Bacteriogenic synthesis of morphologically diverse silver nanoparticles and their assessment for methyl orange dye removal and antimicrobial activity.

Nanotechnology and nanoparticles have gained massive attention in the scientific community in recent years due to their valuable properties. Among various AgNPs synthesis methods, microbial approaches offer distinct advantages in terms of cost-effectiveness, biocompatibility, and eco-friendliness. In the present research work, investigators have synthesized three different types of silver nanoparticles (AgNPs), namely AgNPs-K, AgNPs-M, and AgNPs-E, by using Klebsiella pneumoniae (MBC34), Micrococcus luteus (MBC23), and Enterobacter aerogenes (MBX6), respectively. The morphological, chemical, and elemental features of the synthesized AgNPs were analyzed by using UV-Vis spectroscopy (UV-Vis), Fourier transform-infrared spectroscopy (FTIR), X-ray diffraction (XRD), field emission scanning electron microscope (FESEM) and energy-dispersive spectroscopy (EDX). UV-Vis absorbance peaks were obtained at 475, 428, and 503 nm for AgNPs-K, AgNPs-M, and AgNPs-E, respectively. The XRD analysis confirmed the crystalline nature of the synthesized AgNPs, having peaks at 26.2°, 32.1°, and 47.2°. At the same time, the FTIR showed bands at 599, 963, 1,693, 2,299, 2,891, and 3,780 cm-1 for all the types of AgNPs indicating the presence of bacterial biomolecules with the developed AgNPs. The size and morphology of the AgNPs varied from 10 nm to several microns and exhibited spherical to porous sheets-like structures. The percentage of Ag varied from 37.8% (wt.%) to 61.6%, i.e., highest in AgNPs-K and lowest in AgNPs-M. Furthermore, the synthesized AgNPs exhibited potential for environmental remediation, with AgNPs-M exhibiting the highest removal efficiency (19.24% at 120 min) for methyl orange dye in simulated wastewater. Further, all three types of AgNPs were evaluated for the removal of methyl orange dye from the simulated wastewater, where the highest dye removal percentage was 19.24% at 120 min by AgNPs-M. Antibacterial potential of the synthesized AgNPs assessment against both Gram-positive (GPB) Bacillus subtilis (MBC23), B. cereus (MBC24), and Gram-negative bacteria Enterococcus faecalis (MBP13) revealed promising results, with AgNPs-M, exhibiting the largest zone of inhibition (12 mm) against GPB B. megaterium. Such investigation exhibits the potential of the bacteria for the synthesis of AgNPs with diverse morphology and potential applications in environmental remediation and antibacterial therapy-based synthesis of AgNPs.

Harnessing the power of nutritional antioxidants against adrenal hormone imbalance-associated oxidative stress.

Oxidative stress, resulting from dysregulation in the secretion of adrenal hormones, represents a major concern in human health. The present review comprehensively examines various categories of endocrine dysregulation within the adrenal glands, encompassing glucocorticoids, mineralocorticoids, and androgens. Additionally, a comprehensive account of adrenal hormone disorders, including adrenal insufficiency, Cushing's syndrome, and adrenal tumors, is presented, with particular emphasis on their intricate association with oxidative stress. The review also delves into an examination of various nutritional antioxidants, namely vitamin C, vitamin E, carotenoids, selenium, zinc, polyphenols, coenzyme Q10, and probiotics, and elucidates their role in mitigating the adverse effects of oxidative stress arising from imbalances in adrenal hormone levels. In conclusion, harnessing the power of nutritional antioxidants has the potential to help with oxidative stress caused by an imbalance in adrenal hormones. This could lead to new research and therapeutic interventions.

Ultrasensitive detection of food colorant sunset yellow using nickel nanoparticles promoted lettuce-like spinel Co3O4 anchored GO nanosheets.

Synthetic food colorants are extensively used across the globe regardless of the fact that they induce deleterious side effects when used in higher amounts. In this work, a novel electrochemical sensor based on nickel nanoparticles doped lettuce-like Co3O4 anchored graphene oxide (GO) nanosheets was developed for effective detection of sulfonated azo dye sunset yellow widely used as a food colorant. Hydrothermal synthesis was adopted for the preparation of lettuce-like spinel Co3O4 nanoparticles and Ni-Co3O4 NPs/GO nanocomposite was prepared using ecofriendly and economical sonochemical method. The prepared ternary nanocomposite meticulously fabricated on a screen-printed carbon electrode exhibited remarkable electrocatalytic activity towards sunset yellow determination. This is apparent from the resultant well-defined and intense redox peak currents of Ni-Co3O4 NPs/GO nanocomposite modified electrode at very low potentials. The developed sunset yellow sensor exhibited a high sensitivity of 4.16 µA µM-1 cm-2 and a nanomolar detection limit of 0.9 nM in the linear range 0.125-108.5 µM. Furthermore, experiments were conducted to affirm excellent stability, reproducibility, repeatability, and selectivity of proposed sensor. The practicality of sunset yellow determination using the developed sensor was analyzed in different varieties of food samples including jelly, soft drink, ice cream, and candy resulting in recovery in the range of 96.16%-102.56%.

Bi-functional renewable biopolymer wrapped CNFs/Ag doped spinel cobalt oxide as a sensitive platform for highly toxic nitroaromatic compound detection and degradation.

Nanomolar-level detection of priority toxic pollutant 4-nitrophenol (4-NP) in environment using a novel ternary nanocomposite based electrochemical sensor and its photocatalytic degradation is reported in this paper. A non-toxic and renewable natural biopolymer, chitosan wrapped carbon nanofibers was embedded with Ag doped spinel Co3O4 to prepare the bi-functional ternary nanocomposite. Economical and ecofriendly sonochemical method was employed in preparation of this porous nanocomposite. We used one-pot aqueous solution approach to synthesize Ag-Co3O4 nanoflowers and ultrasound-assisted method was utilized to prepare CS-CNFs. Morphological and structural properties of synthesized materials were analyzed using different characterization techniques. Electrochemical investigations using cyclic voltammetry and differential pulse voltammetry carried out with prepared ternary nanocomposite modified carbon electrode revealed its outstanding electrocatalytic activity in 4-NP quantification. The developed 4-NP sensor showcased excellent sensitivity of 55.98 µAµM-1cm-2 and nanomolar detection limit of 0.4 nM. Moreover, reproducibility, repeatability, stability, and selectivity were evaluated to confirm reliability of developed sensor. Further, real sample analyses were conducted using domestic sewage, underground water, and tomato to affirm the practical feasibility of 4-NP detection using the proposed sensor.

Electrocatalytic Platform Based on Silver-Doped Sugar Apple-like Cupric Oxide Embedded Functionalized Carbon Nanotubes for Nanomolar Detection of Acetaminophen (APAP).

Economical and nanomolar-level determination of the analgesic drug, acetaminophen (APAP), is reported in this work. A novel ternary nanocomposite based on silver-doped sugar apple-like cupric oxide (CuO)-decorated amine-functionalized multi-walled carbon nanotubes (fCNTs) was sonochemically prepared. CuO nanoparticles were synthesized based on the ascorbic acid-mediated low-temperature method, and sidewall functionalization of CNTs was carried out. Important characterizations of the synthesized materials were analyzed using SEM, TEM, HAADF-STEM, elemental mapping, EDX, lattice fringes, SAED pattern, XRD, EIS, UV-Vis, micro-Raman spectroscopy, and FTIR. It was noted the sonochemically prepared nanocomposite diligently fabricated on screen-printed carbon electrode showcased outstanding electrocatalytic performance towards APAP determination. The APAP sensor exhibited ultra-low limit of detection of 4 nM, wide linear concentration ranges of 0.02-3.77 and 3.77-90.02 µM, and high sensitivity of 30.45 µA µM-1 cm-2. Moreover, further evaluation of the sensor's performance based on electrochemical experiments showcased outstanding selectivity, stability, reproducibility, and repeatability. Further, excellent practical feasibility of the proposed APAP sensor was affirmed with excellent recovery larger than 96.86% and a maximum RSD of 3.67%.

Ultrasensitive detection of cytotoxic food preservative tert-butylhydroquinone using 3D cupric oxide nanoflowers embedded functionalized carbon nanotubes.

Accurate detection of cytotoxic food preservative tert-butylhydroquinone (TBHQ) has significant importance in maintaining food quality and safety. TBHQ is a chronic hazard to aquatic life and its use in applications involving direct human exposure and frequent release to environment makes its quantification critical to maintain safety. Hence, we report development of a sensitive electrochemical sensor for TBHQ determination at nanomolar level in commonly used edible oils and water sample. Novel cupric oxide (CuO) decorated amine functionalized carbon nanotubes (NH2-CNTs) were prepared for development of TBHQ sensor. 3D CuO nanoflowers and NH2-CNTs were synthesized using hydrothermal and ultrasound-assisted method respectively. Techniques such as SEM, elemental mapping, XRD, FTIR, micro Raman, XPS, EIS, and UV-Visible spectroscopy were taken to affirm significant characterizations of synthesized materials. We have observed outstanding electrocatalytic activity towards TBHQ detection using the sonochemically prepared nanocomposite modified screen printed carbon electrode (SPCE). The proposed sensor exhibited ultra-low detection limit at 3 nM and exceptional sensitivity at 37.7 µA µM-1 cm-2. Furthermore, TBHQ sensor showcased outstanding anti-interference, stability, reproducibility, and repeatability. The practical feasibility of TBHQ detection was validated using real sample analysis resulting in excellent recovery in the range 95.90-104.87% and a maximum RSD of mere 2.71%.

A novel soft sensor based warning system for hazardous ground-level ozone using advanced damped least squares neural network.

Estimation of hazardous air pollutants in the urban environment for maintaining public safety is a significant concern to mankind. In this paper, we have developed an efficient air quality warning system based on a low-cost and robust ground-level ozone soft sensor. The soft sensor was developed based on a novel technique of damped least squares neural network (DLSNN) with greedy backward elimination (GBE) for the estimation of hazardous ground-level ozone. Only three meteorological factors were used as input variables in the estimation of ground-level ozone and we have used weighted k-nearest neighbors (WkNN) classifier with fast response for development of air quality warning system. We have chosen the urban areas of Taiwan for this study and have analyzed seasonal variations in the ground-level ozone concentration of various cities in Taiwan as part of this work. Moreover, descriptive statistics and linear dependence of ozone concentration based on Spearman correlation coefficient, Kendall's tau coefficient, and Pearson coefficient are calculated. The proposed DLSNN/GBE method exhibited excellent performance resulting in very low mean square error (MSE), mean absolute error (MAE), and high coefficient of determination (R2) compared to other traditional approaches in ozone concentration estimation. We have achieved a good fit in the determination of ozone concentration from meteorological features of atmosphere. Moreover, the excellent performance of proposed urban air quality warning system was evident from the good F1-score value of 0.952 achieved by the WkNN classifier.

A Real-Time Wearable Assist System for Upper Extremity Throwing Action Based on Accelerometers.

This paper focuses on the development of a real-time wearable assist system for upper extremity throwing action based on the accelerometers of inertial measurement unit (IMU) sensors. This real-time assist system can be utilized to the learning, rectification, and rehabilitation for the upper extremity throwing action of players in the field of baseball, where incorrect throwing phases are recognized by a delicate action analysis. The throwing action includes not only the posture characteristics of each phase, but also the transition of continuous posture movements, which is more complex when compared to general action recognition with no continuous phase change. In this work, we have considered six serial phases including wind-up, stride, arm cocking, arm acceleration, arm deceleration, and follow-through in the throwing action recognition process. The continuous movement of each phase of the throwing action is represented by a one-dimensional data sequence after the three-axial acceleration signals are processed by efficient noise filtering based on Kalman filter followed by conversion processes such as leveling and labeling techniques. The longest common subsequence (LCS) method is then used to determine the six serial phases of the throwing action by verifying the sequence data with a sample sequence. We have incorporated various intelligent action recognition functions including automatic recognition for getting ready status, starting movement, handle interrupt situation, and detailed posture transition in the proposed assist system. Moreover, a liquid crystal display (LCD) panel and mobile interface are incorporated into the developed assist system to make it more user-friendly. The real-time system provides precise comments to assist players to attain improved throwing action by analyzing their posture during throwing action. Various experiments were conducted to analyze the efficiency and practicality of the developed assist system as part of this work. We have obtained an average percentage accuracy of 95.14%, 91.42%, and 95.14%, respectively, for all the three users considered in this study. We were able to successfully recognize the throwing action with good precision and the high percentage accuracy exhibited by the proposed assist system indicates its excellent performance.

Ultrasound-assisted synthesis of 3D flower-like zinc oxide decorated fMWCNTs for sensitive detection of toxic environmental pollutant 4-nitrophenol.

Sonochemical synthesis of functionalized multi-walled carbon nanotubes (fMWCNTs) embellished 3D flower-like zinc oxide (ZnO) nanocomposite based novel electrochemical sensor for the detection of toxic environmental pollutant 4-nitrophenol (4-NP) is detailed in this paper. We have used laser-assisted synthesis technique in the development of 3D flower-like ZnO nanoparticles (NPs) and ultrasonication method was employed in preparation of ZnO NPs@fMWCNTs nanocomposite using a high-intensity ultrasonic bath DC200H with power of 200 W/cm2 and 40 KHz frequency. The nanocomposite was meticulously fabricated on screen printed carbon electrode (SPCE) to carry out various electrochemical analysis. Different characterizations such as Raman spectroscopy, Fourier transform infrared (FT-IR) spectroscopy, UV visible spectroscopy (UV-Vis), X-ray diffraction (XRD), scanning electron microscopy (SEM), and high-resolution transmission electron microscopy (HRTEM) of the materials used in this work were taken. Cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques are used in electrochemical investigations. We have observed well-defined oxidation and reduction peak currents representing electrochemical mechanism of 4-NP at very low potentials for ZnO NPs@fMWCNTs/SPCE. Furthermore, we were able to achieve efficient electrochemical determination of 4-NP using the developed sensor with a high sensitivity of 11.44 µA µM-1 cm-2 and very low detection limit (LOD) of 0.013 µM in a broad linear range of 0.06-100 µM. All the significant features of a good sensor including anti-interference, good stability, excellent repeatability, and reproducibility were exhibited by the sensor. Moreover, we have tested practical feasibility of sensor by carrying out real sample analysis on different water samples.

Ecofriendly synthesized reduced graphene oxide embellished marsh marigold-like zinc oxide nanocomposite based on ultrasonication technique for the sensitive detection of environmental pollutant hydroquinone.

A novel electrochemical sensor using reduced graphene oxide (RGO) decorated marsh marigold-like zinc oxide (ZnO) nanocomposite for the detection of hydroquinone (HQ) is detailed in this paper. We have adopted an ecofriendly preparation procedure for the synthesis of RGO and the synthesis of marsh marigold-like ZnO is carried out using aqueous solution method. The RGO/ZnO nanocomposite is prepared based on ultrasonication technique using a high-intensity ultrasonic bath DC200H (200 W/cm2, 40 kHz) and is followed by its precise fabrication on glassy carbon electrode (GCE). Characterizations including X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared (FT-IR) spectroscopy, Raman spectroscopy, and UV visible spectroscopy of ZnO nanoparticles, RGO, and RGO/ZnO nanocomposite are analyzed in this work. Different electrochemical studies were performed in this work to investigate performance of the proposed electrochemical sensor and cyclic voltammetry (CV) and differential pulse voltammetry (DPV) techniques are used to achieve this. The oxidation and reduction peak currents of RGO/ZnO modified GCE exhibited sharp peaks at very low potential of 0.13 V and 0.06 V respectively. We have obtained a high sensitivity of 8.08 µA µM-1 cm-2, ultra-low limit of detection (LOD) value of 0.01 µM, and a broad linear range of 0.1-92 µM for the proposed sensor. Moreover, the fabricated sensor exhibited excellent selectivity, good reproducibility, stability, and repeatability revealing the high efficiency of the proposed sensor. Furthermore, experiments were conducted to examine the practical feasibility of the developed sensor. The electrochemical studies conducted as part of the work shows that RGO/ZnO nanocomposite is an apt material for the highly sensitive and efficient detection of HQ.

Air quality warning system based on a localized PM2.5 soft sensor using a novel approach of Bayesian regularized neural network via forward feature selection.

It is highly significant to develop efficient soft sensors to estimate the concentration of hazardous pollutants in a region to maintain environmental safety. In this paper, an air quality warning system based on a robust PM2.5 soft sensor and support vector machine (SVM) classifier is reported. The soft sensor for the estimation of PM2.5 concentration is proposed using a novel approach of Bayesian regularized neural network (BRNN) via forward feature selection (FFS). Zuoying district of Taiwan is selected as the region of study for implementation of the estimation system because of the high pollution in the region. Descriptive statistics of various pollutants in Zuoying district is computed as part of the study. Moreover, seasonal variation of particulate matter (PM) concentration is analyzed to evaluate the impact of various seasons on the increased levels of PM in the region. To investigate the linear dependence of concentration of different pollutants to the concentration of PM2.5, Pearson correlation coefficient, Kendall's tau coefficient, and Spearman coefficient are computed. To achieve high performance for the PM2.5 estimation, selection of appropriate forward features from the input variables is carried out using FFS technique and Bayesian regularization is incorporated to the neural network system to avoid the overfitting problem. The comparative evaluation of performance of BRNN/FFS estimation system with various other methods shows that our proposed estimation system has the lowest mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE). Moreover, the coefficient of determination (R-squared) is around 0.95 for the proposed estimation method, which denotes a good fit. Evaluation of the SVM classifier showed good performance indicating that the proposed air quality warning system is efficient.

Mobile monitoring and embedded control system for factory environment.

This paper proposes a real-time method to carry out the monitoring of factory zone temperatures, humidity and air quality using smart phones. At the same time, the system detects possible flames, and analyzes and monitors electrical load. The monitoring also includes detecting the vibrations of operating machinery in the factory area. The research proposes using ZigBee and Wi-Fi protocol intelligent monitoring system integration within the entire plant framework. The sensors on the factory site deliver messages and real-time sensing data to an integrated embedded systems via the ZigBee protocol. The integrated embedded system is built by the open-source 32-bit ARM (Advanced RISC Machine) core Arduino Due module, where the network control codes are built in for the ARM chipset integrated controller. The intelligent integrated controller is able to instantly provide numerical analysis results according to the received data from the ZigBee sensors. The Android APP and web-based platform are used to show measurement results. The built-up system will transfer these results to a specified cloud device using the TCP/IP protocol. Finally, the Fast Fourier Transform (FFT) approach is used to analyze the power loads in the factory zones. Moreover, Near Field Communication (NFC) technology is used to carry out the actual electricity load experiments using smart phones.

Fuzzy virtual reference model sensorless tracking control for linear induction motors.

This paper introduces a fuzzy virtual reference model (FVRM) synthesis method for linear induction motor (LIM) speed sensorless tracking control. First, we represent the LIM as a Takagi-Sugeno fuzzy model. Second, we estimate the immeasurable mover speed and secondary flux by a fuzzy observer. Third, to convert the speed tracking control into a stabilization problem, we define the internal desired states for state tracking via an FVRM. Finally, by solving a set of linear matrix inequalities (LMIs), we obtain the observer gains and the control gains where exponential convergence is guaranteed. The contributions of the approach in this paper are threefold: 1) simplified approach--speed tracking problem converted into stabilization problem; 2) omit need of actual reference model--FVRM generates internal desired states; and 3) unification of controller and observer design--control objectives are formulated into an LMI problem where powerful numerical toolboxes solve controller and observer gains. Finally, experiments are carried out to verify the theoretical results and show satisfactory performance both in transient response and robustness.

Cellular neural field and its convergence analysis.

A new continuum model complementary to traditional cellular neural networks is introduced in this note. We consider a cellular neural field formed by infinitely many cellular neurons and modelled by an integrodifferential equation. Then, using LaSalle's invariance principle on Banach space, we show that the field quantity will asymptotically converge to an equilibrium state under the condition that all equilibria of the system are isolated. From the practical sense, the convergence indicates the essential capability of retrieving message from original raw data.

Semi-decentralized adaptive fuzzy control for cooperative multirobot systems with H(infinity) motion/internal force tracking performance.

We present a semi-decentralized adaptive fuzzy control scheme for cooperative multirobot systems to achieve H(infinity) performance in motion and internal force tracking. First, we reformulate the overall system dynamics into a fully actuated system with constraints. To cope with both parametric and nonparametric uncertainties, the controller for each robot consists of two parts: 1) model-based adaptive controller; and 2) adaptive fuzzy logic controller (FLC). The model-based adaptive controller handles the nominal dynamics which results in both zero motion and internal force errors for a pure parametric uncertain system. The FLC part handles the unstructured dynamics and external disturbances. An H(infinity) tracking problem defined by a novel performance criterion is given and solved in the sequel. Hence, a robust controller satisfying the disturbance attenuation is derived being simple and singularity-free. Asymptotic convergence is obtained when the fuzzy approximation error is bounded with finite energy. Maintaining the same results, the proposed controller is further simplified for easier implementation. Finally, the numerical simulation results for two cooperative planar robots transporting an object illustrate the expected performance.