National Institute of Engineering

In this study, heterojunction nanostructures S1-TiO2 and S2-TiO2/NiTiO3—were successfully synthesized via a microwave-assisted solution combustion technique. The heterojunction nanostructures were comprehensively characterized to assess their physicochemical properties relevant to photocatalysis. Structural and morphological analyses using X-ray diffraction (XRD), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDAX), and Brunauer–Emmett–Teller (BET) surface area measurements confirmed the formation of well-crystallized materials. XRD results revealed an average crystallite size of approximately 30 nm. UV–Vis diffuse reflectance spectroscopy (UV-DRS) demonstrated a narrowed optical band gap of 2.69 eV for the S2-TiO2/NiTiO3 composite, indicative of enhanced visible-light absorption. SEM imaging showed a sheet-like NiTiO3 framework decorated with spherical TiO2 nanoparticles ranging from 20 to 50 nm. The S2−TiO2/NiTiO3 sample exhibited a significantly higher surface area (65.215 m2/g) compared to S1-TiO2, correlating with its superior photocatalytic performance. Photocatalytic experiments conducted under natural sunlight using Eriochrome Black T (EBT) and Methyl Red (MR) as model azo dyes confirmed the enhanced activity of the S2-TiO2/NiTiO3 heterostructure. These results establish the S2-TiO2/NiTiO3 composite as a highly effective and environmentally sustainable photocatalyst for the remediation of dye-contaminated water.

Conventional scanning electron microscopy (SEM) readily shows cracks, particles, defects, and surface morphology but does not provide definitive composition signatures without additional methods or sectioning. This paper presents Extrudate Fracture Subsurface SEM (EFS-SEM), a rapid, non-sectioning workflow that generates impact-fractured particles from twin-screw-extruded PP/PVC nanocomposites to expose fresh subsurface faces for direct imaging. EFS augments mechanical fractography by revealing composition-linked particle signatures at 200× magnification and internal structural formation at 1000× magnification that standard test-mode surfaces often miss. The approach reduces preparation time, preserves native fracture textures, and enables building a reference library of particle-shape and interfacial signatures to support material identification, validation, and confirmation alongside complementary spectroscopic and compositional techniques. While Fourier-transform infrared spectroscopy (FTIR) confirms polymer functional groups and general filler presence without clearly differentiating carbon black and graphene, and energy‑dispersive X‑ray spectroscopy (EDS) detects elemental composition mainly confirming increased carbon content from fillers but not specific filler type, EFS-SEM provides direct morphological and interfacial contrast at the microscale, uniquely distinguishing filler shape and dispersion. Applied across PP/PVC ratios (60/40, 50/50, 40/60), EFS-SEM provides a high-throughput pathway to map subsurface morphology and composition to processing history and performance, addressing a key gap in conventional SEM-based analyses.

Reliable condition monitoring of rolling element bearings is essential for improving machinery availability and reducing unplanned downtime. Machine learning (ML) techniques have been widely applied for vibration-based bearing fault diagnosis; however, reported performance is often difficult to interpret due to variations in datasets, operating conditions, and validation protocols. This study presents a controlled experimental benchmark evaluating three classical ML algorithms—Support Vector Machine (SVM), k-Nearest Neighbors (k-NN), and Artificial Neural Network (ANN)—for vibration-based fault classification of cylindrical roller bearings. Experiments were conducted on SKF N204 bearings using a machinery fault simulator under three bearing conditions (healthy, outer race defect, and roller defect) at four rotational speeds (100–400 RPM). Artificial defects were introduced using electrical discharge machining (EDM), and vibration signals were acquired using a National Instruments data acquisition system. Root mean square (RMS), kurtosis, and crest factor were extracted as interpretable statistical features, resulting in a dataset of 119 feature samples. Model performance was evaluated using stratified train–validation–test splits, with k-fold cross-validation applied uniformly across all classifiers, including the ANN, to ensure fair comparison. Under the present laboratory conditions, the ANN exhibited the most consistent performance across validation and testing, while SVM and k-NN showed slightly lower validation accuracy but comparable testing behavior. The results demonstrate that high classification accuracy can be achieved using simple statistical features and classical ML models in a controlled setting. However, the findings should be interpreted as upper-bound diagnostic performance under laboratory conditions, rather than direct indicators of industrial-scale generalization. The study provides a transparent benchmarking reference and highlights the trade-off between interpretability, data efficiency, and classification performance in bearing health monitoring applications.

The rapid rise in electronic waste (e-waste) necessitates sustainable energy and waste valorization strategies. This study presents an e-waste-based triboelectric nanogenerator (EW-TENG) using upcycled components, aluminium electrolytic capacitors, Metallized Polypropylene Self-Healing (MPP-SH) capacitors, and lithium/zinc-ion batteries. Classified as film or powder-based, these materials were integrated with polyvinyl alcohol (PVA) to form composite films, serving as tribopositive layers against polyvinylidene fluoride (PVDF) in a vertical contactseparation mode. Aluminium (Al) foil and recycled polyethylene terephthalate (PET) were used as electrodes and substrate, respectively. The optimized EW-TENG produced an output of 274.40 V, 12.32 mu A, and a peak power of 144.30 mW at 130 M Omega. It successfully powered 70 LEDs and a digital wristwatch, and also operated as a liquid-level sensor via a floating electrode mechanism. This multifunctional device offers a sustainable, low-cost solution for energy harvesting and sensing, highlighting the potential of e-waste in powering household, industrial, and agricultural applications.

With demand for sustainable and next-generation energy technologies, piezoelectric nanogenerators have attracted increasing attention. This study aims to synthesize dual-phase Cs2ZnCl4@Cs3ZnCl5 perovskite nanoparticles (CZC NPs) and incorporate them into Polyvinylidene fluoride-co-hexafluoropropylene (PVDF-HFP) polymer by varying their concentration. The crystallinity and morphology of CZC NPs are examined using an X-ray Diffractometer (XRD) and Transmission Electron Microscope (TEM), respectively. The successful incorporation of CZC NPs into PVDF-HFP polymer matrix is confirmed using Scanning Electron Microscopy (SEM) and XRD, and the interaction between them is verified by Fourier Transform Infrared Spectroscopy (FTIR). The melt properties, topography of CZC/PVDF-HFP are studied using Differential Scanning Calorimetry (DSC), Atomic Force Microscopy (AFM). The piezoelectric response and d33 values are verified using PFM. The incorporation of CZC within PVDF-HFP shows an increase in beta-phase fraction and d33 values. The UV-vis spectroscopy shows the polymer nanocomposite's ability to trap UV-C radiation. The nanocomposites are fabricated as a PENG device, and their electrical output performance has shown remarkable results that can be used to harvest mechanical energy from human body movement. This work presents a new class of materials, designed for both sensitivity and piezoelectric properties, along with UV-C blocking applications.