J. K. College

This paper is proposed a tunable opto-thermal sensor for ultra -high temperature-sensing by developing a one-dimensional (1-D) photonic crystal (PC) structure of alternating layers of Silicon-Dioxide (SiO₂), Titanium-Dioxide (TiO₂) and integrated with a single defect layer of Hafnium-Dioxide (HfO₂) based on the thermo-optic response. The materials are chosen by their unique optical and thermal behaviour especially dielectric properties, absorption loss and melting point of the materials. The sensor is analysed under high-temperature variations, where the RI changes induced by heating directly. The photonic band gap (PBG)is created by the RI variation of the periodic materials. The HfO₂ defect layer enables a sharp resonance peak within the PBG, significantly improving the detection capability of temperature in terms of RI. The optical and thermal behaviour of the proposed 1-D PC is modelled using the Transfer Matrix Method by python computation. Transmissions and reflections spectra are computed numerically and the significance performance of the sensor are plotted such as defect layer thickness variation (− 3

Hyaluronic acid (HA), a glycosaminoglycan widely used in cosmetic and healthcare formulations, is valued for its moisturizing, viscoelastic, and regenerative properties. Accurate determination of HA in commercial products has therefore been considered essential for quality control and regulatory compliance. Although several chromatographic and spectroscopic techniques have been employed for HA quantification, their use has been restricted by high operational cost, complex operation, and labour-intensive sample preparation. In this work, a low-cost, flexible, and disposable conductive paint-based paper (CPP) electrode modified with an electropolymerized hyaluronic acid-imprinted poly(p-phenylenediamine) film [HA-MI-P(p-PD)@CPP] has been fabricated for the selective detection of HA. The conductive paint was prepared by dispersing polyacrylate with a graphene nanosheet-graphite (GNS-g) composite in chloroform to yield a mechanically stable and highly conductive substrate. After electropolymerization, the template removal and rebinding processes were investigated using cyclic voltammetry (CV), differential pulse voltammetry (DPV), and electrochemical impedance spectroscopy (EIS) with the [Fe(CN)6]3-/[Fe(CN)6]4- redox couple. The analytical performance of the imprinted electrode was assessed from DPV signals obtained before and after exposure to varying HA concentrations. The method achieved a low limit of detection (LOD) of 0.009 μM and a limit of quantification (LOQ) of 0.032 μM, exhibiting two linear response ranges (0.1-60 μM and 60-1000 μM). High recoveries (97-104%) and an imprinting factor exceeding 3, confirmed strong selectivity against structurally related interferents. Its affordability, environmental compatibility, and reliable sensing capability highlight its potential for on-site monitoring of HA in cosmetic and healthcare products.

A new mononuclear Hg(II) complex, [Hg(9BuA)2Cl2] (1), was synthesized via a one-pot reaction employing the 9-butyladenine (9BuA) and characterized by using elemental analysis, spectroscopic methods (FT-IR, UV-Vis, 1H NMR), and single-crystal X-ray diffraction analysis. The complex 1 exhibits a distorted tetrahedral geometry, with two 9BuA ligands and two chloride ions coordinating to the mercury center. The supramolecular architecture of 1 is stabilized by directional hydrogen bonds, including N-H & sdot;& sdot;& sdot;N and C-H & sdot;& sdot;& sdot;(Cl/N). Hirshfeld surface analysis indicates that H & ctdot;H interactions are the most prevalent, while Cl & ctdot;H and N & ctdot;H interactions are statistically significant. Notably, C & ctdot;H interactions display the highest enrichment ratio, underscoring their critical role in supramolecular stabilization. Theoretical studies based on DFT analysis have also been employed to demonstrate complex 1's electronic property. The in vitro antibacterial study of complex 1 against four bacterial strains-Gram-positive bacterial strain (Staphylococcus aureus) and Gram-negative bacterial strains (Klebsiella pneumoniae, Pseudomonas aeruginosa, and Escherichia coli)-reveals selective and potent activity, specifically towards Pseudomonas aeruginosa. Moreover, a molecular docking study has been conducted to identify the biomolecular interactions and potential protein binding sites of 1 against Pseudomonas aeruginosa.

This study presents the design and development of a cost-effective, portable potentiostat system (LC-PSTAT) integrated with a molecularly imprinted polymer (MIP)-based graphite sensor (TQMIP-G) for the selective and sensitive detection of thymoquinone (TQ). TQ, the primary bioactive compound in black cumin (Nigella sativa), has significant therapeutic potential, necessitating precise and affordable detection methods. The novelty of this work lies in the design of a standalone electrochemical sensing platform that eliminates reliance on expensive commercial potentiostats and external data processing systems while maintaining high analytical performance. Electrochemical characterization using cyclic voltammetry (CV) and differential pulse voltammetry (DPV) confirmed the system's high selectivity, sensitivity, and reproducibility, achieving a detection limit of 0.2 mu M and a wide detection range of 1-200 mu M, with strong correlation to reverse-phase HPLC results. The integration of a custom-built low-power potentiostat with the TQMIP-G sensor enhances operational efficiency and significantly reduces costs, making it an ideal solution for real-time, on-site monitoring. This portable and low-cost sensing system has immense industrial relevance, particularly in pharmaceutical quality control, food safety, and agricultural monitoring, providing a rapid and decentralized alternative to conventional laboratory-based techniques.

The integration of Artificial Intelligence (AI) and Educational Robotics has created new opportunities for adaptive and personalized learning. Advances in Machine Learning (ML), Deep Learning (DL), Natural Language Processing (NLP), Computer Vision (CV), and Reinforcement Learning (RL) have transformed educational robots into intelligent learning companions capable of adapting to individual learner needs. This review examines the role of AI-driven educational robotics in adaptive learning by synthesizing its theoretical foundations, enabling technologies, architectural frameworks, educational impacts, challenges, and future directions. The review highlights key adaptive learning concepts, including learner modelling, personalization, and continuous feedback, and discusses how AI technologies support intelligent perception, decision-making, and learner adaptation. The findings suggest that AI-driven educational robots can enhance personalized learning, learner engagement, accessibility, and instructional effectiveness. However, challenges related to data privacy, algorithmic bias, scalability, and teacher–robot collaboration remain significant. Future advances in generative AI, multimodal interaction, lifelong learning systems, and immersive technologies are expected to further strengthen the capabilities of educational robotics. Therefore, AI-driven educational robotics represents a promising pathway toward more adaptive, inclusive, and learner-centred education. Keywords: Educational Robotics; Artificial Intelligence in Education; Adaptive Learning; Personalized Learning; Intelligent Tutoring Systems; Human–Robot Interaction.