Photo-Assisted Energy Harvesters and Sensors Using PVDF Matrix-Stabilized α-FAPbI ₃ Perovskite with AI/ML-Based Human Kinematic Detection

Recent advancements in energy-harvesting technologies have led to the development of systems that efficiently convert mechanical energy into electrical power, offering sustainable solutions for a wide range of applications. In particular, piezoelectric materials are paving the way for high-performance, flexible devices capable of energy conversion, storage, and sensing, addressing the growing demand for self-powered and eco-friendly systems. In this study, we present a simple approach to stabilize α-FAPbI3 by incorporating it into a PVDF matrix, resulting in a stable composite film. Optimized FPC 3 composite containing 300 μL of α-FAPbI3/g in PVDF achieved high electroactive-phase formation (∼96%). A comparative study demonstrated that the black phase (α-FAPbI3) outperformed the yellow phase (δ-FAPbI3) due to superior interfacial interactions. The optimized device, FPNG 3, generated a peak-to-peak output voltage of ∼148.4 V and a maximum power density of ∼81.72 μW/cm2. Additionally, the device exhibited high pressure sensitivity (∼1.036 kPa/V at 10-Hz frequency and a moderate pressure range) with the ability to sense various biomechanical movements. The light-assisted nanogenerator device displayed output variations under no light, white light, and UV light exposure, making it suitable for photoassisted energy harvesting. A ∼63.5% increase in output voltage was observed from the photoassisted energy-harvesting device under white light. Overall, this research highlights the multifunctionality of the FAPbI3–PVDF composite in energy conversion, storage, and sensing, along with an effective method for stabilizing the metastable, photoactive α-FAPbI3. Additionally, a machine learning framework utilizing LSTM networks was employed for real-time gesture recognition and classification of the energy-harvesting behavior of the FPNG device. This approach enables real-time performance optimization, enhancing applications in sign language recognition, human–computer interaction, and sensor systems.