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.
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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.
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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.
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TU Delft, in collaboration with Gravity Energy BV, has conducted a feasibility study on harvesting electric energy from wind and vibrations using a wobbling triboelectric nanogenerator (WTENG). Unlike conventional wind turbines, the WTENG converts wind/vibration energy into contact-separation events through a wobbling structure and unbalanced mass. Initial experimental findings demonstrated a peak power density of 1.6 W/m² under optimal conditions. Additionally, the harvester successfully charged a 3.7V lithium-ion battery with over 4.5 μA, illustrated in a self-powered light mast as a practical demonstration in collaboration with TimberLAB. This project aims to advance this research by developing a functioning prototype for public spaces, particularly lanterns, in partnership with TimberLAB and Gravity Energy. The study will explore the potential of triboelectric nanogenerators (TENG) and piezoelectric materials to optimize energy harvesting efficiency and power output. Specifically, the project will focus on improving the WTENG's output power for practical applications by optimizing parameters such as electrode dimensions and contact-separation quality. It will also explore cost-effective, commercially available materials and best fabrication/assembly strategies to simplify scalability for different length scales and power outputs. The research will proceed with the following steps: Design and Prototype Development: Create a prototype WTENG to evaluate energy harvesting efficiency and the quantity of energy harvested. A hybrid of TENG and piezoelectric materials will be designed and assessed. Optimization: Refine the system's design by considering the scaling effect and combinations of TENG-piezoelectric materials, focusing on maximizing energy efficiency (power output). This includes exploring size effects and optimal dimensions. Real-World Application Demonstration: Assess the optimized system's potential to power lanterns in close collaboration with TimberLAB, DVC Groep BV and Gravity Energy. Identify key parameters affecting the efficiency of WTENG technology and propose a roadmap for its exploitation in other applications such as public space lighting and charging.
