Institute of Aeronautical Engineering

Lead-free composite ceramics were created using the traditional solid-state method and thoroughly examined to clarify the relationship between charge-transport mechanisms, dielectric relaxation, and microstructure. Tetragonal perovskite BaTi0.9Zr0.1O3 and cubic spinel Li0.5Fe2.5O4 phases were shown to coexist by X-ray diffraction without any discernible secondary impurities, whereas FESEM showed progressive grain refinement as the ferrite content increased. Interfacial polarization was strengthened by the decrease in grain size as well as the rise in microstrain and defect density. The real and imaginary dielectric permittivities showed a strong temperature dependency controlled by Maxwell–Wagner interfacial polarization, as well as strong frequency dispersion. Thermally activated, non-Debye relaxation indicated by electric modulus analysis had a dispersion of relaxation durations; time–temperature superposition was confirmed by normalized M″ spectra collapsing onto a master curve. AC conductivity changed from small-polaron hopping to correlated barrier hopping conduction as a function of temperature, according to Jonscher’s universal power law. ZBLF3 had the lowest activation energy and the fastest relaxation, according to Arrhenius analysis, which produced activation energies of 0.41–0.62 eV. ZBLF3 is the composition that best balances high dielectric permittivity, regulated loss, quick relaxation, and improved AC conductivity among those examined. For lead-free ferroelectric ferrite composites used in tunable capacitors and multipurpose electronic devices that operate throughout a broad frequency and temperature range, the results offer precise design directives.

The traditional solid-state reaction method was used to prepare ferroelectric–magnetic composite ceramics based on ferroelectric BaTiO3 (BT) and magnetic lithium ferrite (Li0.5Fe2.5O4 LF) in order to investigate structure–property–composition correlations. X-ray diffraction and Rietveld refinement verified phase-pure (1–x) BaTiO3 + (x) Li0.5Fe2.5O4 (BT + LF) composites with coexisting tetragonal perovskite BT and cubic spinel LF phases, showing the lack of secondary phases or solid-solution formation. Because of efficient grain boundary pinning, microstructural investigation showed consistent grain size refinement with increasing LF concentration, resulting in increased interfacial area. For every composition, ferroelectric experiments revealed clearly defined hysteresis loops. Ferrite dilution and interfacial strain effects were responsible for the progressive decrease in polarization. The 80BT + 20LF composition showed excellent saturation magnetization and decreased coercivity, and magnetic investigations verified soft ferrimagnetic behavior. Temperature-dependent magnetization studies demonstrated the importance of interfacial disorder and magnetic dilution at larger LF concentrations and showed composition-dependent Curie temperatures. The combined results show that tunable ferroelectric and magnetic responses are made possible by the regulated inclusion of LF into BT, with 80BT + 20LF providing an ideal balance for strain-mediated magnetoelectric and multifunctional device applications.

Water pollution caused by toxic dyes, heavy metals, and microorganisms has become a serious environmental problem, creating an urgent need for effective wastewater treatment materials. This study presents a sustainable synthesis of a multifunctional CeO2@N-doped carbon (CeO2@NC) hybrid nanocomposite utilizing Anacardium occidentale (cashew) juice as a reducing agent and cashew nut shell waste as a nitrogen-rich carbon precursor. X-Ray Diffraction (XRD) investigation verified the cubic fluorite structure of CeO2 with a crystallite size of around 28 nm, which diminished to around 21 nm in the CeO2@NC composite as a result of carbon matrix interaction. The structural analysis validated the effective incorporation of nitrogen-functional groups (pyridinic-N, graphitic-N) and Ce–O bonding. Morphological analysis demonstrated evenly disseminated CeO2 nanoparticles (8–15 nm) anchored on porous NC sheets. BET analysis indicated a surface area increase from 38.5 m²/g (CeO2) to 112.4 m2/g (CeO2@NC) and a narrow mesopore distribution ( 3.6 nm). UV–Vis DRS analysis demonstrated an increased light absorption accompanied by a reduced band gap, decreasing from 3.61 eV (CeO2) to 2.99 eV (CeO2@NC). The CeO2@NC composite demonstrated exceptional photocatalytic degradation of Malachite Green (94

Wind energy is a promising renewable resource, but its variable nature makes accurate wind speed prediction challenging. This study evaluated five Machine Learning (ML) techniques: Multiple Linear Regression, Support Vector Machines, Random Forests, Stochastic Gradient Boosting (SGBoost), and Artificial Neural Networks (ANNs) to forecast hourly wind speeds at two locations in Hyderabad, India (HL1—17.4° N, 78.5° E, elevation 495 m; HL2—17.4° N, 78.4° E, elevation 542 m). In parallel, a long-term wind resource assessment (2014–2023) showed that HL1 and HL2 exhibit mean wind speeds of 5.10 and 5.06 m/s, with Weibull scale factors of 5.75 and 5.71 m/s and corresponding wind power densities of 137.4 and 131.8 W/m², respectively, reflecting promising wind conditions for regional renewable-energy development. To enhance predictive accuracy, key meteorological parameters, such as surface air temperature, mean sea level pressure, soil temperature, long-wave radiation, surface pressure, skin temperature, cloud cover, and soil moisture, were incorporated. Model performance was assessed using Root Mean Square Error (RMSE), Correlation Coefficient (CC), Mean Absolute Error, Index of Agreement (IOA), BIAS, and Standard Deviation. Results indicated that all models provided statistically reliable predictions; however, ANN consistently outperformed the others (RMSE = 0.7, CC = 0.96, IOA = 0.98), with bias tightly constrained between − 1.5 and + 1.5 m/s, compared to the wider variability of alternative approaches. Seasonal analysis further confirmed ANN’s robustness across all periods, while SGBoost delivered complementary performance during the monsoon (JJAS) season. Overall, these findings demonstrated the potential of ML techniques, particularly ANNs, for accurate wind speed forecasting, providing critical insights for renewable energy planning and regional wind power development.

Bioethanol as renewable energy is receiving more attention due to increasing demand for sustainable energy on a global scale. This study presents an innovative optimization strategy that makes use of biomass resources to plan and design a sustainable supply chain for the production of bioethanol. The increasing interest in sustainability creates difficulties for decision makers (DMs) in selecting sustainable vehicles. It introduces an advanced cash credit-based mixed-integer nonlinear programming model to optimize bioethanol supply chain design, balancing cost efficiency with sustainability. The approach minimizes overall costs while ensuring employment generation and reduced green-house gas emissions, making the bioethanol supply chain both economically and environmentally sustainable. The problem is extended under type-2 intuitionistic fuzzy sets under carbon cap, tax, reward policies with proper sustainable vehicle selection. Next, a triangular intuitionistic type-2 fuzzy (TrIT2F)-analytic hierarchy process (AHP) is chosen to evaluate the weight of sustainability parameters. Thereafter, a TrIT2F-data envelopment analysis (DEA) is done to evaluate the efficiency score of each vehicle type according to sustainable criteria. A new ranking function is introduced to convert the TrIT2F number to a crisp form. A novel neutrosophic-technique for order of preference by similarity to ideal solution (TOPSIS) method is incorporated to obtain a Pareto-optimal solution for the formulated model. Furthermore, we evaluate the deterministic model using an LP-metric approach; an analogy is described between the executed solutions evaluated from two methods by considering the decisions of six DMs. The proposed optimization approach is validated through a numerical experiment and enhanced with a multi-criteria decision-making method to identify the best option among six alternatives based on six DM’ preferences.