“Nano”: an emerging avenue in electrochemical detection of neurotransmitters

The growing importance of nanomaterials toward the detection of neurotransmitter molecules has been chronicled in this review. Neurotransmitters (NTs) are chemicals that serve as messengers in synaptic transmission and are key players in brain functions. Abnormal levels of NTs are associated with numerous psychotic and neurodegenerative diseases. Therefore, their sensitive and robust detection is of great significance in clinical diagnostics. For more than three decades, electrochemical sensors have made a mark toward clinical detection of NTs. The superiority of these electrochemical sensors lies in their ability to enable sensitive, simple, rapid, and selective determination of analyte molecules while remaining relatively inexpensive. Additionally, these sensors are capable of being integrated in robust, portable, and miniaturized devices to establish point-of-care diagnostic platforms. Nanomaterials have emerged as promising materials with significant implications for electrochemical sensing due to their inherent capability to achieve high surface coverage, superior sensitivity, and rapid response in addition to simple device architecture and miniaturization. Considering the enormous significance of the levels of NTs in biological systems and the advances in sensing ushered in with the integration of nanotechnology in electrochemistry, the analysis of NTs by employing nanomaterials as interface materials in various matrices has emerged as an active area of research. This review explores the advancements made in the field of electrochemical sensors for the sensitive and selective determination of NTs which have been described in the past two decades with a distinctive focus on extremely innovative attribut,es introduced by nanotechnology

Stego on FPGA: An IWT Approach

A reconfigurable hardware architecture for the implementation of integer wavelet transform (IWT) based adaptive random image steganography algorithm is proposed. The Haar-IWT was used to separate the subbands namely, LL, LH, HL, and HH, from 8×8 pixel blocks and the encrypted secret data is hidden in the LH, HL, and HH blocks using Moore and Hilbert space filling curve (SFC) scan patterns. Either Moore or Hilbert SFC was chosen for hiding the encrypted data in LH, HL, and HH coefficients, whichever produces the lowest mean square error (MSE) and the highest peak signal-to-noise ratio (PSNR). The fixated random walk’s verdict of all blocks is registered which is nothing but the furtive key. Our system took 1.6 µs for embedding the data in coefficient blocks and consumed 34% of the logic elements, 22% of the dedicated logic register, and 2% of the embedded multiplier on Cyclone II field programmable gate array (FPGA)

Modulation of ZnO film thickness and formation of water-hyacinth nanostructure

The influence of precursor medium was investigated on the structural, morphological, optical and electrical properties of spray pyrolysis deposited nanostructured ZnO thin films. Three batches of ZnO thin films were deposited on glass substrates using three different solvents (water, water-ethanol [ratio of 1:1] and ethanol) based precursor solution of zinc nitrate hexahydrate. The substrate temperature was fixed at 523 K. The variation in film thickness from 150 to 875 nm was observed as the effect of changing solvent medium. X-ray diffraction (XRD) data confirmed the formation of polycrystalline ZnO thin films with hexagonal wurtzite crystallite structure and the estimated crystallite size was found to be ranging from 31 to 55 nm. Scanning electron micrographs revealed the formation of water-hyacinth shaped nanostructures when water-ethanol mixture was used as the solvent medium. Interestingly, UV-vis spectrophotometer revealed the formation of ZnO film with twin optical band gap of 3.15 eV and 3.56 eV when ethanol was used as the solvent medium. The modulation of film thickness and grain size by solvent medium has strongly influenced the electrical conductivity of ZnO thin films. The homogenous nano-spherical grains with uniform grain boundaries showed a good response towards 100 ppm of ammonia at room temperature

Colour guided colour image steganography

Abstract-Information security has become a cause of concern because of the electronic eavesdropping. Capacity, robustness and invisibility are important parameters in information hiding and are quite difficult to achieve in a single algorithm. This paper proposes a novel steganography technique for digital color image which achieves the purported targets. The professed methodology employs a complete random scheme for pixel selection and embedding of data. Of the three colour channels (Red, Green, Blue) in a given colour image, the least two significant bits of any one of the channels of the color image is used to channelize the embedding capacity of the remaining two channels. We have devised three approaches to achieve various levels of our desired targets. In the first approach, Red is the default guide but it results in localization of MSE in the remaining two channels, which makes it slightly vulnerable. In the second approach, user gets the liberty to select the guiding channel (Red, Green or Blue) to guide the remaining two channels. It will increase the robustness and imperceptibility of the embedded image however the MSE factor will still remain as a drawback. The third approach improves the performance factor as a cyclic methodology is employed and the guiding channel is selected in a cyclic fashion. This ensures the uniform distribution of MSE, which gives better robustness and imperceptibility along with enhanced embedding capacity. The imperceptibility has been enhanced by suitably adapting optimal pixel adjustment process (OPAP) on the stego covers

Fabrication of Electrochemical Sensor for the Detection of Mg(II) Ions Using CeO₂ Microcuboids as an Efficient Electrocatalyst

In human blood serum, the concentration of magnesium ions typically ranges from 0.7 mM to 1.05 mM. However, exceeding the upper limit of 1.05 mM can lead to the condition known as hypermagnesemia. In this regard, a highly sensitive and selective electrochemical sensor for Mg(II) ion detection was successfully fabricated by immobilizing cerium oxide (CeO2) microcuboids, synthesized via microwave radiation method, onto the surface of glassy carbon electrode (GCE). Cyclic voltammetry studies revealed the exceptional electrocatalytic effect of CeO2 microcuboid-modified GC electrode, particularly in relation to the irreversible reduction signal of Mg(II). The microcuboid-like structure of CeO2 microparticles facilitated enhanced adsorption of Mg(II) ion (Γ=2.17×10−7mol cm−2) and electron transfer (ks=8.94 s−1) between the adsorbed Mg(II) ions and GCE. A comprehensive analysis comparing the performance characteristics of amperometry, differential pulse voltammetry, cyclic voltammetry, and square wave voltammetry was conducted. The square wave voltammetry-based Mg(II) sensor exhibited remarkable sensitivity of 2.856 μA mM−1, encompassing a broad linear detection range of 0–3 mM. The detection and quantification limits were impressively low, with values of 19.84 and 66.06 μM, respectively. Remarkably, the developed electrode showed a rapid response time of less than 140 s. Multiple linear regression and partial least squares regression models were employed to establish a mathematical relationship between magnesium ion levels and electrochemical parameters. Notably, the proposed sensor exhibited excellent anti-interferent ability, repeatability, stability, and reproducibility, enabling the fabricated electrode to be used effectively for Mg(II) ion sensing in real-world samples

Metabolic Syndrome—An Emerging Constellation of Risk Factors: Electrochemical Detection Strategies

Metabolic syndrome is a condition that results from dysfunction of different metabolic pathways leading to increased risk of disorders such as hyperglycemia, atherosclerosis, cardiovascular diseases, cancer, neurodegenerative disorders etc. As this condition cannot be diagnosed based on a single marker, multiple markers need to be detected and quantified to assess the risk facing an individual of metabolic syndrome. In this context, chemical- and bio-sensors capable of detecting multiple analytes may provide an appropriate diagnostic strategy. Research in this field has resulted in the evolution of sensors from the first generation to a fourth generation of ‘smart’ sensors. A shift in the sensing paradigm involving the sensing element and transduction strategy has also resulted in remarkable advancements in biomedical diagnostics particularly in terms of higher sensitivity and selectivity towards analyte molecule and rapid response time. This review encapsulates the significant advancements reported so far in the field of sensors developed for biomarkers of metabolic syndrome