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 [email protected] 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 [email protected]/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.

In this paper, a novel electrochemical sensor based on flower shaped Zinc Oxide (ZnO) nanoparticles for the determination of dopamine is reported. Flower shaped ZnO nanoparticles is synthesized by a new aqueous solution method without the use of any shape directing reagents and the verification of its successful formation is carried out by scanning electron microscope (SEM) and X-ray diffraction analysis (XRD). Cyclic Voltammetry (CV), Differential Pulse voltammetry (DPV) and amperometric (i-t) techniques are used to study the electrochemical characteristics of flower shaped ZnO nanoparticles. In this study, the lower oxidation potential and higher oxidation peak current of flower shaped ZnO modified glassy carbon electrode (GCE) indicates that its electrocatalytic activity is better than bare GCE in the oxidation of dopamine. Even in the presence of common interfacing compounds, the modified GCE exhibited excellent selectivity which is a trait of good electrochemical sensor. The linear response range of the fabricated electrochemical sensor based on flower shaped ZnO nanoparticles is 0.1 - 16 µM, limit of detection is 0.04 µM and the sensitivity is 4.38 µAµM-1 cm-2. The low detection limit, excellent sensitivity and wide linear response range signified the efficiency of flower shaped ZnO nanoparticles based electrochemical sensor for the detection of dopamine.

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.

Keyword recognition is the basis of speech recognition, and its application is rapidly increasing in keyword spotting, robotics, and smart home surveillance. Because of these advanced applications, improving the accuracy of keyword recognition is crucial. In this paper, we proposed voice conversion (VC) - based augmentation to increase the limited training dataset and a fusion of a convolutional neural network (CNN) and long-short term memory (LSTM) model for robust speaker-independent isolated keyword recognition. Collecting and preparing a sufficient amount of voice data for speaker-independent speech recognition is a tedious and bulky task. To overcome this, we generated new raw voices from the original voices using an auxiliary classifier conditional variational autoencoder (ACVAE) method. In this study, the main intention of voice conversion is to obtain numerous and various human-like keywords’ voices that are not identical to the source and target speakers’ pronunciation. Parallel VC was used to accurately maintain the linguistic content. We examined the performance of the proposed voice conversion augmentation techniques using robust deep neural network algorithms. Original training data, excluding generated voice using other data augmentation and regularization techniques, were considered as the baseline. The results showed that incorporating voice conversion augmentation into the baseline augmentation techniques and applying the CNN-LSTM model improved the accuracy of isolated keyword recognition.