Technology and market perspective for indoor photovoltaic cells

Indoor photovoltaic cells have the potential to power the Internet of Things ecosystem, including distributed and remote sensors, actuators, and communications devices. As the power required to operate these devices continues to decrease, the type and number of nodes that can now be persistently powered by indoor photovoltaic cells are rapidly growing. This will drive significant growth in the demand for indoor photovoltaics, creating a large alternative market for existing and novel photovoltaic technologies. With the re-emergence of interest in indoor photovoltaic cells, we provide an overview of this burgeoning field focusing on the technical challenges that remain to create energy autonomous sensors at viable price points and to overcome the commercial challenges for individual photovoltaic technologies to accelerate their market adoption

Introducing perovskites to the IoT world using photovoltaic-powered ID tags

Thesis: Ph. D., Massachusetts Institute of Technology, Department of Mechanical Engineering, 2020Cataloged from student-submitted PDF of thesis.Includes bibliographical references (pages 85-97).Billions of everyday objects could benefit from being augmented with sensors and wireless data transmitters. The prospect of developing advanced battery-powered sensors and smart devices with on-board radio and computing power has been a recent research direction for the Internet of Things (IoT). IoT devices enable us to build powerful data-driven applications by acquiring rich environmental information about an object. Often these devices are powered by batteries or direct power to run the electronics and transmit the information. Battery-powered devices are expensive and require frequent battery replacements resulting in higher maintenance costs that limit their pervasive implementation. Demand for low-cost wireless connectivity presents a huge potential to use passive sensors to augment everyday objects. Passive sensors based on Radio Frequency Identification (RFID) provide an inexpensive, scalable and energy efficient way to gather environmental information.However, traditional passive tags are restricted in functionality to due to the limited RF energy available from an RFID reader. In this thesis, I show how traditional passive RFID tags can be enhanced by providing extra power with low-cost, high performance perovskite photovoltaic energy harvesters. I divide the work into three segments. First, I determine the power required for RFID tags and the current constraints on the communication range. Second, I explore perovskite photovoltaics for powering up passive tags to improve the communication range, and to provide onboard power for external sensors. I explore the tunability of perovskite photovoltaic materials to improve their indoor performance as well as create mechanically flexible energy harvesters. Third, I investigate how having additional sensors on RFID tags powered by low-cost energy harvesters can enable new IoT applications in a variety of areas. The main objectives of this thesis are: 1. Investigate passive tag power consumption with respect to different operating conditions 2. Investigate the current constraints on communication range in RFID tags and identify the limitations in real-world implementation 3. Investigate the performance and tunability of perovskite photovoltaics and their integration with the RFID tags 4. Explore industrial applications where the perovskite photovoltaic-powered tags are useful.by Sai Nithin R. Kantareddy.Ph. D.Ph.D. Massachusetts Institute of Technology, Department of Mechanical Engineerin

3D Printing of Shape Changing Polymer Structures: Design and Characterization of Materials

Additive manufacturing (AM) gives engineers unprecedented design and material freedom, providing the ability to 3D print polymer structures that can change shape. Many of these Shape Memory Polymer (SMP) structures require multi-material composites, and different programmed shapes can be achieved by designing and engineering these composites to fold and unfold at different rates. To enable SMP applications involving shape-changing geometries, it is important to have an understanding of the relationships between intermediate shapes and the initial and final designed shapes. To accomplish this, we investigated readily available 3D printable polymer materials and their thermo-mechanical characteristics to create multi-member structures. This paper demonstrates a way to generate different temporary geometric profiles on a single 3D printed shape with the same material. This paper also includes insights from thermo-mechanical analysis of the materials to help create multi-member shape-changing geometries using 3D printing.Mechanical Engineerin

Low-cost diaper wetness detection using hydrogel-based RFID tags

There is an opportunity to utilize wearable, consumer-oriented sensors for self-health monitoring and preventative medicine. Disposable diaper with built-in lowcost, disposable moisture sensors is one such product. Diaper users include infants, elderly, disabled individuals, and hospital patients. Event-based alerting can enhance care of this population by improving incontinencemanagement, preventing rashes and infections, and avoiding embarrassment. Collected data can be used to optimize change intervals, thereby reducingwaste and expense. In this article,we realize a novel disposable sensor for moisture detection leveraging the material properties of the water absorbing polymer gel common to most diapers. We demonstrate a functional UHF RFID moisture monitor based on hydrogel sensing and propose a hybrid sensor design utilizing metal and hydrogel optimized for a specific diaper geometry. The proposed sensor design achieves a 1-meter read range, a bend radius of < 20mm, is insensitive to sensor orientation relative to the reader, and is lower-cost than sensors with metallic antennas. We detail design considerations for integration with manufacturing processes and conclude by exploring possible future applications enabled by hydrogel sensing

Saving Weight with Metallic Lattice Structures: Design Challenges with a Real-World Example

Lattice structures are structurally efficient yet complex designs that enable high stiffness and reduce weight. While lattice structures are traditionally difficult to manufacture in metal with conventional fabrication processes, AM is a viable solution to manufacture such complex geometries to achieve lightweight designs. However, there is relatively little information available in the literature about designing large-scale lattice structures, particularly concerning computer-aided design tools, structural analysis, and post-processing for functional metallic components. In this study, we investigate and discuss these aspects in the context of a real-world problem for an oil and gas application. The lattice structure is designed and fabricated with IN 718 powder using an EOS M280 laser-based powder bed fusion system. A weight reduction of 42.4% is achieved while obtaining the desired mechanical performance. Results and challenges, particularly with the design workflow, are discussed along with future research directions.Mechanical Engineerin

Learning Gestures Using A Passive Data-Glove With RFID Tags

Hand gesture recognition enables non-tactile interfaces for human-machine interactions. Cameras are currently powerful tools to recognize these gestures, however, use of cameras is constrained by privacy concerns and need for welllit, line of sight implementation. In this study, we propose an alternate method to recognize gestures using a passive data-glove augmented with passive RFID tags. We envision passive tags-based gesture recognition will have applications in improving operator safety around machines, activity monitoring in factories and sign to speech recognition, etc. Low-level reader information (RSSI, Phase and Doppler frequency) can be used to capture changes to the tags in the environment, therefore generating enough information to infer gestures. In this paper, we present a technique to enable fast feature recognition using low-level reader data by correcting for inconsistencies in phase data due to frequency hopping. We experimented with four different classifiers on the low-level reader data and our Fully-Connected Neural Network (FCCN) classifier is able to learn gestures from tag-data with 98% accuracy

Technology and Market Perspective for Indoor Photovoltaic Cells

Indoor photovoltaic cells have the potential to power the Internet of Things ecosystem, including distributed and remote sensors, actuators, and communications devices. As the power required to operate these devices continues to decrease, the type and number of nodes that can now be persistently powered by indoor photovoltaic cells are rapidly growing. This will drive significant growth in the demand for indoor photovoltaics, creating a large alternative market for existing and novel photovoltaic technologies. With the re-emergence of interest in indoor photovoltaic cells, we provide an overview of this burgeoning field focusing on the technical challenges that remain to create energy autonomous sensors at viable price points and to overcome the commercial challenges for individual photovoltaic technologies to accelerate their market adoption. The Internet of Things (IoT) ecosystem promises large networks of connected devices collecting the big data upon which our medical, manufacturing, infrastructure, and energy industries will be monitored and optimized. Billions of wireless sensors are expected to be installed over the coming decade, with almost half to be located inside buildings. Currently, the use of batteries to power these devices places significant constraints on their power consumption, where the range and frequency of data transmission are curtailed to achieve sufficient battery life, and the range of applications is also limited to the ones that allow battery replacement. Additional operation and maintenance costs are also incurred by providing replacement batteries. Indoor photovoltaics has the potential to solve these hardware issues, providing greater reliability and operational lifetimes in wireless sensor networks. Persistently powering individual nodes by harvesting ambient light using small ∼cm2 photovoltaic cells is becoming possible for more and more wireless technologies and devices. Characterizing IPV cells is a growing research field with the performance of a considerable number of different PV technologies having now been measured under ambient light sources. Given the interest in commercializing different photovoltaic cells in this growing market, we discuss here the outstanding research questions that must be answered by the indoor photovoltaic community to enable self-powered, indoor-located IoT nodes. ©2019 Elsevier Inc.EU Horizon 2020 research & innovation program - Marie Skłodowska-Curie grant (agreement no. 746516)GS1-MIT AutoID labs collaborationDOE-NSF ERF for Quantum Energy and Sustainable Solar Technologies (QESST)Singapore's National Research Foundation t- Singapore MIT Alliance for Research & Technology’s “Low energy electronic systems (LEES)” IR