We provide here structural features and the optical properties of Niobium (V) Oxide nanoparticles (NPs) synthesized by thermolysis method. It A quick and affordable technique to produce NPs has been found, and it was utilized to create Niobium (V) Oxide NPs using a set amount of precursor Niobium pentachloride and a variable amount of surfactant Polyvinylpyrrolidone (PVP). Utilizing X-ray diffraction (XRD), Field Emission Scanning Electron microscopy (FESEM), Fourier Transform Infrared Spectroscopy (FT-IR), and Ultraviolet-Visible (UV-Vis) spectroscopic techniques and Raman Spectroscopy, NPs were thoroughly studied. XRD and Raman Spectroscopy were employed to verify the crystalline quality and stoichiometry of these nanoparticles. The average crystallite size of nanoparticles calculated from XRD analysis was observed to be 18-30nm. Measurements of diffuse reflectance spectroscopy in the ultraviolet-visible region indicate that the optical band gap values (similar to 3.10eV, 3.11eV, 3.13eV, and 3.14eV) varied during the crystal formation process. The orthorhombic phase of the material is shown by the Raman vibrational modes. The material's vibrational information is collected by FT-IR, which identifies the various modes of stretching vibration of oxygen and niobium in the spectrum. It was found that all samples exhibited the fingerprint stretching vibrations within a particular range. The work identifies the characteristics of Niobium (V) Oxide nanoparticles that make the material adaptable, enabling its use in high-power batteries, sensors, and supercapacitors. PVP's effectiveness as a capping agent for nanoparticle formation is also described.
This study compares the physiological and biochemical effects of 3-ring polycyclic aromatic hydrocarbons (PAHs), Anthracene (ANT, linear arrangement) and Phenanthrene (PHE, angular arrangement), on microalgae Chlorella vulgaris and Scenedesmus acutus. The findings revealed that PHE exerted significant toxic effects on algal growth and photosynthetic parameters, including electron transport rate of PSII [ETR(II)] and quantum yield of PSII [Y(II)], in both species. Additionally, it significantly influenced the regulation of the non-photochemical quenching mechanism [Y(NPQ) and Y(NO)], which was responsible for the energy distribution. Moreover, it altered the photosynthetic pigments content, biomolecules profile, and non-enzymatic antioxidants in both microalgae. On the other hand, ANT exposure showed noticeable positive changes in the measured parameters. Both microalgae exhibited a slight increase in chlorophyll content, biochemical compounds, and PSII activity, with the more pronounced effects observed in S. acutus, indicating its higher resistance to ANT. Furthermore, our findings also reveal that both species effectively removed ANT and PHE from the medium within 7 days of cultivation. These findings suggest that PAH toxicity is not only influenced by molecular weight and the number of benzene rings but also by the structural arrangement of their rings. Importantly, this study highlights that both microalgae can tolerate ANT and are suitable candidates for ANT bioremediation, but not for PHE.
Large metallic components are fabricating through wire arc direct energy deposition (WA-DED) which is most preferred method among all additive manufacturing (AM) methods due to its exceptionally high deposition rate. However, the high deposition rate introduces defects including cracking, porosity and other defects all of which significantly compromise the structural integrity and overall performance of WA-DED components. Accurate detection of these defects within the component are therefore paramount importance. Conventionally, such defects have been identified through non-destructive testing (NDT) methods. However, these NDT methods are associate with limitations such as dependency on skilled human intervention and specific kind of NDT tool or instrument for the identification of each kind of defects. These limitations overcome thorough deep learning techniques, whereas, the current research collected the acoustic signals are acquired using a high sensitivity microphone, then perform slitting using wire cut EDM of fabricated samples to identified the corresponding defects and then perform preprocessing to eliminate the noise of the acoustic signals. The resulting data is analysed by the time and frequency domain and the feature extraction by Fast Fourier Transform (FFT). The CNN and several machine learning (ML) models are trained validated using k-fold cross validation techniques. Among all evaluated models, the CNN framework outperforms, and its accuracy is 99
A K-tapered two-frequency undulator configuration is proposed to explore its potential advantages over conventional helical wiggler-based inverse free-electron lasers. The conventional helical wiggler based inverse free electron configuration provides efficient acceleration in the optical regime at low electron energies, it becomes fundamentally limited at high electron beam energy due to radiation losses and resonance detuning. The two-frequency tapered undulator overcomes these limitations through dynamic resonance control, leading to sustained acceleration even at high electron beam energies. We presents an analytically derived dual-frequency resonance tapered inverse free electron laser configuration that modifies the longitudinal phase dynamics and enhances energy extraction compared to conventional helical wiggler inverse free electron laser configurations. The theoretical research incorporates the Lorentz force equation, generalized Bessel functions, and energy exchange relations to derive analytical expressions for electron trajectories, acceleration gradient. This study provides for a comprehensive evaluation of how tapering with a dual-frequency undulator affects beam-wave interaction. The results demonstrate that the resonance tapered two-frequency undulator scheme provides a significantly higher acceleration gradient than conventional configurations, thereby enabling more efficient energy transfer between the electron beam and the radiation field. In addition, the design offers an alternative to the helical wiggler inverse free electron laser, providing a simpler geometry for the inverse free electron laser accelerator. This analytical treatment underscores the capability of the proposed approach to advance IFEL accelerator technology by offering enhanced efficiency and performance compared with conventional wiggler-based systems.
Medical science has worked proactively for long years—sees symptoms, treat them. Yet, the deployment of AI is changing this to a creative predictive environment [1]. This review questions that transition, from being physician subjective to highly-precision data based diagnosis [2]. We review the literature in the field on Machine Learning (ML) and Deep Learning (DL) which suggests that models such as Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks are obtaining diagnostic accuracies of 90% and up in areas such as oncology and neurology[3]. But beyond the technical marvels we dive into the uncomfortable fault lines: A vast "black box" of algorithmic decision-making The danger of data-bias Why there’s a desperate need for XAI (Explainable AI)[4]. Finally, this article envisioned one future in which rather than replacing the clinician, AI helps them via precision medicine and real-time surveillance[5].
