Ferroelectrets: from material science to energy harvesting and sensor applications

The purpose of this thesis is to develop innovative ferroelectrets that can be used in energy harvesting devices as well as mechanical sensors. In the first stage, the focus lies on the application of ferroelectrets as energy harvesters. The inability to control the environment where the energy harvesters will be applied, requires the use of materials that can be utilized in harsh environment such as high temperature or humidity. Therefore, new ferroelectrets based on polymers with excellent electret properties, such as fluoroethylene propylene (FEP) are developed. Two types of ferroelectrets are considered, one optimized for the longitidunal piezoelectric effect and the other one optimized for the transverse piezoelectric effect in these materials. Hereby, new void structures are achieved through thermally fusing such films so that parallel tunnels (parallel-tunnel ferroelectrets) are formed between them, or by fusing round-section FEP tubes together so that they form a band or membrane. The FEP tube configuration is optimized based on a finite element model showing that implementing a single tube structure (25 mm × 1.5 mm) as the energy harvester exhibits the largest output power. By building the energy harvester and modeling it analytically, it is demonstrated that the generated power is highly dependent on parameters such as wall thickness, load resistance, and seismic mass. Utilizing a seismic mass of 80 g at resonance frequencies around 80 Hz and an input acceleration of 1 g (9.81 m s−2), output powers up to 300 μW are reached for a transducer with 25 μm thick walls. The parallel-tunnel ferroelectrets (40 mm × 10 mm) are characterized and used in an energy harvester device based on the transverse piezoelectric effect. The energy harvesting device is an air-spaced cantilever arrangement produced by additive manufacturing technique (3D-printing). The device is tested by exposing it to sinusoidal vibrations with an acceleration a, generated by a shaker. By placing the ferroelectret at a defined distance from the neutral axis of the cantilever beam and using a proper pre-stress of the ferroelectret, an output power exceeding 1000 μW at the resonance frequency of approximately 35 Hz is reached. This demonstrates a significant improvement of air-spaced vibrational energy harvesting with ferroelectrets and greatly exceeds previous performance data for ferroelectret energy harvester of maximal 230 μW. In the second stage of the dissertation, the focus is shifted to develop ferroelectrets for chosen applications such as force myography, ultrasonic transducer and smart insole. Hereby, new arrangements and manufacturing methods are investigated to build the ferroelectret sensors. Furthermore, and following the recent requirements of eco-friendlier sensors, ferroelectrets based on polylactic acid (PLA) are investigated. PLA is a biodegradable and bioabsorbable material derived from renewable plant sources, such as corn or potato starch, tapioca roots, and sugar canes. This work relays a promising new technique in the fabrication of ferroelectrets. The novel structure is achieved through sandwiching a 3D-printed grid of periodically spaced thermoplastic polyurethane (TPU) spacers and air channels between two 12.5 μm-thick FEP films. Due to the ultra-soft TPU sections, very high quasistatic (22.000 pC N−1) and dynamic (7500 pC N−1) d33-coefficients are achieved. The isothermal stability of the d33-coefficients showed a strong dependence on poling temperature. Furthermore, the thermally stimulated discharge currents revealed well-known instability of positive charge carriers in FEP, thereby offering the possibility of stabilization by high-temperature poling. A similar approach is taken by replacing the environmentally harmful FEP by PLA. Large piezoelectric d33-coefficients of up to 2850 pC N−1 are recorded directly after charging and stabilized at about 1500 pC N−1 after approximately 50 days under ambient environmental conditions. These ferroelectrets when used for force myography to detect the slightest muscle movement when moving a finger, resulted in signal shapes and magnitudes that can be clearly distinguished from each other using simple machine learning algorithms known as Support Vector Machine (SVM) with a classification accuracy of 89.5%. Following the new manufacturing route using 3D-printing, an insole is printed using pure polypropylene filament and consists of eight independent sensors, each with a piezoelectric d33 coefficient of approximately 2000 pC N−1. The active part of the insole is protected using a 3D-printed PLA cover that features eight defined embossments on the bottom part, which focus the force on the sensors and act as overload protection against excessive stress. In addition to determining the gait pattern, an accelerometer is implemented to measure kinematic parameters and validate the sensor output signals. The combination of the high sensitivity of the sensors and the kinematic movement of the foot, opens new perspectives regarding diagnosis possibilities through gait analysis. By 3D-printing a PLA backplate and using it in combination with a bulk PLA film, a new possibility to build ultrasonic transducers is presented. The ultrasonic transducer consists of three main components all made from PLA: the film presenting the vibrating plate, the printed backplate with well-defined groves, and the printed holder. The PLA film and the printed backplate build together the ferroelectret with artificial air voids. The printed holder clamps the film on the backplate and fixes the ferroelectret together. The resulting sound pressure is measured with a calibrated microphone (Type 4138, Bruel & Kjaer) at a distance of 30 cm. The biodegradable ultrasonic transducer exhibits a large bandwidth of approximately 45 kHz and fractional bandwidth of 70%. The resulting sound pressure at the resonance frequency can be increased from 98 dB up to 106 dB for driving voltages from 30 to 70 V. respectively. The obtained theoretical and experimental results are an excellent base for further optimizing ferroelectrets to be accepted in the field of energy harvesting and mechanical sensors, where flexibility and high sensitivity are mandatory for the applications

Wearable-based human activity recognition: from a healthcare application to a kinetic energy harvesting approach

Wearable technology is changing society by becoming an essential component of daily life. Human activity recognition (HAR) is one of the most prominent research areas where wearable devices play a key role. The first major contribution to the field in this dissertation is a smart physical work- load tracking system that combines wearable-based HAR and heart rate tracking. The proposed system employs a concept from ergonomics, the Frimat’s method, to compute the physical workload from heart rate measurements within a specified time window. This dissertation includes a case of study where tracking of an individual over the course of 20 days corroborates the ability of the system to assess adaptation to an exercise routine. The second and third contributions of this dissertation point to KEH in wearable environments. The second contribution is an energy logger for wrist-worn systems, with the purpose of tracking energy generation in KEH systems during daily activities. Thus, it is possible to determine if the harvested energy is enough to power a conventional wearable device. The proposed system computes the harvested energy using the characteristics of the objective load, which in this case is a battery charger. I carried out experiments with multiple subjects to examine the generation capabilities of a commercial harvester under the conditions of human motion. This study provides insights of the performance and limitations of kinetic harvesters as battery chargers. The third contribution is a KEH-based HAR system using deep learning, data augmentation and transfer learning to outperform existing classification approaches in the KEH domain. The proposed architecture comprises convolutional neural networks (CNN) and long short-term memory networks (LSTM), which has been demonstrated to outperform other architectures found in the literature. Since deep learning classifiers require large amounts of data, and KEH datasets are limited in size, this thesis also includes the proposal of three data augmentation methods to synthesize KEH signals simulating new users. Finally, transfer learning is employed to build a system that maintains performance independent of device location or the subject wearing the device.DoctoradoDoctor en Ingeniería Eléctrica y Electrónic

Proceedings of the Conference on Progress in Electrically Active Implants - Tissue and Functional Regeneration (ELAINE 2020)

The conference on Progress in Electrically Active Implants - Tissue and Functional Regeneration (ELAINE 2020) focused on novel methods in the electric stimulation of bio-material compounds of living cells and implantable electric stimulation devices. ELAINE 2020 provided international scientists a virtual platform to discuss the latest achievements in the form of invited presentations, selected talks from abstract submissions, and virtual poster sessions. In addition, we particularly invited critical reviews and contributions with negative results or unsuccessful replications to foster the scientific discussion and explicitly encourage young scientists to contribute and submit their work

Additive Manufacturing of Piezoelectric Photopolymer Ferroelectrets

Electroactive polymers are an emerging branch of smart materials that possess the capability to change shape in the presence of an electric field. Particularly, ferroelectrets are films of electrostatically charged dielectric polymer foam that comprise a growing portion of EAP literature. Ferroelectrets are characterized by a piezoelectric response and a quasi-permanent charge polarization owing to the dipoles created when the air cavities in the polymer matrix undergo dielectric breakdown. However, these cavities are often irregular in shape, size, and distribution when produced with conventional preparation methods. To overcome this, we employ a light-based additive manufacturing technique to create a hybrid photopolymer ferroelectret structure with regular and repeatable voids. Furthermore, we developed a high-voltage electrostatic poling apparatus that allows a precise and uniform charging of the engineered ferroelectrets. The stability and uniformity of the charge polarization and the piezoelectric performance of the hybrid photopolymer ferroelectrets are characterized to evaluate the suitability of this material in energy harvesting applications