The growing occurrence of pharmaceutical contaminants in aquatic systems has intensified the demand for advanced nanostructured materials capable of selective adsorption and removal of such pollutants. Paracetamol, a commonly used analgesic and antipyretic drug, is frequently detected in wastewater and poses severe ecological and health risks when accumulated in the water bodies and the soil. In this study, density functional theory (DFT) calculations were employed to investigate the structural, electronic, and adsorption behaviors of phosphorus (P), sulfur (S), and silicon (Si) doped COF-PEDOT frameworks for paracetamol adsorption. All geometries were optimized using the PBE0-D3/6-311G(d) basis set. The optimized structures exhibited minimal distortion after adsorption, indicating stable interactions between the adsorbate and the doped surfaces. Density of states (DOS) analysis revealed that heteroatom incorporation enhanced the electronic activity and reactivity of the complexes, while frontier molecular orbital (FMO) analysis showed a notable narrowing of the energy gap, confirming improved electron transfer capability. The ionization potential values (5.29-5.44 eV) remained within the range of moderately stable adsorbents. Natural bond orbital (NBO) analysis indicated that phosphorus doping produced the highest orbital stabilization energies, suggesting stronger donor-acceptor interactions. Adsorption energy calculations yielded negative values for all systems, confirming exothermic and thermodynamically favorable adsorption processes. Furthermore, quantum theory of atoms in molecules (QTAIM) and non-covalent interaction (NCI) analyses demonstrated the presence of weak but stable van der Waals and hydrogen-bond interactions governing paracetamol adsorption. The results demonstrate that tailored heteroatom doping can effectively tune the electronic and adsorption characteristics of COF-PEDOT frameworks. The P-, S-, and Si-doped systems exhibit enhanced sensitivity, stability, and reversibility, making them promising candidates for the selective adsorption of paracetamol from pharmaceutical contaminants in aquatic environments.
The quantum-classical convergence offers a new paradigm of the next-generation Nano electronic systems. The paper discusses the design and the application of the hybrid quantum-classical Nano devices merging quantum-related phenomenon: superposition, tunnelling, and entanglement, with classical control architecture. The focus is put on scalable nanomaterial platforms, interface engineering, and maintenance of coherence at room temperature. The work explores new hybrid designs that can help to achieve a higher density of computation, efficiency, and adaptability of functions. Simulation outcomes and experimental data depict that quantum-aided logic units can be better as compared to standard Nano scale transistors in information processing. This paper fills the void between the new quantum technologies and the well-developed classical systems, leading to the viable quantum-enhanced computation and smart Nano electronic systems.
Urban street lighting systems are essential components of modern smart city infrastructure, yet conventional lighting systems suffer from high energy consumption, delayed fault detection, and limited monitoring capabilities. This paper proposes a hybrid EdgeAI framework for adaptive smart street lighting that integrates environmental sensing, electrical parameter monitoring, and intelligent fault detection within a distributed IoT architecture. The proposed system employs a Raspberry Pi edge controller combined with LDR, PIR, and INA219 sensors to continuously monitor ambient illumination, motion activity, voltage, and current consumption at the lighting node. A Decision Tree-based adaptive control algorithm dynamically determines lamp operation based on real-time environmental conditions, enabling intelligent ON/OFF switching and adaptive brightness management. To improve system reliability, an electrical state verification mechanism compares predicted lamp states with measured current characteristics to identify hardware faults such as open circuits, relay failures, and abnormal power consumption. Experimental validation demonstrates that the proposed system achieves 80% adaptive control accuracy and 91.8% fault detection accuracy with a false alarm rate of 5.6%. Electrical measurements confirm stable voltage behavior during switching and reliable differentiation between normal and faulty conditions. The proposed framework supports future predictive maintenance extensions. It uses regression and LSTM-based forecasting models to analyze long-term electrical behavior. Experimental evaluation shows better adaptive control performance, stable electrical operation, and effective edge-level fault detection that is suitable for intelligent street lighting applications. The proposed edgeintelligent architecture reduces fault detection latency, minimizes network dependency, and provides a scalable foundation for large-scale smart city lighting deployments.
Lead-free vacancy-ordered double perovskite Cs2SnI6 has emerged as a promising alternative to toxic lead-based halide perovskites for optoelectronic applications. In this work, Cs2SnI6 was successfully synthesized via hydrothermal and microwave methods, and their structural, optical, morphological, photodetector, and photovoltaic properties were systematically compared. XRD confirmed phase-pure cubic (Fm-3m) structure, with hydrothermal samples showing superior crystallinity. Raman and XPS analyses verified stable Sn4+ states and strong Sn-I bonding, indicating enhanced chemical stability. Optical studies revealed strong visible-NIR absorption with band gaps of 1.46-1.47 eV, while hydrothermal samples exhibited higher absorption, stronger photoluminescence, and longer carrier lifetimes (108 ns), suggesting reduced recombination. Photodetectors (ITO/TiO2/Cs2SnI6/Ag) demonstrated broadband response with high responsivity (17 A W-1) and detectivity (similar to 10(12) Jones). Solar cells (FTO/TiO2/Cs2SnI6/Spiro-OMeTAD/Au) achieved 10.39% efficiency, outperforming microwave-derived devices. Enhanced performance is attributed to reduced recombination and improved charge transport.
Melanoma is a very aggressive skin cancer that would need early and correct diagnosis to be treated. Nevertheless, it is not an easy task to distinguish between melanoma and benign lesions since melanoma and benign lesions may look similar re- garding their visual characteristics including color, texture and shape. The present paper introduces an automated melanoma classification system that will be based on an ensemble model based on Convolutional Neural Network (CNN). The suggested system would involve preprocessing methods that include nor- malization, artifact elimination and lesion segmentation to en- hance the quality of the input. The feature extraction is per- formed on multiple CNN models whose outputs are weighted av- eraged to increase the classification accuracy and minimize over- fitting. Experimental outcomes show better performance using the experimental methods over single-model methods, as well as accuracy and robustness. Explainable AI techniques are also in- corporated in the system to aid in clinical decision-making. Keywords — Melanoma, Dermoscopy Images, CNN, Ensemble Learning, Deep Learning, Medical Image Analysis.
