Flexible and Wearable Piezoelectric Nanogenerators

Sengupta, Debarun; Kottapalli, Ajay Giri Prakash

doi:10.1007/978-3-030-05554-7_2

Cited by 4 publications

(2 citation statements)

References 70 publications

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“…However, few studies in the literature have considered energy prediction for wearable devices. Therefore, based on the architectural resemblance of wearable devices to EH-WS nodes, the energy prediction methods proposed for the latter [59][60][61][62] have been investigated in this study to determine their effectiveness for wearable devices. From this deduction, the energy prediction methods for EH sources that are commonly used by wearable devices would be only considered.…”

Section: Review Of Energy Prediction Methodsmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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Self-powered or autonomously driven wearable devices are touted to revolutionize the personalized healthcare industry, promising sustainable medical care for a large population of healthcare seekers. Current wearable devices rely on batteries for providing the necessary energy to the various electronic components. However, to ensure continuous and uninterrupted operation, these wearable devices need to scavenge energy from their surroundings. Different energy sources have been used to power wearable devices. These include predictable energy sources such as solar energy and radio frequency, as well as unpredictable energy from the human body. Nevertheless, these energy sources are either intermittent or deliver low power densities. Therefore, being able to predict or forecast the amount of harvestable energy over time enables the wearable to intelligently manage and plan its own energy resources more effectively. Several prediction approaches have been proposed in the context of energy harvesting wireless sensor network (EH-WSN) nodes. In their architectural design, these nodes are very similar to self-powered wearable devices. However, additional factors need to be considered to ensure a deeper market penetration of truly autonomous wearables for healthcare applications, which include low-cost, low-power, small-size, high-performance and lightweight. In this paper, we review the energy prediction approaches that were originally proposed for EH-WSN nodes and critique their application in wearable healthcare devices. Our comparison is based on their prediction accuracy, memory requirement, and execution time. We conclude that statistical techniques are better designed to meet the needs of short-term predictions, while long-term predictions require the hybridization of several linear and non-linear machine learning techniques. In addition to the recommendations, we discuss the challenges and future perspectives of these technique in our review.

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Section: Review Of Energy Prediction Methodsmentioning

confidence: 99%

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

2020

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“…Maschmann et al [24] used glass fibers as artificial hairs and grew carbon nanotubes (CNTs) between two electrodes that compressed the CNTs when the glass fibers were disturbed to change the resistance between the two electrodes. Piezoresistive and capacitive sensors require an external power supply during use, piezoelectric sensors offer fast responses and have self-powered characteristics [25,26]. Piezoelectric materials are mainly divided into three groups: inorganic, organic, and composite.…”

Section: Introductionmentioning

confidence: 99%

Electrohydrodynamic jet printed bioinspired piezoelectric hair-like sensor for high-sensitivity air-flow detection

Wang,

Li,

et al. 2023

Smart Mater. Struct.

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Inspired by the highly sensitive hairs of creatures in nature, this work proposes a high-aspect-ratio piezoelectric sensor that is processed using electrohydrodynamic jet printing technology. A thin layer of piezoelectric material is directly written on the surface of tungsten steel microwire by electrohydrodynamic jet printing. The piezoelectric thin-film was crystallized by high-temperature annealing and be bonded fully to the microwire. The annealed piezoelectric thin-film was then sputtered with copper electrodes which forms a piezoelectric hair-like sensor with a diameter of 120 μm and an aspect ratio of 300. It was observed that the piezoelectric hair-like sensor generates 18 mV with a displacement bend of 100 μm. The output signal increases linearly with the increase of lateral displacement and velocity. It was examined that the piezoelectric hair-like sensor can detect airflow velocity as low as 0.01 m/s, which shows a lower detection threshold compared to piezoelectric hair-like airflow sensors processed in other ways. In addition, the detection of an airflow velocity of 10 m/s and a response time is 70 ms is obtained using the piezoelectric hair-like sensor, which exhibits large detection range and fast response. The experimental results show that the piezoelectric hair-like sensor has high sensitivity, large detection range and rapid detection response, which is expected to be a high-performance sensor for biosensing and airflow monitoring.

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Polymer Electrolytes as Energy‐Harvesting Materials to Capture Electrical Energy from Dynamic Mechanical Deformations

Cao

Kyu

2021

Macromol. Rapid Commun.

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Ionic electroactive polymers (iEAPs) can generate electrical energy under bending deformations exhibiting great potential for fabricating energy harvesters from dynamic vibrating environments. According to a previous study, this flexoelectric energy-harvesting potential is explored in polymer electrolyte membrane (PEM) assemblies subjected to intermittent square wave bending modes. The above study reveals that the mechanoelectrical transduction is likely to be the consequence of ion polarization under a pressure gradient across the PEM thickness. To further evaluate the applicability of the PEM assemblies for harvesting energy from dynamic environments, oscillatory bending deformation is applied in the present study, whereby the complex flexoelectric coefficient corresponding to dynamic capacitance exhibits strong frequency dependence. At very high oscillatory bending frequencies, the ionic clouds inside the PEM assemblies cannot be fully polarized, and thus the corresponding energy output tends to become smaller. However, the PEM assemblies having higher ionic conductivities can enhance energy output at high frequencies. Of particular interest is that the incorporated ionic liquid (IL) is not only capable of effectively plasticizing the polymer network, but also expediting the ionic conductivity, thereby enhancing the electrical energy output, which in turn provides important design guidance for efficient polymer energy harvesters.

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Flexible and Wearable Piezoelectric Nanogenerators

Cited by 4 publications

References 70 publications

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Prediction of Harvestable Energy for Self-Powered Wearable Healthcare Devices: Filling a Gap

Electrohydrodynamic jet printed bioinspired piezoelectric hair-like sensor for high-sensitivity air-flow detection

Polymer Electrolytes as Energy‐Harvesting Materials to Capture Electrical Energy from Dynamic Mechanical Deformations

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