stability. Waste sludge palm oil (SPO), an abundant non-edible industrial waste, was reused and selected as the feedstock for HVO production. Techno-economical and energy analyses were conducted for HVO production using Aspen HYSYS with a plant capacity of 25,000 kg/h. Alternatively, hydrogen has been recycled to reduce the hydrogen feed. With a capital investment of RM 65.86 million and an annual production cost of RM 332.56 million, the base case of the SPO-HVO production process was more desirable after consideration of all economic indicators and HVO purity. The base case of SPO-HVO production could achieve a return on investment (ROI) of 89.03% with a payback period (PBP) of 1.68 years. The SPO-HVO production in this study has observed a reduction in the primary greenhouse gas, carbon dioxide (CO2) emission by up to 90% and the total annual production cost by nearly RM 450 million. Therefore, SPO-HVO production is a potential and alternative process to produce biobased diesel fuels with waste oil.

Silica quantum dots; an optical nanosensing approach for trace detection of pesticides in environmental and biological samples.

Both the environment and human health have suffered as a result of excessive and irrational pesticide use. The human body is vulnerable to a wide range of illnesses brought on by prolonged exposure to or intake of food contaminated with pesticide residues, including immunological and hormonal abnormalities and the development of certain tumors. Sensors based on nanoparticles stand out from more conventional spectrophotometry analytical methods due to their low detection limits, high sensitivity, and ease of use; that is why the demand for simple, fast, and less expensive sensing methods increases daily and presents myriad uses. Such demands are fulfilled by employing paper-based analytical devices having intrinsic properties. The presented work reports an on-site, easy-to-handle, and disposable paper-based sensing device for performing fast screening along with readout from a smartphone. The fabricated device utilizes luminescent silica quantum dots, immobilized into a paper cellulose matrix, and the resonance energy transfer phenomenon is employed. The silica quantum dots probes were fabricated from citric acid and, by undergoing physical adsorption, were confined on the nitrocellulose substrate in small wax-traced spots. The silica quantum dots were excited by smartphone ultraviolet LED, acting as an energy source and for capturing the image. The obtained LOD is 0.054 µM, and the coefficient of variation is less than 6.1%, comparable to the result obtained by UV-Visible and fluorometric analysis under similar experimental conditions. In addition, high reproducibility (≥9.8%) and high recovery ≥90% were obtained in spiked blood samples. The fabricated sensor sensitively detected pesticides giving a LOD of 2.5 ppm along with the development of yellow color within a short period of 5 min. The sensor functions well when sophisticated instrumentation is not accessible. The presented work shows the potential of the paper strip for the on-site detection of pesticides in biological and environmental samples.

Hydrothermal synthesis of V2O5/TiO2 decorated graphitic carbon nitride nanocomposite for biomolecule oxidation inhibition and dye degradation applications.

Oxides of vanadium, titanium and graphitic carbon nitride (g-C3N4) are well known for their catalytic activities. In order to achieve synergic catalytic effects, a novel nanocomposite (NC) i.e. V2O5/TiO2/g-C3N4 has been synthesized by a very simple, ecofriendly and nonhazardous hydrothermal method. The fabricated NC was characterized employing UV-Visible, FTIR, SEM, and XRD techniques. UV-Visible and FTIR analysis indicated the formation of the nanocomposite and XRD analysis confirmed the association of V2O5 and TiO2 with g-C3N4 in nanocomposite. SEM study indicated the hetero-structure of NC having size ranging from 50 to 80 nm and it was found having hexagonal crystallite structure. The synthesized nanocomposite exhibited excellent scavenging of free radicals DPPH● (91%) and ABTS●+ (64%) that are responsible for the oxidation of biomolecules. Therefore, NC can be claimed having biomolecule oxidation protective potential. In addition, photocatalytic ability for the degradation of methylene blue (MB) and methyl orange (MO) was also achieved up to 94% and 89% respectively. The synthesized novel nanocomposite exhibited excellent potential to remove free radicals and dyes from aqueous medium which can be further used for the environmental remediation.

Predictive capability evaluation and optimization of Pb(II) removal by reduced graphene oxide-based inverse spinel nickel ferrite nanocomposite.

Pb(II) is a heavy metal that is a prominent contaminant in water contamination. Among the different pollution removal strategies, adsorption was determined to be the most effective. The adsorbent and its type determine the adsorption process's efficiency. As part of this effort, a magnetic reduced graphene oxide-based inverse spinel nickel ferrite (rGNF) nanocomposite for Pb(II) removal is synthesized, and the optimal values of the independent process variables (like initial concentration, pH, residence time, temperature, and adsorbent dosage) to achieve maximum removal efficiency are investigated using conventional response surface methodology (RSM) and artificial neural networks (ANN). The results indicate that the initial concentration, adsorbent dose, residence time, pH, and process temperature are set to 15 mg/L, 0.55 g/L, 100 min, 5, and 30 °C, respectively, the maximum removal efficiency (99.8%) can be obtained. Using the interactive effects of process variables findings, the adsorption surface mechanism was examined in relation to process factors. A data-driven quadratic equation is derived based on the ANOVA, and its predictions are compared with ANN predictions to evaluate the predictive capabilities of both approaches. The R2 values of RSM and ANN predictions are 0.979 and 0.991 respectively and confirm the superiority of the ANN approach.

Process modeling, characterization, optimization, and mechanisms of fluoride adsorption using magnetic agro-based adsorbent.

In this study, fluoride removal from polluted potable water using magnetic carbon-based adsorbents derived from agricultural biomass was thoroughly investigated. An experimental matrix is designed considering the interactive effects of independent process variables (pH, adsorbent dose, contact time, and initial fluoride concentration) on the removal efficiency. Isotherms and kinetics studies, as well as anions interactions, were also investigated to understand the adsorption mechanisms further. The model parameters of isotherms and kinetics are estimated using nonlinear differential evolution optimization (DEO). Approaches like adaptive neuro-fuzzy inference system (ANFIS) and response surface methodology (RSM) are implemented to predict the fluoride removal and identify the optimal process values. The optimum removal efficiency of GAC-Fe3O4 (89.34%) was found to be higher than that of PAC-Fe3O4 (85.14%). Kinetics experiments indicated that they follow the intraparticle diffusion model, and adsorption isotherms indicated that they follow Langmuir and Freundlich models. Both PAC-Fe3O4 and GAC-Fe3O4 adsorbents have shown an adsorption capacity of 1.20 and 2.74 mg/g, respectively. The model predictions from ANFIS have a strong correlation with experimental results and superior to RSM predictions. The shape of the contours depicts the nonlinearity of the interactive effects and the mechanisms in the adsorption process.

Modeling and optimization by particle swarm embedded neural network for adsorption of methylene blue by jicama peroxidase immobilized on buckypaper/polyvinyl alcohol membrane.

Jicama peroxidase (JP) immobilized functionalized Buckypaper/Polyvinyl alcohol (BP/PVA) membrane was synthesized and evaluated as a promising nanobiocomposite membrane for methylene blue (MB) dye removal from aqueous solution. The effects of independent process variables, including pH, agitation speed, initial concentration of hydrogen peroxide (H2O2), and contact time on dye removal efficiency were investigated systematically. Both Response Surface Methodology (RSM) and Artificial Neural Network coupled with Particle Swarm Optimization (ANN-PSO) approaches were used for predicting the optimum process parameters to achieve maximum MB dye removal efficiency. The best optimal topology for PSO embedded ANN architecture was found to be 4-6-1. This optimized network provided higher R2 values for randomized training, testing and validation data sets, which are 0.944, 0.931 and 0.946 respectively, thus confirming the efficacy of the ANN-PSO model. Compared to RSM, results confirmed that the hybrid ANN-PSO shows superior modeling capability for prediction of MB dye removal. The maximum MB dye removal efficiency of 99.5% was achieved at pH-5.77, 179 rpm, ratio of H2O2/MB dye of 73.2:1, within 229 min. Thus, this work demonstrated that JP-immobilized BP/PVA membrane is a promising and feasible alternative for treating industrial effluent.

A comprehensive review of applications of magnetic graphene oxide based nanocomposites for sustainable water purification.

With the rapid growth of industrialization, water bodies are polluted with heavy metals and toxic pollutants. In pursuit of removal of toxic pollutants from the aqueous environment, researchers have been developed many techniques. Among these techniques, magnetic separation has caught research attention, as this approach has shown excellent performance in the removal of toxic pollutants from aqueous solutions. However, magnetic graphene oxide based nanocomposites (MGO) possess unique physicochemical properties including excellent magnetic characteristics, high specific surface area, surface active sites, high chemical stability, tunable shape and size, and the ease with which they can be modified or functionalized. As results of their multi-functional properties, affordability, and magnetic separation capability, MGO's have been widely used in the removal of heavy metals, radionuclides and organic dyes from the aqueous environment, and are currently attracting much attention. This paper provides insights into preparation strategies and approaches of MGO's utilization for the removal of pollutants for sustainable water purification. It also reviews the preparation of magnetic graphene oxide nanocomposites and primary characterization instruments required for the evaluation of structural, chemical and physical functionalities of synthesized magnetic graphene oxide nanocomposites. Finally, we summarized some research challenges to accelerate the synthesized MGO's as adsorbents for the treatment of water pollutants such as toxic and radioactive metal ions and organic and agricultural pollutants.

Modeling and optimization by particle swarm embedded neural network for adsorption of zinc (II) by palm kernel shell based activated carbon from aqueous environment.

Zn (II) is one the common pollutant among heavy metals found in industrial effluents. Removal of pollutant from industrial effluents can be accomplished by various techniques, out of which adsorption was found to be an efficient method. Applications of adsorption limits itself due to high cost of adsorbent. In this regard, a low cost adsorbent produced from palm oil kernel shell based agricultural waste is examined for its efficiency to remove Zn (II) from waste water and aqueous solution. The influence of independent process variables like initial concentration, pH, residence time, activated carbon (AC) dosage and process temperature on the removal of Zn (II) by palm kernel shell based AC from batch adsorption process are studied systematically. Based on the design of experimental matrix, 50 experimental runs are performed with each process variable in the experimental range. The optimal values of process variables to achieve maximum removal efficiency is studied using response surface methodology (RSM) and artificial neural network (ANN) approaches. A quadratic model, which consists of first order and second order degree regressive model is developed using the analysis of variance and RSM - CCD framework. The particle swarm optimization which is a meta-heuristic optimization is embedded on the ANN architecture to optimize the search space of neural network. The optimized trained neural network well depicts the testing data and validation data with R2 equal to 0.9106 and 0.9279 respectively. The outcomes indicates that the superiority of ANN-PSO based model predictions over the quadratic model predictions provided by RSM.

Optimization and modeling of methyl orange adsorption onto polyaniline nano-adsorbent through response surface methodology and differential evolution embedded neural network.

Presence of pigments and dyes in water bodies are growing tremendously and pose as toxic materials and have severe health effects on human and aquatic creatures. Treatments methods for removal of these toxic dyes along with other pollutants are growing in different dimensions, among which adsorption was found a cheaper and efficient method. In this study, the performance of polyaniline-based nano-adsorbent for removal of methyl orange (MO) dye from wastewater in a batch adsorption process is studied. Along with this to minimize the number of experiments and obtain optimal conditions, a multivariate predictive model based on response surface methodology (RSM) is developed. This is compared with data-driven modeling using the artificial neural network (ANN) which is integrated with differential evolution optimization (DEO) for prediction of the adsorption of MO. The interactive effects on MO removal efficiency with respect to independent process variables were investigated. The fit of the predictive model was found to good enough with R2 = 0.8635. The optimal ANN architecture with 5-12-1 topology resulted in higher R2 and lower RMSE of 0.9475 and 0.1294 respectively. Pearson's Chi-square measure which provides a good measurement scale for weighing the goodness of fit is found to be 0.005 and 0.038 for RSM and ANN-DEO respectively, and other statistical metrics evaluated in this study further confirms that the ANN-DEO is very superior over RSM for model predictions.

Exposure to environmental pollutants: A mini-review on the application of wastewater-based epidemiology approach.

Wastewater-based epidemiology (WBE) is considered an innovative and promising tool for estimating community exposure to a wide range of chemical and biological compounds by analyzing wastewater. Despite scholars' interest in WBE studies, there are uncertainties and limitations associated with this approach. This current review focuses on the feasibility of the WBE approach in assessing environmental pollutants, including pesticides, heavy metals, phthalates, bisphenols, and personal care products (PCPs). Limitations and challenges of WBE studies are initially discussed, and then future perspectives, gaps, and recommendations are presented in this review. One of the key limitations of this approach is the selection and identification of appropriate biomarkers in studies. Selecting biomarkers considering the basic requirements of a human exposure biomarker is the most important criterion for validating this new approach. Assessing the stability of biomarkers in wastewater is crucial for reliable comparisons of substance consumption in the population. However, directly analyzing wastewater does not provide a clear picture of biomarker stability. This uncertainty affects the reliability of temporal and spatial comparisons. Various uncertainties also arise from different steps involved in WBE. These uncertainties include sewage sampling, exogenous sources, analytical measurements, back-calculation, and estimation of the population under investigation. Further research is necessary to ensure that measured pollutant levels accurately reflect human excretion. Utilizing data from WBE can support healthcare policy in assessing exposure to environmental pollutants in the general population. Moreover, WBE seems to be a valuable tool for biomarkers that indicate healthy conditions, lifestyle, disease identification, and exposure to pollutants. Although this approach has the potential to serve as a biomonitoring tool in large communities, it is necessary to monitor more metabolites from wastewater to enhance future studies.

Prediction of CO2 solubility in Ionic liquids for CO2 capture using deep learning models.

Ionic liquids (ILs) are highly effective for capturing carbon dioxide (CO2). The prediction of CO2 solubility in ILs is crucial for optimizing CO2 capture processes. This study investigates the use of deep learning models for CO2 solubility prediction in ILs with a comprehensive dataset of 10,116 CO2 solubility data in 164 kinds of ILs under different temperature and pressure conditions. Deep neural network models, including Artificial Neural Network (ANN) and Long Short-Term Memory (LSTM), were developed to predict CO2 solubility in ILs. The ANN and LSTM models demonstrated robust test accuracy in predicting CO2 solubility, with coefficient of determination (R2) values of 0.986 and 0.985, respectively. Both model's computational efficiency and cost were investigated, and the ANN model achieved reliable accuracy with a significantly lower computational time (approximately 30 times faster) than the LSTM model. A global sensitivity analysis (GSA) was performed to assess the influence of process parameters and associated functional groups on CO2 solubility. The sensitivity analysis results provided insights into the relative importance of input attributes on output variables (CO2 solubility) in ILs. The findings highlight the significant potential of deep learning models for streamlining the screening process of ILs for CO2 capture applications.

Synthesis, molecular docking studies and biological evaluation of N-(4-oxo-2-(trifluoromethyl)-4H-chromen-7-yl) benzamides as potential antioxidant, and anticancer agents.

A series of novel chromone derivatives of (N-(4-oxo-2-(trifluoromethyl)-4H-chromen-6-yl) benzamides) were synthesized by treating 7-amino-2-(trifluoromethyl)-4H-chromen-4-one with K2CO3 and/or NaH, suitable alkyl halides and acetonitrile and/or 1,4-dioxane. The obtained products are in high yields (87 to 96%) with various substituents in short reaction times with no more by-products and confirmed by FT-IR, 1H, and 13C-NMR Spectral data. The in vitro cytotoxic activity was examined against two human cancer cell lines, namely the human lung adenocarcinoma (A-549) and the human breast (MCF-7) cancer cell line. Compound 4h showed promising cytotoxicity against both cell lines with IC50 values of 22.09 and 6.40 ± 0.26 µg/mL respectively, compared to that of the standard drug. We also performed the in vitro antioxidant activity by DPPH radical, hydrogen peroxide, NO scavenging, and total antioxidant capacity (TAC) assay methods, and they showed significant activities. The possible binding interactions of all the synthesized chromone derivatives are also investigated against selective pharmacological targets of human beings, such as HERA protein for cytotoxic activity and Peroxiredoxins (3MNG) for antioxidant activity which showed closer binding free energies than the standard drugs and evidencing the above two types of activities.

Insights into kinetics, thermodynamics, and mechanisms of chemically activated sunflower stem biochar for removal of phenol and bisphenol-A from wastewater.

This study synthesized a highly efficient KOH-treated sunflower stem activated carbon (KOH-SSAC) using a two-step pyrolysis process and chemical activation using KOH. The resulting material exhibited exceptional properties, such as a high specific surface area (452 m2/g) and excellent adsorption capacities for phenol (333.03 mg/g) and bisphenol A (BPA) (365.81 mg/g). The adsorption process was spontaneous and exothermic, benefiting from the synergistic effects of hydrogen bonding, electrostatic attraction, and stacking interactions. Comparative analysis also showed that KOH-SSAC performed approximately twice as well as sunflower stem biochar (SSB), indicating its potential for water treatment and pollutant removal applications. The study suggests the exploration of optimization strategies to further enhance the efficiency of KOH-SSAC in large-scale scenarios. These findings contribute to the development of improved materials for efficient water treatment and pollution control.

Production of hydrogen using plastic waste via Aspen Hysys simulation.

Plastic waste is being manufactured for the production of hydrogen. The amount of plastic waste collected annually is 189,953 tonnes from adjacent nations like Indonesia and Malaysia. Polyethylene (PE), Polypropylene (PP), Polyethylene Terephthalate (PET), Polyvinyl chloride (PVC), and Polystyrene (PS) are the five most prevalent forms of plastic found in most waste. Pyrolysis, water gas shift and steam reforming reaction, and pressure swing adsorption are the three main phases utilized and studied. In this research, authors examines the energy consumption on every stage. The plastic waste can be utilized to manufacture many hydrocarbons using the pyrolysis reaction. For this process, fast pyrolysis is being used at a temperature of 500 °C. A neutralization process is also needed due to the presence of Hydrochloric acid from the pyrolysis reaction, with the addition of sodium hydroxide. This is being carried to prevent any damage to the reactor during the process. Secondly, the steam reforming process continues after the water gas shift reaction has produced steam and carbon monoxide, followed by carbon dioxide and hydrogen formation. Lastly, pressure swing adsorption is designed to extract H2S and CO2 from the water gas shift and steam reforming reaction for greater purity of hydrogen. From the simulation study, it is observed that using various types of plastic waste procured (total input of 20,000 kg per hour of plastics) from, Brunei Darussalam, Malaysia and Indonesia, can produce about 340,000 tons of Hydrogen per year. Additionally, the annual profit of the Hydrogen production is estimated to be between $ 271,158,100 and $ 358,480,200. As per the economic analysis, it can be said that its a good to start hydrogen production plant in these regions.

Recent trends in the applications of sonochemical reactors as an advanced oxidation process for the remediation of microbial hazards associated with water and wastewater: A critical review.

Water is one of the major sources that spread human diseases through contamination with bacteria and other pathogenic microorganisms. This review focuses on microbial hazards as they are often present in water and wastewater and cause various human diseases. Among the currently used disinfection methods, sonochemical reactors (SCRs) that produce free radicals combined with advanced oxidation processes (AOPs) have received significant attention from the scientific community. Also, this review discussed various types of cavitation reactors, such as acoustic cavitation reactors (ACRs) utilizing ultrasonic energy (UE), which had been widely employed, involving AOPs for treating contaminated waters. Besides ACRs, hydrodynamic cavitation reactors (HCRs) also effectively destroy and deactivate microorganisms to varying degrees. Cavitation is the fundamental phenomenon responsible for initiating many sonochemical reactions in liquids. Bacterial degradation occurs mainly due to the thinning of microbial membranes, local warming, and the generation of free radicals due to cavitation. Over the years, although extensive investigations have focused on the antimicrobial effects of UE (ultrasonic energy), the primary mechanism underlying the cavitation effects in the disinfection process, inactivation of microbes, and chemical reactions involved are still poorly understood. Therefore, studies under different conditions often lead to inconsistent results. This review investigates and compares other mechanisms and performances from greener and environmentally friendly sonochemical techniques to the remediation of microbial hazards associated with water and wastewater. Finally, the energy aspects, challenges, and recommendations for future perspectives have been provided.

Hydrogeochemical characterization of groundwater and their associated potential health risks.

The present study assessed the human health risk exposure from the consumption of poor quality groundwater in the Lucknow area, a part of Central Ganga alluvial plain in India. Around 27 (n = 27) groundwater samples were collected from the study area. The analytical results of the samples (n = 27) collected indicate silicate and carbonate weathering is the dominant process along with cation exchange, sulfide oxidation, and reverse ion exchange. The type of groundwater is Ca2-Na-HCO3- type having all cations and anions within permissible WHO limits except for iron (Fe2+) and nitrate (NO3-). The high concentrations of Fe2 and NO3- in samples indicate the possibility of a non-geogenic point source for the same in an urban-influenced environment. The ionic concentration of dissolved constituents is used in weighted overlay analysis to generate the water quality index (WQI). WQI indicates that most urban areas (~ 98.52%) have fallen in the good to excellent category except few situated in the highly populated parts of Lucknow. The ionic concentrations of Fe2+ and NO3- have been further used to estimate human health risk by integrating regional urban population density data in Lucknow. The risk map shows alarming risks in the west-central part, where nearly ~ 35% of the total area is at moderate to high health risk.