This study introduces an integrated design and optimization framework for AlCoCrFeNi-based composites reinforced with graphene nanoplatelets (GNPs), combining machine learning-driven predictive modelling with experimental validation to achieve uniform reinforcement distribution across a predefined microstructure. A comprehensive dataset was constructed incorporating ten elements (Al, Co, Cr, Fe, Ni, Cu, Mn, Ti, V, Mo) with carbon additions, where carbon specifically represents various reinforcement phases, including graphene nanoplatelets, enabling diverse carbon-based reinforcement strategies. A comparative analysis of five machine learning models was performed using six thermodynamic descriptors: valence electron concentration (VEC), atomic size difference (δ), electronegativity difference (Δχ), mixing enthalpy (ΔHmix), mixing entropy (ΔSmix) and Gibbs energy of mixing (ΔGmix) for the 750-sample dataset. Both random forest (RF) and extreme gradient boosting (XGBoost) demonstrated superior predictive accuracy (test accuracy: 0.94927; receiver-operating characteristic curve - area under the curve (ROC-AUC): 0.99446-0.99627; 10-fold cross-validation: 0.83315-0.85184), consistently identifying ΔHmix as the most critical feature influencing phase stability. X-ray diffraction confirmed dual-phase (FCC + BCC) structures in all samples, with BCC phase volume fraction increasing from 77.25
Nitrogen dioxide (NO2) is a highly reactive oxidizing gas that is considered one of the most dangerous and toxic atmospheric pollutants. Due to its significant impact on environmental and human health, strict monitoring, and control of NO2 levels are essential. In this context, metal oxide semiconductor (MOS)-based gas sensors have emerged as an effective and practical solution for detecting NO2. In this work, we report a tin oxide (SnO2) based gas sensor by incorporating gold (Au) nanoparticles. This Au–SnO2 nanocomposite-based gas sensor exhibits a high response of 107 for 20 ppm NO2 gas at an optimum temperature of 150 °C. The sensor exhibits good repeatability, and it had a fast response and recovery time of 12 s and 31 s, respectively, when exposed to 20 ppm of NO2 gas. Its response to carbon monoxide (CO), methane (CH4), Ammonia (NH3), Ethanol (C2H5OH) and Acetone (C3H6O) is significantly weaker than to NO2, showcasing the sensor’s preference for detecting NO2.
Dimensionality reduction is crucial for the effective management of high-dimensional datasets, particularly in the healthcare industry. Feature selection identifies the most relevant attributes, reducing computational overhead and ensuring robust performance, especially in resource-constrained environments. This study introduces a Kullback-Leibler Divergence (KLD)-based feature selection method for heart sound analysis to diagnose valvular heart diseases. Mel-frequency cepstral coefficients and mel spectrograms were extracted from the dataset as input features. The KLD was applied to identify the most informative features, which were subsequently validated using various classifiers. This approach resulted in an accuracy of 99% in diagnosing five distinct heart sounds, outperforming classifiers using the full feature set. The method prioritizes critical features, leading to improved performance across all evaluated classifiers. This endeavor also aims to classify heart sounds on an embedded platform, enabling the efficient analysis and accurate diagnosis of cardiovascular conditions. These findings highlight the potential of KLD-based feature selection for the real-time detection of heart valve disorders. By reducing the processing overhead while preserving classification accuracy, this approach supports the development of efficient, cost-effective edge-based tools, ultimately improving diagnostic precision and healthcare resource efficiency.
Lead zirconate titanate (PZT) is a piezoelectric material that exhibits excellent piezoelectric and ferroelectric properties. PZT microtubes are now receiving significant attention for various applications such as sensors, actuators, and energy harvesters. PZT microtubes are often synthesized using a sacrificial template, which involves complex steps to remove the parent template. In this work, we introduce an alternative approach for synthesizing PZT microtubes utilizing a bio-template. The structural characteristics of PZT microtubes are analyzed using x-ray diffraction and Raman spectroscopy, while the hollow tubular morphology is confirmed using FESEM analysis. The formation of PZT microtubes with diameters ranging from 13 to 15 µm was confirmed by FESEM images. The complete decomposition of the bio-template during the annealing process was ensured by Fourier transform infrared spectroscopy. Piezoresponse force microscopy images confirm the ferroelectric nature of the microtubes through contrast reversal under opposite bias polarities of ± 10 V, indicating polarization switching. This study demonstrates that bio-templating is a better alternative for synthesizing phase-pure PZT hollow microtubes, as it avoids the necessity of removing the parent template from the synthesized microtubes.
This study examines the spatio-temporal variation in the stable isotopic composition of oxygen (δ¹⁸O) and hydrogen (δD) in groundwater from alluvial and lateritic aquifers in Northern Kerala, India, in relation to seasonal rainfall patterns and hydrogeological settings. The isotopic composition of rainwater in the study area exhibits higher variability during the monsoon seasons, attributed to the influence of cyclonic activity and isotopic fractionation during precipitation events. Groundwater in both alluvial and lateritic aquifers of the study area exhibits distinct seasonal variations in δ¹⁸O and δD values between pre-monsoon and post-monsoon periods, reflecting a shift from evaporation-influenced conditions during the pre-monsoon to dominant meteoric recharge in the post-monsoon season. Negative isotopic separation (Δδ) values and regression characteristics indicate that Southwest Monsoon (SWM) rainfall is the primary source of groundwater recharge, with post-monsoon depletion reflecting seasonal mixing rather than dominant North East Monsoon (NEM) influence. Deuterium excess in groundwater serves as an effective proxy for recharge dynamics, and its relationship with Total Dissolved Solids (TDS) highlights the influence of infiltration rates, evaporation intensity, and aquifer permeability on groundwater recharge processes. A mass balance approach estimates that rainwater contributes 35.7
