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Due to the release of hazardous heavy metals from various industries, water pollution has become one of the biggest challenges for environmental scientists today. Mercury Hg(II) is regarded as one of the most toxic heavy metals due to its ability to cause cancer and other health issues. In this study, a tailor-made modern eco-friendly molecularly imprinted polymer (MIP)/nanoporous carbon (NC) nanocomposite was synthesized and examined for the uptake of Hg(II) using an aqueous solution. The fabrication of the MIP/NC nanocomposite occurred via bulk polymerization involving the complexation of the template, followed by polymerization and, finally, template removal. Thus, the formed nanocomposite underwent characterizations that included morphological, thermal degradation, functional, and surface area analyses. The MIP/NC nanocomposite, with a high specific surface area of 884.9 m2/g, was evaluated for its efficacy towards the adsorptive elimination of Hg(II) against the pH solution changes, the dosage of adsorbent, initial concentration, and interaction time. The analysis showed that a maximum Hg(II) adsorption effectiveness of 116 mg/g was attained at pH 4, while the Freundlich model fitted the equilibrium sorption result and was aligned with pseudo-second-order kinetics. Likewise, thermodynamic parameters like enthalpy, entropy, and Gibbs free energy indicated that the adsorption was consistent with spontaneous, favorable, and endothermic reactions. Furthermore, the adsorption efficiency of MIP/NC was also evaluated against a real sample of condensate from the oil and gas industry, showing an 87.4% recovery of Hg(II). Finally, the synthesized MIP/NC showed promise as a selective adsorbent of Hg(II) in polluted environments, suggesting that a variety of combined absorbents of different precursors is recommended to evaluate heavy metal and pharmaceutical removals.

In this study, we tested the biosorption capacity of trimethyl chitosan (TMC)-ZnO nanocomposite (NC) for the adsorptive removal of Escherichia coli (E. coli) in aqueous suspension. For the formation of ZnO NPs, we followed the green synthesis route involving Terminalia mantaly (TM) aqueous leaf extract as a reducing agent, and the formed ZnO particles were surface-coated with TMC biopolymer. On testing of the physicochemical characteristics, the TM@ZnO/TMC (NC) hydrogel showed a random spherical morphology with an average size of 31.8 ± 2.6 nm and a crystal size of 28.0 ± 7.7 nm. The zeta potential of the composite was measured to be 23.5 mV with a BET surface area of 3.01 m2 g−1. The spectral profiles of TM@ZnO/TMC NC hydrogel on interaction with Escherichia coli (E. coli) revealed some conformational changes to the functional groups assigned to the stretching vibrations of N-H, C-O-C, C-O ring, and C=O bonds. The adsorption kinetics of TM@ZnO/TMC NC hydrogel revealed the pseudo-second-order as the best fit mechanism for the E. coli biosorption. The surface homogeneity and monolayer adsorption of the TM@ZnO/TMC NC hydrogel reflects majorly the entire adsorption mechanism, observed to display the highest correlation for Jovanovic, Redlich–Peterson, and Langmuir’s isotherm models. Further, with the use of TM@ZnO/TMC NC hydrogel, we measured the highest adsorption capacity of E. coli to be 4.90 × 10 mg g−1, where an in-depth mechanistic pathway was proposed by making use of the FTIR analysis.

Industrial areas play an important role in the urban ecosystem. Industrial site environmental quality is linked to human health. Soil samples from two different cities in India, Jamshedpur and Amravati, were collected and analyzed to assess the sources of polycyclic aromatic hydrocarbons (PAHs) in industrial areas and their potential health risks. The total concentration of 16 PAHs in JSR (Jamshedpur) varied from 1662.90 to 10,879.20 ng/g, whereas the concentration ranged from 1456.22 to 5403.45 ng/g in the soil of AMT (Amravati). The PAHs in the samples were dominated by four-ring PAHs, followed by five-ring PAHs, and a small percentage of two-ring PAHs. The ILCR (incremental lifetime cancer risk) of the soil of Amravati was lower compared to that of Jamshedpur. The risk due to PAH exposure for children and adults was reported to be in the order of ingestion > dermal contact > inhalation while for adolescents it was dermal contact > ingestion > inhalation in Jamshedpur. In contrast, in the soil of Amravati, the PAH exposure path risk for children and adolescents were the same and showed the following order: dermal contact > ingestion > inhalation while for the adulthood age group, the order was ingestion > dermal contact > inhalation. The diagnostic ratio approach was used to assess the sources of PAHs in various environmental media. The PAH sources were mainly dominated by coal and petroleum/oil combustion. As both the study areas belong to industrial sites, the significant sources were industrial emissions, followed by traffic emissions, coal combustion for domestic livelihood, as well as due to the geographical location of the sampling sites. The results of this investigation provide novel information for contamination evaluation and human health risk assessment in PAH-contaminated sites in India.

The present study deals with the adsorptive removal of Escherichia coli (E. coli) by making use of chitosan-silica/calcium carbonate (CS-SiO2/CaCO3) nanocomposites (NCs) where it was synthesized using the waste eggshells and rice husks occurred by natural sources. The bioadsorbent CS-SiO2/CaCO3 NCs were synthesized by incorporating eggshell-CaCO3 nanoparticles (NPs) and rice husk-SiO2 NPs in chitosan NPs solution. The adsorbents were characterized using HRTEM, BET, DLS, and TGA. The characterization of NCs revealed the formation of adsorbents in the range of 10–50 nm and some structural changes to the spectra of adsorbents before and after the adsorption of E. coli was revealed by the FTIR analysis. Moreover, the adsorption efficiency of E. coli over the adsorbents after 35 min of incubation was about 80% for CS-SiO2/CaCO3 NCs. Further, the kinetics of adsorption studies were observed to be well-fitted to the Langmuir isotherm model with an adsorption capacity of 3.18 × 101 (CFU E. coil per gram of CS-SiO2/CaCO3). From the analysis, the synthesized bioadsorbent demonstrated the potential for ameliorating the inherent risk of pathogens in water.

Ventilation mode is one of the most crucial ventilator settings, selected and set by knowledgeable critical care therapists in a critical care unit. The application of a particular ventilation mode must be patient-specific and patient-interactive. The main aim of this study is to provide a detailed outline regarding ventilation mode settings and determine the best machine learning method to create a deployable model for the appropriate selection of ventilation mode on a per breath basis. Per-breath patient data is utilized, preprocessed and finally a data frame is created consisting of five feature columns (inspiratory and expiratory tidal volume, minimum pressure, positive end-expiratory pressure, and previous positive end-expiratory pressure) and one output column (output column consisted of modes to be predicted). The data frame has been split into training and testing datasets with a test size of 30%. Six machine learning algorithms were trained and compared for performance, based on the accuracy, F1 score, sensitivity, and precision. The output shows that the Random-Forest Algorithm was the most precise and accurate in predicting all ventilation modes correctly, out of the all the machine learning algorithms trained. Thus, the Random-Forest machine learning technique can be utilized for predicting optimal ventilation mode setting, if it is properly trained with the help of the most relevant data. Aside from ventilation mode, control parameter settings, alarm settings and other settings may also be adjusted for the mechanical ventilation process utilizing appropriate machine learning, particularly deep learning approaches.

Curbing tuberculosis (TB) requires a combination of good strategies, including a proper prevention measure, diagnosis, and treatment. This study proposes an improvised tuberculosis diagnosis based on an amperometry approach for the sensitive detection of MPT64 antigen in clinical samples. An MPT64 aptamer specific to the target antigen was covalently attached to the carboxyphenyl diazonium-functionalized carbon electrode via carbodiimide chemistry. The electrochemical detection assay was adapted from a sandwich assay format to trap the antigen between the immobilized aptamer and horseradish peroxidase (HRP) tagged polyclonal anti-MPT64 antibody. The amperometric current was measured from the catalytic reaction response between HRP, hydrogen peroxide, and hydroquinone, which is used as an electron mediator. From the analysis, the detection limit in the measurement buffer was 1.11 ng mL−1. Additionally, the developed aptasensor exhibited a linear relationship between the current signal and the MPT64 antigen-spiked serum concentration ranging from 10 to 150 ng mL−1 with a 1.38 ng mL−1 detection limit. Finally, an evaluation using the clinical sputum samples from both TB (+) and TB (−) individuals revealed a sensitivity and specificity of 88% and 100%, respectively. Based on the analysis, the developed aptasensor was found to be simple in its fabrication, sensitive, and allowed for the efficient detection and diagnosis of TB in sputum samples.

Flower-like microstructured trigonal tellurium (t-Te) was prepared by a simple solution-based strategy with the reaction of 1,2,3,4,5-pentamethylcyclopentadiene (HCp*) and TeCl4 in tetrahydrofuran (THF) under reflux condition. Powder X-ray diffraction (XRD) and electron microscopy techniques confirmed the phase purity and morphology of the synthesized sample. X-ray photoelectron spectroscopy (XPS) analysis provides evidence for forming elemental Te, viz, Te (0). Also, XPS analysis indicates the presence of a higher oxidation state, i.e., Te(IV), which may be due to the formation of an ultrathin layer of TeO2 on the surface of the elemental t-Te microstructure. According to the synthetic strategy, higher oxidation is due to the surface oxidation of Te (0) to Te(IV) while washing the sample. Additionally, Fourier-transform infrared (FTIR) technique further supports the presence of Te–O bonds on the surface of t-Te. The semiconducting behavior of the prepared elemental t-Te microstructure was confirmed by obtaining a positive Hall coefficient (RH) value of 4.98 × 103 cm3 C−1. In addition, the resistivity (conductivity) value, i.e., 1.05 × 102 Ω cm (9.52 × 10−4 Ω−1 cm−1) is calculated using Hall-effect measurement.

Due to the complexity of high temperature and cutting tool wear, most machined components are still facing problems in terms of harder functional fillers that reinforce aluminium matrix composites. Conversely, abrasive water jet machining (AWJM) incredibly useful for the cutting of anisotropic and non-homogeneous metal matrix composites. In this research article, silicon carbide (SiC) particulates were utilized as reinforcement in the AA6026 matrix material (AA6026/SiC) and machined using AWJM under different process parameters namely SiC loading, traverse speed and stand-off distance. Two different compositions of SiC (4, and 8 wt%) were considered to fabricate AA6026 composites using the stir casting. In addition, outputs have been examined, e.g., surface roughness, material removal rate, and kerf angle. An optical microscope, scanning electron microscope, Brinell hardness tester and universal testing machine have been used to characterize the matrix material AA6026 and its composites. Microstructural analysis revealed that the inclusion of SiC particulates in AA6026 affects the very fine grain size of the composite. Furthermore, the 8 wt% composite exhibits the evolution of the Al-Si eutectic phase during solidification. Processing of these composites was performed using the L _27 orthogonal geometry, successfully improving the parameters of the abrasive water jet process. The output response shows that reducing the SiC load improves the surface roughness under the key parameters of traverse speed and stand-off distance. However, increasing the SiC loading increases the material removal rate and kerf angle under the key parameters, namely traverse speed, and stand-off distance.