Piezoelectric/Triboelectric Nanogenerators for Biomedical Applications

Bodily movements can be used to harvest electrical energy via nanogenerators and thereby enable self-powered healthcare devices. In this chapter, first we summarize the requirements of nanogenerators for the applications in biomedical fields. Then, the current applications of nanogenerators in the biomedical field are introduced, including self-powered sensors for monitoring body activities; pacemakers; cochlear implants; stimulators for cells, tissues, and the brain; and degradable electronics. Remaining challenges to be solved in this field and future development directions are then discussed, such as increasing output performance, further miniaturization, encapsulation, and improving stability. Finally, future outlooks for nanogenerators in healthcare electronics are reviewed

Piezoelectric Materials for Medical Applications

This chapter describes the history and development strategy of piezoelectric materials for medical applications. It covers the piezoelectric properties of materials found inside the human body including blood vessels, skin, and bones as well as how the piezoelectricity innate in those materials aids in disease treatment. It also covers piezoelectric materials and their use in medical implants by explaining how piezoelectric materials can be used as sensors and can emulate natural materials. Finally, the possibility of using piezoelectric materials to design medical equipment and how current models can be improved by further research is explored. This review is intended to provide greater understanding of how important piezoelectricity is to the medical industry by describing the challenges and opportunities regarding its future development

Ferroelectric Polymer PVDF-Based Nanogenerator

This chapter deals with the development of ferroelectric polymer polyvinylidene fluoride (PVDF)-based nanogenerators. Due to its inherent flexibility, PVDF has been studied for application in nanogenerators. We first introduce PVDF and its copolymers, and briefly discuss their properties. Then, we discuss fabrication methods, including solution casting, spin coating, template-assisted method, electrospinning, thermal drawing, and dip coating. Using these methods, a wide variety of ferroelectric polymer structures can be fabricated. In addition to the performance enhancements provided by fabrication methods, the performance of PVDF-based nanogenerators has been improved by incorporating fillers that can alter the factors affecting the performance. Next, we review energy sources that can be exploited by PVDF-based nanogenerators to harvest electricity. The abundant energy sources in the environment include sound, wind flow, and thermal fluctuation. Finally, we discuss implantable PVDF-based nanogenerators. Another advantage of PVDF is its biocompatibility, which enables implantable nanogenerators. We believe that this chapter can also be helpful to researchers who study sensors and actuators as well as nanogenerators

Reducing time to discovery : materials and molecular modeling, imaging, informatics, and integration

This work was supported by the KAIST-funded Global Singularity Research Program for 2019 and 2020. J.C.A. acknowledges support from the National Science Foundation under Grant TRIPODS + X:RES-1839234 and the Nano/Human Interfaces Presidential Initiative. S.V.K.’s effort was supported by the U.S. Department of Energy (DOE), Office of Science, Basic Energy Sciences (BES), Materials Sciences and Engineering Division and was performed at the Oak Ridge National Laboratory’s Center for Nanophase Materials Sciences (CNMS), a U.S. Department of Energy, Office of Science User Facility.Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.Peer reviewe

Cost-effective and strongly integrated fabric-based wearable piezoelectric energy harvester

Fabric-based wearable electronics are becoming more important in the fourth industrial revolution (4IR) era due to their connectivity, wearability, comfort, and durability. Conventional fabric-based wearable electronics have been demonstrated by several researchers, but still need complex methods or additional supports to be fabricated and sewed in clothing. Herein, a cost-effective, high throughput, and strongly integrated fabric-based wearable piezoelectric energy harvester (fabric-WPEH) is demonstrated. The fabric-WPEH has a heterostructure of a ferroelectric polymer, poly(vinylidene fluoride-co-trifluoroethylene) [P(VDF-TrFE)] and two conductive fabrics via simple fabrication of tape casting and hot pressing. Our fabrication process would enable the direct application of the unit device to general garments using hot pressing as graphic patches can be attached to the garments by heat press. Simulation and experimental analysis demonstrate fully bendable, compact and concave interfaces and a high piezoelectric d33 coefficient (−32.0 pC N−1) of the P(VDF-TrFE) layer. The fabric-WPEH generates piezoelectric output signals from human motions (pressing, bending) and from quantitative force test machine pressing. Furthermore, a record high interfacial adhesion strength (22 N cm−1) between the P(VDF-TrFE) layer and fabric layers has been measured by surface and interfacial cutting analysis system (SAICAS) for the first time in the field of fabric-based wearable piezoelectric electronics. © 2020 Elsevier Ltd1

Flexible 3D Electrodes of Free-Standing TiN Nanotube Arrays Grown by Atomic Layer Deposition with a Ti Interlayer as an Adhesion Promoter

Nanostructured electrodes and their flexible integrated systems have great potential for many applications, including electrochemical energy storage, electrocatalysis and solid-state memory devices, given their ability to improve faradaic reaction sites by large surface area. Although many processing techniques have been employed to fabricate nanostructured electrodes onto flexible substrates, these present limitations in terms of achieving flexible electrodes with high mechanical stability. In this study, the adhesion, mechanical properties and flexibility of TiN nanotube arrays on a Pt substrate were improved using a Ti interlayer. Highly ordered and well-aligned TiN nanotube arrays were fabricated on a Pt substrate using a template-assisted method with an anodic aluminum oxide (AAO) template and atomic layer deposition (ALD) system. We show that with the use of a Ti interlayer between the TiN nanotube arrays and Pt substrate, the TiN nanotube arrays could perfectly attach to the Pt substrate without delamination and faceted phenomena. Furthermore, the I-V curve measurements confirmed that the electric contact between the TiN nanotube arrays and substrate for use as an electrode was excellent, and its flexibility was also good for use in flexible electronic devices. Future efforts will be directed toward the fabrication of embedded electrodes in flexible plastic substrates by employing the concepts demonstrated in this study

Reducing time to discovery:materials and molecular modeling, imaging, informatics, and integration

Multiscale and multimodal imaging of material structures and properties provides solid ground on which materials theory and design can flourish. Recently, KAIST announced 10 flagship research fields, which include KAIST Materials Revolution: Materials and Molecular Modeling, Imaging, Informatics and Integration (M3I3). The M3I3 initiative aims to reduce the time for the discovery, design and development of materials based on elucidating multiscale processing-structure-property relationship and materials hierarchy, which are to be quantified and understood through a combination of machine learning and scientific insights. In this review, we begin by introducing recent progress on related initiatives around the globe, such as the Materials Genome Initiative (U.S.), Materials Informatics (U.S.), the Materials Project (U.S.), the Open Quantum Materials Database (U.S.), Materials Research by Information Integration Initiative (Japan), Novel Materials Discovery (E.U.), the NOMAD repository (E.U.), Materials Scientific Data Sharing Network (China), Vom Materials Zur Innovation (Germany), and Creative Materials Discovery (Korea), and discuss the role of multiscale materials and molecular imaging combined with machine learning in realizing the vision of M3I3. Specifically, microscopies using photons, electrons, and physical probes will be revisited with a focus on the multiscale structural hierarchy, as well as structure-property relationships. Additionally, data mining from the literature combined with machine learning will be shown to be more efficient in finding the future direction of materials structures with improved properties than the classical approach. Examples of materials for applications in energy and information will be reviewed and discussed. A case study on the development of a Ni-Co-Mn cathode materials illustrates M3I3's approach to creating libraries of multiscale structure-property-processing relationships. We end with a future outlook toward recent developments in the field of M3I3.</p