This study highlights the significance of thiazole-indole scaffolds in the development of potential therapeutics for Alzheimer's disease. A new series of indole-tethered thiazole derivatives was synthesized and characterized by spectroscopic techniques. Docking revealed favorable binding poses, and MM/GBSA binding free energies (-36 to -55 kcal/mol) confirmed strong ligand-protein interactions within the BACE-1 active site. A 200 ns molecular dynamics simulation confirmed complex stability, with protein RMSD of similar to 2-3 & Aring; and ligand RMSD of similar to 3-4.5 & Aring;, supported by persistent hydrophobic and hydrogen-bond interactions with key residues, and in silico ADMET analysis suggested acceptable pharmacokinetic properties (QplogBB values between -3.0 and 1.2 and oral bioavailability >80%). DFT calculations were performed to assess the electronic stability of the molecule using the B3LYP/TZVP basis set. Further, optical properties studies were conducted using ultraviolet-visible and photoluminescence spectroscopy. UV-Vis spectra obtained in DMF displayed two sets of absorption bands near 250-300 nm and 350-400 nm, attributed to tc-tc* transitions. Photoluminescence measurements revealed a single emission peak centered near 440-560 nm in solution. Furthermore, density functional theory (DFT) was employed for excited-state and absorption studies to gain deeper insight into molecular structure and electronic transitions. Excited-state DFT analysis provided deeper insight into electronic transitions. Overall, these hybrids demonstrate promising stability and drug-like properties as potential BACE-1 inhibitors.
p-type NiO thin films were deposited on n-type Si substrates via spray pyrolysis and were subjected to proton rays of energy 50 keV for varying durations. The effect of this exposure was studied on the structural, optical and electrical properties. It was found that controlled proton irradiation initially enhances UV photodetection through beneficial defect engineering, whereas excessive irradiation causes carrier trapping and structural degradation. XRD analysis revealed reduction in the peak intensity and crystallite size suggesting that the proton rays have induced structural defects in the system. The scanning electron microscopy revealed deterioration in surface morphology with increasing duration of proton irradiation. Optical studies showed reduced reflectance in irradiated samples due to the increased strain and defects in the system. Photoluminescence spectra revealed the defects states and indicated quenching of peak intensity. Rectifying behavior was observed in all the samples during electrical analysis. Proton irradiation enhanced conductivity due to the increased defect density. Improved photodetection was observed in the samples after irradiation. However, excessive irradiation caused deterioration in structural integrity, detectivity and transport stability due to increased clustering and carrier trapping. The study reveals the correlation between the defects induced by proton irradiation and the performance of the spray deposited Si/NiO photodetectors. The results presented here will help us to understand and optimize radiation resistant UV detectors for use in harsh radiation environment.
This study investigates the influence of four distinct pre-heating temperatures (150 °C, 200 °C, 250 °C and 300 °C) on the V2O5 thin films synthesized via the sol-gel spin coating method. Structural analysis using XRD revealed mixed orthorhombic phases of α- V2O5 and V4O9, with crystallite sizes varying from 18.5 to 24.3 nm depending on pre-heating conditions. While SEM confirmed the uniform grain morphology, EDAX corroborated the elemental composition. Optical studies indicated the bandgap values are nearly 2.40 eV and subtle variations in Urbach energy, with the lowest disorder observed at 250 °C. Photoluminescence spectra exhibited a non-monotonic trend, where pre-heating to 250 °C suppresses deep-level defect emissions (DLE) centred at 565 nm while eliminating non-radiative quenching pathways to produce a strong near-band-edge (NBE) emission at 459 nm. XPS analysis confirmed that pre-heating at 250 °C promotes the recovery process of coordination deficient defects, increasing the percentage of structured lattice oxygen from 56.10% to 58.50% and maintaining a dominant V5+ oxidation state. The chemiresistive measurements demonstrated a maximum response of 25.1% towards 5 ppm of H2S gas and a practical limit of detection (LOD) of 1 ppm at an optimized operating temperature of 300 °C for the V2O5 thin films preheated at 250 °C. These findings identify 250 °C as the optimal pre-heating temperature for producing stoichiometric, defect-engineered V2O5 films for environmental monitoring.
The growing demand for high-performance power conversion has led to the widespread adoption of multilevel inverters (MLIs), which are capable of delivering high-quality output voltages with reduced harmonic distortion. However, their reliance on a greater number of semiconductor switches makes them susceptible to failure, thereby compromising overall system reliability. This work proposes a diagnostic and fault-tolerant control strategy for a three-phase three-level T-type (3L-T2) MLI. The diagnostic framework uses a discrete Fourier transform (DFT) to obtain the fundamental frequency components of pole voltages and phase currents, which are then normalized to detect faulty phases and perform switch-level fault diagnosis accurately. Unlike conventional AI-based techniques that impose high computational demands, the proposed approach directly leverages spectral features, providing a computationally simple signal-processing and threshold-based diagnostic framework. Furthermore, a fault-tolerant mechanism is incorporated, which utilizes an auxiliary leg and bidirectional switches to enable inverter reconfiguration during fault events, thereby ensuring uninterrupted operation. The effectiveness of the proposed methodology is evaluated through MATLAB/Simulink simulations and real-time hardware-in-the-loop validation using the OPAL-RT4510 platform, demonstrating fault detection, switch-level diagnosis, and post-fault-tolerant operation under varying operating conditions. The abovementioned work contributes to the United Nations Sustainable Development Goal 7 (SDG 7) by advancing clean and sustainable energy solutions.
It is a key challenge in the electrospinning of polymeric nanofibers to accurately control the fiber diameter due to the nonlinear and complex relationships among process parameters. When experimental datasets are limited, it is a resource-intensive and time-consuming practice to perform optimization using the conventional trial-and-error technique. In this data-driven approach, the electrospinning parameters for the fabrication of Nylon 6 nanofibers were optimized using machine learning techniques. The Taguchi L9 orthogonal array was used to generate experimental data by systematically varying parameters such as the polymer concentration, applied voltage, flow rate, and needle-to-collector distance, with the average fiber diameters measured using scanning electron microscopy. Various regression and ensemble machine learning models were trained and evaluated using systematic hyperparametric tuning. Among all the models used, the Extra Trees regressor provided the most reliable predictive performance, achieving a coefficient of determination R² score of 0.99 and Mean Average Error (MAE) of 0.2003 with an overall efficiency of 99.89%. The SEM-derived fiber diameters were used to validate the model predictions, confirming the experimental consistency. The feature-importance plot proved the dominance of the applied voltage and flow rate. The proposed approach significantly reduces the experimental effort required for Nylon 6 nanofiber synthesis.
