Designing lead-free piezoelectric ceramics with tailored electrical properties remains a critical challenge for various applications. In this paper we present a novel methodology integrating Machine Learning (ML) and optimization procedures to fine-tune electrical properties in lead-free (1-x) Na0.5 Bi0.5 TiO3 - x CaTiO3 piezoelectric ceramics. A comprehensive dataset of dielectric measurements serves as the foundation for training ML models that accurately predict the permittivity (𝜀′) and dielectric loss (tan 𝛿) as functions of Ca2+concentration (x % Ca), temperature and frequency. Two ML techniques are evaluated: random forest regression, and Multi-Layer Perceptron neural network Regression (MLPR). The MLPR model exhibited a superior regression performance, achieving a correlation coefficient of 0.931 and a root mean squared error of 0.029. The MLPR was then optimized by the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to maximizes 𝜀′ while minimizes tan 𝛿. Within the NSGA-II framework, the optimal values were found at the Pareto curve knee, corresponding to a frequency, temperature, and x % Ca of 609.739 kHz, 398.15 K, and 6.10, respectively, resulting in 𝜀′ equal to 857.87 and tan 𝛿 equal to 0.0120. This approach demonstrates the effectiveness of combining ML andoptimization for designing the electrical properties of piezoelectric ceramics, paving the way for more efficient and targeted material development.
Vibrational and structural properties of lead-free piezoelectric (1-x)Na0.5Bi0.5TiO3–xCaTiO3 (0 < x < 1.00) solid solutions have been investigated using Raman spectroscopy and X-ray diffraction. Different anomalies were detected and analyzed taking into consideration the phase transition from rhombohedral to orthorhombic phase at room temperature. All Raman bands were interpreted through the variation in the peak positions (frequency) and the corresponding half-widths at half maximum (HWHM) as a function of x. XRD used as a complementary technique to Raman spectroscopy, showed that the rhombohedral – orthorhombic phase transition went gradually through an intermediate phase consisting of a mixture of rhombohedral (R3c) and orthorhombic (Pnma) structures and that the fraction of orthorhombic phase increased with CT composition. The results show that the morphotropic phase boundary (MPB) is located between 0.09 and 0.15.
An energy harvesting device for obtaining energy from drops without needing of moving the drops along the device, in a reduced scale and combinable with othertypes of harvesting devices, the energy harvesting device comprising one or more triboelectric generators comprising a bottom electrode, a friction or triboelectric element placed over the bottom electrode, and at least two top electrodes placed over the triboelectric element and defining at least one gap between them, exposing the triboelectric element to the external environment so that on contacting a drop of liquid makes an electrical connection between the top electrodes varying the capacitance of the triboelectric generators and alternatively for functioning as a power unit for a sensor or as a self-powered sensor producing an electrical signal generated by the contact of the liquid with the electrodes.
