Swati Ghosh Acharyya

Polyvinylidene fluoride (PVDF) nanocomposites embedded with inorganic fillers have become leading candidates for self-powered flexible electronics. However, understanding the interaction between nanoscale piezoelectric domains and macroscopic mechanical durability remains a challenge for the practical development of sensors. This work provides a comprehensive investigation into the mechano-electrical properties of Zinc Oxide (ZnO) nanorod/PVDF flexible composite films. Structural characterization confirmed the nucleation of the electroactive β-phase, reaching a maximum of 97.3% in the 5 wt% composite. Field Emission Scanning Electron Microscopy (FESEM) confirmed the homogeneous dispersion of nanorods at this optimal loading, while cross-sectional analysis revealed a dense microstructure indicative of strong interfacial compatibility. Piezoresponse Force Microscopy (PFM) revealed that well-dispersed nanorods function as piezo-active hotspots, significantly enhancing local domain switching and amplitude compared to the pristine polymer. Furthermore, nanoindentation and tensile testing demonstrated that the nanorods serve as dual-functional reinforcing agents. The optimized composite showed a 39% increase in elastic modulus and a 56% improvement in toughness (1.72 MJ/m³) without sacrificing flexibility. The optimized nanocomposite combines enhanced local piezoelectric response with superior mechanical robustness, establishing it as a durable material system suitable for next-generation flexible electronic applications.

Accurate and effective localization of fatigue cracks is essential for Structural Health Monitoring (SHM) systems to assure safety, save maintenance costs, and prevent structural failures. Existing acoustic emission (AE)-based localization algorithms sometimes require numerous sensors and are sensitive to environmental fluctuations that might impact wave velocity and signal quality. This paper provides a robust and feasible technology for real-time crack localization that uses only two wideband AE sensors, reducing sensor infrastructure. The method uses a Random Forest Regressor (RFR) model trained on time difference of arrival to predict crack locations accurately. To improve the reliability of localization in noisy environments, a simple energy-based thresholding followed by a feedforward neural network (FNN) automatically filters out non-crack signals before localization. The models are trained and verified with data from fatigue crack propagation, Hsu-Nielsen pencil lead breaks, and corrosion-induced AE events. The proposed method achieves 99.76% localization accuracy, indicating excellent resistance to environmental noise and signal variability. Computational performance indicators demonstrate the solution’s suitability for real-time implementation in resource-constrained industrial environments. The methodology provides a cost-effective, accurate, and scalable solution for real-world AE-based SHM applications.

Corrosion is one of the most common sources of deterioration to a ship during its service years, affecting structural integrity and usability which eventually leads to failure. This necessitates a real-time structural health monitoring of ships which detects the initiation of corrosion before it becomes severe. Acoustic emission (AE), a popular non-destructive testing method, has been proved to be effective in assessing corrosion deterioration for years giving the real-time assessment benefits. Though it has real-time advantages it is threshold based corrosion detection resulting in loss of important signal features. The current study presents an approach for assessing impact of corrosion by employing only AE time domain signal. The methodology identifies real-time parameters using phase space reconstruction which are also proven to be correlating to the corrosive parameters. This is accomplished by generating phase portraits from the AE signal and correlating them to the frequency and energy of AE signal and also the corrosive parameters like current density and pitting%. In this study, DMR249A high strength low alloy (HSLA) steel used in ship structure manufacturing underwent several potentiodynamic polarisation experiments, including full scan and intermittent method, which provided valuable insights into corrosion phenomena. Field emission scanning electron microscopy (FESEM) was used to examine the material’s morphology both before and after experiments. The output of the developed methodology was correlated with FESEM images by calculating the pitting% to confirm the corrosion degradation of the material, resulting in an average correlation of 93.0160 %.

Acoustic emission (AE) has gained popularity in real-time prediction of fatigue crack growth rate (CGR). However, the past methods rely significantly on the fatigue cycle and threshold-based AE parameters such as count rate, event rate, energy rate, etc, making them less appealing for real-time applications. Therefore, we introduced a novel methodology for fatigue CGR prediction that eliminates the need for fatigue cycles and threshold-based AE parameters, making it more suitable for real-time situations. This study uses in-situ AE signals acquired during the propagation of fatigue cracks in ship steel DMR 249A. The fatigue experiments were conducted for three different load ratios of 0.1, 0.25, and 0.5 with the in-situ AE sensors. These load ratios correspond to the sea-state IV, where waves’ height reaches 1.25 m to 2.5 m, and the wind is moderate. The detected AE signals are utilised to predict the fatigue CGR by using hybrid deep learning model consist of long short term memory and convolutional neural network. Prior to prediction, raw AE data undergoes meticulous pre-processing, involving the application of B-spline basis functions and first-order derivatives. The projected CGR attain strong correlations of 79.64%, 86.87%, and 94.42% for load ratios 0.1,0.25, and 0.5, respectively, in comparison with the actual CGR values. The same is also validated through the microstructural characterisation. Subsequently, the proposed hybrid DNN model is deployed on Nvidia Jetson Nano platform. This implementation demonstrates the model’s adaptability to real-world industrial challenges, when used on edge devices, showcasing the potential of AE signals for real-time CGR prediction.

A significant health risk arises from the bioaccumulation of harmful Cd (II) in drinking water. Here, we report the unique Cd (II) remediation from drinking water by using novel GO-ZnO-curcumin composite. The composites were tailored by varying the ratio of GO-ZnO and curcumin. The composites followed Langmuir adsorption isotherm and pseudo-second-order kinetics. ZnO nano-rods were more effective in Cd (II) than ZnO nano-disks. A maximum adsorption capacity of 4580 ± 40 mg/gm was achieved for 21G-B with a removal efficiency of 87.5% at neutral pH under optimized conditions. The removal process was governed by ion exchange and electrostatic attraction, followed by cation exchange capacity (CEC). The lattice parameter increase was detected after adsorption of Cd (II) ions. The regeneration and reusability of the composite was studied. Also, the effect of presence of dyes such as methylene blue on Cd (II) adsorption was noted. The latter had negligible effect on Cd (II) removal efficiency from water. The composite showed high antibacterial activity against B. subtilis and P. aeruginosa with minimum inhibitory concentration (MIC) of 10 ± 0.75 µg/ml and 5 ± 1 µg/ml respectively due to the presence of zinc. Composite stability was confirmed through leaching and thermal gravimetric analysis (TGA) analysis. The study establishes the nanocomposite as a potential material for remediation of hazardous Cd (II) ions from real water samples under neutral conditions.

A nanocomposite of graphene oxide, zinc oxide, and curcumin (GO-ZnO-cur) was synthesized for the adsorption of toxic Cd (II) ions from water. The nanocomposites with different concentrations were characterized by FESEM, X-ray diffraction (XRD), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), and contact angle measurement. GO-ZnO-cur nanocomposites had a specific surface area of 18 m2/gm and were rich in hydroxyl and carboxyl groups. The hydrophilicity and stability over a range of pH of the composite were enhanced by optimization of curcumin concentration. The contact angle of the nanocomposite was 27.06° as compared to 37.13° for graphene oxide. Adsorption studies showed that GO-ZnO-cur nanocomposite could eliminate 90% of Cd (II) from water and had an adsorption capacity of 1568 ±10 mg/gm at neutral pH and room temperature.

Here, we report a single-step, high-yield technique for producing planar sheets of wrinkle-free multi-layer graphene (MLG) under ambient conditions by ablation of graphite powder dispersed in ethanol using a femtosecond laser. The parameters for the synthesis of MLG have been optimized and established. The number of layers and quality of MLG produced at several laser fluences (39.4 J/cm ² , 98.72 J/cm ² , 197.45 J/cm ² , 296.16 J/cm ² ) was established based on detailed characterization using field emission scanning electron microscopy (FESEM), high-resolution transmission electron microscopy (HRTEM) with selected area electron diffraction (SAED), atomic force microscopy (AFM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and Fourier transform infrared spectroscopy (FTIR). Wrinkle-free, ultra-flat crystalline MLG sheets with 2-12 layers were produced in each case with an average lateral dimension of ∼1-3 μm. The associated oxy- functional groups were characterized and quantified using FTIR and XPS. The highest percentage of sp ² C atoms and C/O ratio for MLG was achievedat the lowest laser fluence. The higher the laser fluence higher the defects produced in the MLG sheets. The technique produces zero chemical waste, has a very high yield of ∼ 95%, and is promising for bulk-scale production of MLG.