Maharani's Science College for Women, Mysore

A new electrochemical sensor was fabricated for the selective analysis of melatonin (MEL). In this work, a novel sensing platform combining biosynthesized zinc oxide nanoparticles (ZnONPs) with electropolymerized DL-phenylalanine (DLP) was developed. The electrode was fabricated by modifying the surface of bare carbon paste electrode with biosynthesized Zinc oxide nanoparticles (BZnONPs/CPE) and further coated with an electrochemically polymerized DL-phenylalanine film (P(DLP)MZnONPs/CPE). The polymer layer enhanced the surface of the electrode and significantly improved the redox response of MEL, with the optimum peak obtained in 0.2 M phosphate buffer solution (PBS) at pH 4.0. The surface morphology and elemental composition were examined using Field Emission Scanning Electron Microscopy (FESEM), Energy-Dispersive X-ray Spectroscopy (EDX), and X ray diffraction (XRD). Electrochemical characteristics were explored through cyclic voltammetry (CV), linear sweep voltammetry (LSV), differential pulse voltammetry (DPV), and impedance spectroscopy (EIS). The scan rate results indicated that the process was diffusion controlled. The modified electrode displayed enhanced current responses across a broad concentration range for both LSV and DPV analyses. The detection limits (LOD) were calculated as 0.11 μm and 0.09 μm with corresponding limits of quantification (LOQ) were 0.37 μm and 0.30 μm using LSV and DPV methods, respectively. Notably, the developed sensor demonstrated improved analytical performance compared to conventional carbon paste electrodes, highlighting the advantage of the proposed composite configuration. The P(DLP)MZnONPs/CPE sensor also exhibited strong tolerance towards interfering metal ions and organic molecules, along with consistent reproducibility, repeatability and stability. Furthermore, it enabled the simultaneous determination of MEL, adenosine (ADN) and tryptophan (TR). This dual analyte detection capability further demonstrates the practical significance and novelty of the proposed sensing platform. The applicability of the developed electrode was verified by analysing pharmaceutical tablet samples, where precise and consistent results were obtained.

Indigo carmine (IC) has been used for over a century in textiles and medicine, but its potential environmental risks have made its detection increasingly important. To address this, we developed a poly(L-leucine) modified carbon paste electrode (P(LL)MCPE using a simple electro polymerization method. This modified electrode shows a strong oxidation signal at pH 7.0 and was characterized using the SEM technique to confirm successful surface modification. Its electrochemical behavior was evaluated using cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS). We optimized parameters like the effect of pH, scan rate variation, accumulation parameters, and concentration variation of IC and selectivity detection for IC, Fast shalpon black (FB), tartrazine (TT), and methyl red (MR). The electrode gave linear responses between 0.2 and 2.6 μM (CV) and 0.2 to 2.4 μM (DPV), with excellent sensitivity down to a limit of detection (LOD) of 0.02 μM (CV) and 0.06 μM (DPV) and limit of quantification (LOQ) of 0.67 μM (CV) and 0.23 μM (DPV). The fabricated sensor was presenting good stability, reproducibility, repeatability, and selectivity, even when interfering substances were present. Finally, the sensor proved effective for IC detection in a real sample application in tap water, with good recovery.

This study presents an integrative approach, correlating spectroscopic evidence of molecular interactions with incremental variations in macroscopic properties such as density (ρ), viscosity (η), and ultrasonic velocity (U), to elucidate the behavior of multicomponent liquid mixtures. Accordingly, FTIR spectroscopy was used to identify the molecular-level interactions, to interpret the changes in thermoacoustic properties and molar excess parameters of the two ternary liquid mixtures:benzaldehyde (BA) + 2-chlorophenol (CP) + n-hexane (NH) and cinnamaldehyde (CA) + CP + NH. These aspects were systematically investigated for different mole fractions of the liquid components (BA,CA, CP and NH) at four temperatures (T = 293.15–308.15 K). Experimental measurements of ρ, η and U were used to evaluate the key thermoacoustical parameters such as adiabatic compressibility (βₐd), intermolecular free length (Lf), free volume (Vf), internal pressure (π), and specific acoustic impedance (Z), along with their corresponding excess functions (βₐdᴱ, Lfᴱ, Vfᴱ, πᴱ, and Zᴱ). The experimental U values were analyzed using Nomoto (UN), Impedance (UIR), and Rao (UR) relations, and model suitability was assessed through average percentage deviation (APD). The results reveal pronounced non-linear composition dependence and significant temperature sensitivity in both systems. Comparative analysis shows that the CA-based mixtures exhibit lower ρ, η, u, and Z and higher βₐd and Vf than the BA-based system, indicating looser molecular packing and weaker associative interactions. FTIR spectral analysis confirms O–H···O=C hydrogen-bonded complexes between CP and the aldehydes, with stronger interactions in the BA system and weaker, more delocalized associations in the CA system due to extended π-conjugation.

This study investigates the interplay of mixed velocity and thermal boundary conditions on the onset of local thermal non-equilibrium (LTNE) Darcy–Bénard convection in an anisotropic porous layer. Four distinct combinations of boundary permeability—impermeable/impermeable, porous/porous, porous/impermeable, and impermeable/porous are considered, with a constant heat flux lower surface and an isothermal upper surface. Anisotropies in permeability and thermal conductivities of the fluid and solid phases are incorporated, assuming horizontal isotropy. Linear stability of the steady basic state is examined using a normal-mode analysis of the governing perturbation equations. It is established that the principle of exchange of stabilities holds for all the velocity boundary conditions examined, and the corresponding eigenvalue problem is solved numerically by the shooting method. The system is most stable for impermeable–impermeable boundaries and least stable for porous–porous boundaries, with mixed configurations showing intermediate behavior. Increased mechanical anisotropy and reduced thermal anisotropy, inter-phase heat transfer, and porosity-modified conductivity ratios destabilize the flow, while a lower isoflux–upper isothermal configuration requires a smaller temperature difference to trigger convection compared to the isothermal–isothermal case.

Offline signature verification is a traditional method used to authenticate a person since ancient times. Though it has been studied for over 40 years, it remains a challenge for the research community due to two major issues: intra-person variation and inter-person similarity. To address these issues, it is crucial to determine stability-driven, consistent features that are stable, repeatable, and influential discriminatory features across genuine signature samples of the same signer. In this paper, a Multi-Phase Fusion Architecture is proposed, where fusion at multiple stages is employed to develop a reliable signature verification system. The primary aim of this work is to identify a compact set of consistent and stable discriminatory features from offline signature images that repeatedly appear across genuine signature samples of the same individual. The first phase of the proposed method combines global texture features with fine-grained texture features to effectively capture the discriminative characteristics of handwritten signatures. Proceeding with the second phase to determine the consistent features, multiple feature selection techniques encompassing both filter-based and wrapper-based methods were explored. More emphasis is placed on stability analysis, which evaluates the consistency and robustness of the selected features through stability metrics across varying datasets. Evaluating the consistency of features ensures that the model generalises well on unseen and challenging signature samples in real-world scenarios. In the third phase, an ensemble learning technique is used to achieve enhanced reliability in final decision-making. The proposed method is evaluated on multilingual signature datasets: CEDAR, BHSig260 (Hindi, Bengali), MCYT-75, and UTSig, achieving accuracies of 100