Sona College of Technology

Covellite (CuS) nanostructures were synthesized via a chelation-assisted hydrothermal route using EDTA and PEG, followed by annealing at 100 and 200 °C, to regulate their structural, optical, and electrochemical properties for organic pollutant degradation and energy storage applications. X-ray diffraction confirms a hexagonal covellite phase for all samples, with chelating agents promoting preferential (110) growth. PEG-assisted annealing induces a morphological transition from irregular aggregates to uniform nanoflower architectures, most pronounced for PEG–CuS annealed at 200 °C. BET analysis shows that CuS–PEG has a higher surface area (28.70 m2 g−1, 15.7 nm pores) than CuS–EDTA (27.58 m2 g−1, 23.30 nm), with uniform mesopores that enhance active sites and ion diffusion. XPS verifies availability of Cu and S. The bandgap is tunable from 1.1 to 1.4 eV and correlates with crystallite size, enabling strong visible-light absorption, while suppressed electron–hole recombination enhances charge separation. Under natural sunlight, methyl orange acts as a photosensitizer, injecting excited electrons into the CuS conduction band and significantly improving photocatalytic activity. Scavenger studies and DFT-based Fukui function analysis confirm a dominant photo-sensitized charge-transfer pathway, with hydroxyl radicals as secondary reactive species. Beyond photocatalysis, PEG-derived CuS nanoflowers demonstrate excellent electrochemical performance as the positive electrode in an asymmetric supercapacitor, delivering 415 F g−1 at 1 A g−1, an energy density of 147 Wh kg−1 at 856 W kg−1, and 98

Hydrogen bond liquid crystal complexes (HBLCs) are successfully isolated from the mesogenic compounds of 4-n-alkyloxybenzoic acid (nOBA, n = 10 and 11), and the non-mesogenic compound of 1,3‑phenylenediacetic acid. Formation of H-bonds between the mesogen and non-mesogen and the presence of functional groups are detailed using FTIR analysis, whereas, UV–Vis spectroscopic analysis endorses the nature of the electronic transition and optical properties of the HBLC complexes. Polarizing optical microscopy (POM) textural analysis authenticates the presence of induced mesophases along with transition temperatures. Mesophase transition temperatures and changes in enthalpy, entropy of the resultant mesogens is assessed by differential scanning calorimetry (DSC) studies. It is noticed that the formation of H-bonds induces rich phase polymorphism with an extended mesogenic range. Furthermore, the thermodynamic equilibrium of the HBLC system is confirmed by the enthalpy calculation, which shows that the amount of energy absorbed by the system is equal to the amount of energy released by the system. Another interesting observation is that the thermochromic phenomenon exhibited in the nematic phase offers valuable insights for diverse sensor applications. The repeated thermal scanning and positive entropy values favour the thermally stable mesogenic HBLCs. Moreover, the obtained result elucidates the critical role of hydrogen bonding and molecular ordering in governing the mesomorphic behavior of the synthesized HBLC complex.

Wearable electronics necessitate flexible, safe, and high-efficiency energy storage systems to power integrated sensors for real-time health monitoring; however, traditional metal-ion supercapacitors are hindered by limited energy density, mechanical rigidity, and safety issues for skin-contact applications. To tackle these issues, a band-aid-style portable AIHSC was created utilizing a Ti₃C₂Tₓ/ WSe2 nanocomposite for positive electrode, activated carbon for negative electrode, and a PVA/(NH₄)₂SO₄ gel electrolyte on flexible textile substrate. The Ti₃C₂Tₓ/WSe2 heterostructure was synthesized to synergistically improve electrical conductivity, ion transport, and pseudocapacitive charge storage, while flexible electrodes were produced using a scalable doctor-blade coating method. The electrochemical assessment demonstrated that Ti₃C₂Tₓ/WSe2 electrode exhibited a substantial specific capacitance of 252 F g⁻¹ in 1 M (NH₄)₂SO₄, whereas the constructed AIHSC attained a specific power density of 1332 W kg⁻¹, capacitance of 120 F g⁻¹, and an energy density of 60 Wh kg⁻¹, maintaining 92

The introduction of Internet of Things (IoT) devices into smart grid frameworks has posed significant cybersecurity challenges, including advanced attacks such as false data injection, distributed denial-of-service (DDoS), and sophisticated network intrusions. In this paper, a new hybrid deep learning architecture is proposed that combines Convolutional Neural Networks (CNNs) to extract spatial features with Long Short-Term Memory (LSTM) networks to capture temporal patterns, and an autoencoder-based preprocessing unit to reduce dimensionality and remove noise. The architecture suggested, AE-CNN-LSTM, can detect anomalies in real time and is designed as an edge-deployable framework, making it highly suitable for resource-constrained IoT-enabled smart grid environments. The experimental validation of the technology on two benchmark datasets, CICIoT2023 and UNSW-NB15, shows improved performance, with accuracies of 99.15

This study benchmarks four lightweight YOLO nano-variant object detection models, namely YOLOv5n, YOLOv8n, YOLOv11n, and YOLOv12n, using 701 thermal images of okra (Abelmoschus esculentus), comprising 355 adequately mature and 346 overripe samples. The models were evaluated using mean average precision (mAP@0.5−0.95), precision, recall, and inference latency across heterogeneous computing platforms, including GPU-accelerated parallel inference using an NVIDIA T4 GPU with TensorRT and sequential CPU execution using ONNX Runtime. This benchmarking addresses a critical gap in high-performance computing (HPC) deployment for non-RGB modalities, where real-time throughput exceeding 600 FPS requires GPU-level parallelism and optimized operator fusion. Under a 10-epoch training protocol designed for resource-efficient deployment, YOLOv8n achieved the highest detection accuracy with a mean mAP of 66.3 ± 0.3