Insight into mechanism of excellent visible-light photocatalytic activity of CuO/MgO/ZnO nanocomposite for advanced solution of environmental remediation.

Environmental remediation has sought several innovative ways for the treatment of wastewater and captivated researchers around the globe towards it. Through this study, we aim to proceed with the efforts to foster sustainable and feasible ways for the treatment of wastewater. In this work, we report the sol-gel synthesis of CuO/MgO/ZnO nanocomposite and carry out their systematic characterization with the help of state-of-the-art analytical techniques, such as FTIR, SEM, TEM, PL, XRD, Raman, and AFM. The SEM along with TEM and AFM provided useful insights into the surface morphology of the synthesized nanocomposite on both 2D and 3D surfaces and concluded the well-dispersed behavior of the nanocomposite. The characteristic functional groups responsible for carrying out the reaction of Cu-O, Mg-O, and Zn-O were identified by FTIR spectroscopy. On the other hand, crystal size, dislocation density, and microstrain of the nanocomposite were calculated by XRD. For optical studies, photoluminescence spectroscopy was performed. Once the characterization of the nanocomposite was done, they were eventually treated against the toxic organic dye, methylene blue. The calculated rate constant values of k for CuO was 2.48 × 10-3 min-1, for CuO/MgO (2.04 × 10-3 min-1), for CuO/ZnO (1.82 × 10-3 min-1) and CuO/MgO/ZnO was found to be 2.00 × 10-3 min-1. It has become increasingly evident that nanotechnology can be used in various facets of modern life, and its implementation in wastewater treatment has recently received much attention.

Empirical study of robust/developed PID control for nonlinear time-delayed dynamical system in discrete time domain.

In addition to the high nonlinearity of liquid dynamics inside a tank, a study was conducted to overcome loading/unloading liquid storage tank control problems. In this study, the author developed a nonlinear control system to conquer both nonlinearity and the significant time delay arising from using a pressure-difference-based level sensor. To this end, this study proposes the implementation of nonlinear state dependent (SDP-PID+) control using the SDP transfer function model as a class of nonlinear descriptions of dynamical systems. By incorporating additional robust (plus) compensators alongside the traditional P-, I-, and D-compensators, the robust SDP-PID controller utilizes the complete state feedback to create a time-varying state variable feedback (SDP-SVF) control law. This approach effectively mitigates the effects of the discrete-time SDP-TF. It introduces the pole placement tuning approach, which renders significant performance using the laboratory test rig TPS8.2.2.4, for automatic liquid level control.

Fabrication and characterization of binary composite MgO/CuO nanostructures for the efficient photocatalytic ability to eliminate organic contaminants: A detailed spectroscopic analysis.

Design and eco-friendly fabrication of affordable and sustainable materials for the treatment of wastewater consisting of dyes, antibiotics, and other harmful substances has always been demanding. Untreated wastewater being released from industries imposes serious threats to our ecosystem, seeking convenient approaches to diminish this alarming issue. Here in this work, we synthesized MgO/CuO nanocomposites from a plant extract of Ammi visnaga L. and then employed these nanocomposites for the treatment of organic dye (methylene blue). We characterized the synthesized nanocomposites by dynamic light scattering (DLS), zeta potential, scanning electron microscopy (SEM), atomic force microscopy (AFM), transmission electron microscopy (TEM), x-ray diffraction (XRD), and X-ray photoelectron microscopy (XPS). DLS presented information about the explicit size of nanocomposites, while the surface charge was examined by zeta potential. XRD provided detailed information about the crystalline behavior and the information regarding surface morphology and size was extracted by SEM, TEM, and AFM. Moreover, the fabricated nanocomposites were used as a photocatalyst in the treatment of methylene blue. The overall catalytic reaction took an hour to complete, and the value of percentage degradation was 98 %. Substantially, a detailed account of the kinetics, rate of reaction, and mechanism is also fostered in the context. The presented study can assist scientists and researchers around the world to reproduce the results and use them to apply them on a broader scale.

Synthesis of carbon nanotubes-chitosan nanocomposite and immunosensor fabrication for myoglobin detection: An acute myocardial infarction biomarker.

The use of single-walled carbon nanotubes (SWCNTs) in biomedical applications is limited due to their inability to disperse in aqueous solutions. In this study, dispersed -COOH functionalized CNTs with N-succinylated chitosan (CS), greatly increasing the water solubility of CNTs and forming a uniformly dispersed nanocomposite solution of CNTs@CS. Coupling reagent EDC/NHS was used as a linker with the -COOH groups present on the N-succinylated chitosan which significantly improved the affinity of the CNTs for biomolecules. Myoglobin (Mb) is a promising biomarker for the precise assessment of cardiovascular risk, type 2 diabetes, metabolic syndrome, hypertension and several types of cancer. A high level of Mb can be used to diagnose the mentioned pathogenic diseases. The CNTs@CS-FET demonstrates superior sensing performance for Mb antigen fortified in buffer, with a wide linear range of 1 to 4000 ng/mL. The detection limit of the developed Mb immunosensor was estimated to be 4.2 ng/mL. The novel CNTs@CS-FET immunosensor demonstrates remarkable capability in detecting Mb without being affected by interferences from nonspecific antigens. Mb spiked serum showed a recovery rate of 100.262 to 118.55 % indicating great promise for Mb detection in clinical samples. The experimental results confirmed that the CNTs@CS-FET immunosensor had excellent selectivity, reproducibility and storage stability.

Solving the fouling mechanisms in composite membranes for water purification: An advance approach.

With the increasing population worldwide more wastewater is created by human activities and discharged into the waterbodies. This is causing the contamination of aquatic bodies, thus disturbing the marine ecosystems. The rising population is also posing a challenge to meet the demands of fresh drinking water in the water-scarce regions of the world, where drinking water is made available to people by desalination process. The fouling of composite membranes remains a major challenge in water desalination. In this innovative study, we present a novel probabilistic approach to analyse and anticipate the predominant fouling mechanisms in the filtration process. Our establishment of a robust theoretical framework hinges upon the utilization of both the geometric law and the Hermia model, elucidating the concept of resistance in series (RIS). By manipulating the transmembrane pressure, we demonstrate effective management of permeate flux rate and overall product quality. Our investigations reveal a decrease in permeate flux in three distinct phases over time, with the final stage marked by a significant reduction due to the accumulation of a denser cake layer. Additionally, an increase in transmembrane pressure leads to a correlative rise in permeate flux, while also exerting negative effects such as membrane ruptures. Our study highlights the minimal immediate impact of the intermediate blocking mechanism (n = 1) on permeate flux, necessitating continuous monitoring for potential long-term effects. Additionally, we note a reduced membrane selectivity across all three fouling types (n = 0, n = 1.5, n = 2). Ultimately, our findings indicate that the membrane undergoes complete fouling with a probability of P = 0.9 in the presence of all three fouling mechanisms. This situation renders the membrane unable to produce water at its previous flow rate, resulting in a significant reduction in the desalination plant's productivity. I have demonstrated that higher pressure values notably correlate with increased permeate flux across all four membrane types. This correlation highlights the significant role of TMP in enhancing the production rate of purified water or desired substances through membrane filtration systems. Our innovative approach opens new perspectives for water desalination management and optimization, providing crucial insights into fouling mechanisms and proposing potential strategies to address associated challenges.

Label-free and ultrasensitive electrochemical transferrin detection biosensor based on a glassy carbon electrode and gold nanoparticles.

In this study, we developed a label-free and ultrasensitive electrochemical biosensor for the detection of transferrin (Tf), an important serum biomarker of atransferrinemia. The biosensor was fabricated by using glassy carbon electrode (GCE) and modified with gold nanoparticles (AuNPs) via electroless deposition. The electrochemical characteristics of the GCE-AuNPs biosensors were characterized using cyclic voltammetry and electrochemical impedance spectroscopy analysis. Differential pulse voltammetry was used for quantitative evaluation of the Tf-antigen by recording the increase in the anodic peak current of GCE-AuNPs biosensor. The GCE-AuNPs biosensor demonstrates superior sensing performance for Tf-antigen fortified in buffer, with a wide linear range of 0.1 to 5000 µg/mL and a limit of detection of 0.18 µg/mL. The studied GCE-AuNPs biosensor showed excellent sensitivity, selectivity, long-term storage stability and simple sensing steps without pretreatment of clinical samples. This GCE-AuNPs biosensor indicates great potential for developing a Tf detection platform, which would be helpful in the early diagnosis of atransferrinemia. The developed GCE-AuNPs biosensor holds great potential in biomedical research related to point of care for the early diagnosis and monitoring of diseases associated with aberrant serum transferrin levels. These findings suggest that the GCE-AuNPs biosensor has great potential for detecting other serum biomarkers.

Synthesis of activated biochar from sustainable bamboo resources: An environment-friendly and low-cost solution for palladium (II) removal from wastewater.

This article highlights the developing capabilities of low-cost activated biochar from bamboo waste used for Palladium (II) (Pd(II)) separation from man-made electroless plating solutions (ELP). From a novelty perspective, this article addresses the effect of coupled sonication and surfactant for the adsorptive elimination of Pd(II) on Bamboo stem activated carbon (BSAC) from ELP. The optimal activation procedure referred to an acid-to-bamboo ratio of 4:1 at sintering of 600-900 °C, which provided an activated carbon (AC) adsorbent with surface area analysis (BET) of 1014.36 m2/g, a value comparable to the commercially procured AC. Pd(II) adsorption characteristics in the solution of Pd with 50-500 mg/L concentration range were evaluated utilizing both agitation and sonication. Adsorption time, pH, dose, and adsorbate concentration were among the pertinent optimal batch adsorption parameters that were found. When utilizing ELP solutions without surfactant, the proposed adsorbent for agitation-assisted adsorption had a simultaneous improvement in metal intake of 6.68-43.2 mg/g and removal efficiency of 72.96-54.5% (cTAB). For cTAB-containing solutions, sonication and agitation-assisted adsorption were outperformed in terms of removal efficiency of 80.32-60.16% and metal uptake of 6.69-50.13 mg/g. Equilibrium, kinetic, and thermodynamic models with good fitting to the reported Pd(II) adsorption properties have been developed.

Serum CRP biomarker detection by using carbon nanotube field-effect transistor (CNT-FET) immunosensor.

C-reactive protein (CRP) is produced by the liver in response to systemic inflammation caused by bacterial infection, trauma and internal organ failures. CRP serves as a potential biomarker in the precise diagnosis of cardiovascular risk, type-2 diabetes, metabolic syndrome, hypertension and various types of cancers. The pathogenic conditions indicated above are diagnosed by an elevated CRP level in the serum. In this study, we successfully fabricated a highly sensitive and selective carbon nanotube field-effect transistor (CNT-FET) immunosensor for the detection of CRP. The CNTs were deposited on the Si/SiO2 surface, between source-drain electrodes, afterwards modified with well-known linker PBASE and then anti-CRP was immobilized. This anti-CRP functionalized CNT-FET immunosensor exhibits a wide dynamic detection range (0.01-1000 µg/mL) CRP detection, rapid response time (2-3 min) and low variation (<3 %) which can be delivered as a low-cost and rapid clinical detection technology for the early diagnosis of coronary heart disease (CHD). For the clinical applications, our sensor was tested using CRP fortified serum samples and sensing performance was validated using enzyme-linked immune-sorbent assay (ELISA). This CNT-FET immunosensor will be helpful in taking over the complex laboratory-based expensive traditional CRP diagnostic procedures practiced in the hospitals.

Plant Extract-Based Fabrication of Silver Nanoparticles and Their Effective Role in Antibacterial, Anticancer, and Water Treatment Applications.

Ammi visnaga is a biennial or annual herbaceous plant belonging to the family Apiaceae. For the first time, silver nanoparticles were synthesized using an extract of this plant. Biofilms are a rich source of many pathogenic organisms and, thus, can be the genesis of various disease outbreaks. In addition, the treatment of cancer is still a critical drawback for mankind. The primary purpose of this research work was to comparatively analyze antibiofilms against Staphylococcus aureus, photocatalytic activity against Eosin Y, and in vitro anticancer activity against the HeLa cell line of silver nanoparticles and Ammi visnaga plant extract. The systematic characterization of synthesized nanoparticles was carried out using UV-Visible spectroscopy (UV-Vis), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), atomic force microscopy (AFM), dynamic light scattering (DLS), zeta potential, and X-ray diffraction microscopy (XRD). The initial characterization was performed with UV-Vis spectroscopy, where a peak appeared at 435 nm, which indicated the SPR band of the silver nanoparticles. AFM and SEM were performed to determine the morphology and shape of the nanoparticles, while EDX confirmed the presence of Ag in the spectra. The crystalline character of the silver nanoparticles was concluded with XRD. The synthesized nanoparticles were then subjected to biological activities. The antibacterial activity was evaluated by determining the inhibition of the initial biofilm formation with Staphylococcus aureus using a crystal violet assay. The response of the AgNPs against cellular growth and biofilm formation was found to be dose dependent. Green-synthesized nanoparticles showed 99% inhibition against biofilm and bacteria, performed excellent anticancer assay with an IC50 concentration of 17.1 ± 0.6 µg/mL and 100% inhibition, and photodegradation of the toxic organic dye Eosin Y up to 50%. Moreover, the effect of the pH and dosage of the photocatalyst was also measured to optimize the reaction conditions and maximum photocatalytic potential. Therefore, synthesized silver nanoparticles can be used in the treatment of wastewater contaminated with toxic dyes, pathogenic biofilms, and the treatment of cancer cell lines.

Modeling Behavior and Vaccine Hesitancy Using Twitter-Derived US Population Sentiment during the COVID-19 Pandemic to Predict Daily Vaccination Inoculations.

The sentiment analysis of social media for predicting behavior during a pandemic is seminal in nature. As an applied contribution, we present sentiment-based regression models for predicting the United States COVID-19 first dose, second dose, and booster daily inoculations from 1 June 2021 to 31 March 2022. The models merge independent variables representing fear of the virus and vaccine hesitancy. Large correlations exceeding 77% and 84% for the first-dose and booster-dose models inspire confidence in the merger of the independent variables. Death count as a traditional measure of fear is a lagging indicator of inoculations, while Twitter-positive and -negative tweets are strong predictors of inoculations. Thus, the use of sentiment analysis for predicting inoculations is strongly supported with administrative events being catalysts for tweets. Non-inclusion in the second-dose regression model of data occurring before the 1 June 2021 timeframe appear to limit the second-dose model results-only achieving a moderate correlation exceeding 53%. Limiting tweet collection to geolocated tweets does not encompass the entire US Twitter population. Nonetheless, results from Kaiser Family Foundation (KFF) surveys appear to generally support the regression factors common to the first-dose and booster-dose regression models and their results.

Adaptive Finite Element Modeling of Linear Elastic Fatigue Crack Growth.

This paper proposed an efficient two-dimensional fatigue crack growth simulation program for linear elastic materials using an incremental crack growth procedure. The Visual Fortran programming language was used to develop the finite element code. The adaptive finite element mesh was generated using the advancing front method. Stress analysis for each increment was carried out using the adaptive mesh finite element technique. The equivalent stress intensity factor is the most essential parameter that should be accurately estimated for the mixed-mode loading condition which was used as the onset criterion for the crack growth. The node splitting and relaxation method advances the crack once the failure mechanism and crack direction have been determined. The displacement extrapolation technique (DET) was used to calculate stress intensity factors (SIFs) at each crack extension increment. Then, these SIFs were analyzed using the maximum circumferential stress theory (MCST) to predict the crack propagation trajectory and the fatigue life cycles using the Paris' law model. Finally, the performance and capability of the developed program are shown in the application examples.