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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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DOE OSTI2022

Dynamic, Adaptive, Systems and Materials: Complex, Simple and Emergent Behaviors

This program has been funded by DoE/BES for twenty years. It has moved into and out of various subjects as it has developed, but it has retained its focus on complexity and complex systems. The project has evolved in the following way: Self-Assembly and Biomimetic Self-Assembly: All self-assembling systems depend upon a minimum of two types of interaction: a repulsion and an attraction. For the familiar molecular systems, attractive interactions are typically hydrogen bonds and electrostatic interactions. Repulsive interactions include steric effects, hydrophobic effects (in biological systems), and charge-charge repulsion. We have expanded this repertoire to include surface interactions, magnetic interactions, and others. I list these systems in the order in which we have explored them: i) A key emphasis in current work is in understanding how the movement of ions in a magnetic field (the Lorentz effect) interacts with catalytic systems. We have demonstrated that an acceleration in rate of reduction of CO 2 to CO can be accomplished by applying an external magnetic field. This acceleration is largely due to the application of the Lorentz effect on mass transport at the catalyst’s surface. ii) We have also extensively explored the influence of electrostatics, as exhibited in self-assembling systems, by tribocharging. iii) Another key system involves surface tension effects; examples include interactions between heavy particles floating at a liquid-air interface, and interacting by changes in surface area; interactions of bubbles and bubble rafts, behaviors of bubble trains in microfluidic networks, and behaviors of microorganisms in constraining environments. iv) This work has intentionally de-emphasized biological systems; but it does include some work on protein-ligand interactions and interactions among microorganisms. v) We have also explored applications of some of these effects, these explorations include bubble rafts as diffraction gratings, exploration of the structures that can be obtained by tribocharging and uses of these structures in exploring nucleation and melting of crystals. vi) Although not a major focus of this work, several other topics have emerged and offer opportunities for future work. These include the behavior of bubble trains and bubble rafts in microfluidic systems. A particularly interesting example is the formation of bubble trains that repeat in the alteration of large and small bubbles according to rules we do not presently understand, but are uniquely large-period oscillating systems. These systems offer a new route into understanding the instabilities of the type represented by oscillations. vii) We have also begun exploratory projects on magnetic levitation (especially to determine molecular density), and information storage (in molecules). Magnetic Levitation: Self-assembling and biomimetic systems require both attraction and repulsion. We have used electrostatics (tribocharging), interfacial free-energies (surface tension and related forces) and others. Potential uses include reconfigurable diffraction gratings and liquid lenses; exploration of mechanisms in tribocharging; tunneling in EGaIn junctions; and bubble trains (especially in micro-fluidic systems). Examples of systems representing these topics is included in the following papers: Complexity: Disks rotating at a water-air interface; Benard-Marangoni effects; Vortex-Crystals from spinning magnetic disks (Marangoni effects); EGaIn Electrode to study quantum tunneling; Self-Assembly of electrostatically-charged metallic spheres (electrets); Dynamically reconfigurable lens; Using computational designs of ligands for enzymes; Electrostatic self-assembly by tribocharging; Monodisperse bubble trains in microchannel systems; Inverted dripping faucet; Flames; Printing of micro-organisms to regenerate the “ink” of printing device; Using micro-organisms to move loads (“microoxen”); Motion of bacterial swarms near surfaces; Making monodisperse particles in microfluidic systems; Coding/decoding of information stored in droplet trains in microfluidic networks; Magnetic levitation; and Information storage. i) Tribocharging. The change in focus of this work on electrets from the fundamentals of charging to applications of these materials in studying self-assembly using electrostatic interactions. ii) Bubbles in Microchannels. The realization that systems of bubbles in microchannels represented a major opportunity to study complexity in a very tractable system, and the development of a semi-quantitative theory of this subject. iii) Flames. The growth of “flames” remains an exploratory subject for the research, although their currently relatively little active work involving it ongoing. iv) Systems with Microorganisms. The removal of work in biological systems from this project. Based on work supported in this program, we now have a significant project on the development of microfluidic tools for studying C. elegans (a nematode), but this work was not appropriate for a program focused on complexity, and we developed separate support for it. (It is, however, an example of successful seeding of a new area by BES.) The work on electrets has gone through a period in which a part of the program was the subject of a MURI; the focus of this work was to develop materials that did not charge electrostatically on friction or contact. The MURI is now over, and the work on dynamic self-assembly (supported by BES) is the major focus. “Flames” has also enjoyed synergistic support, in terms of a project supported by DARPA on flame suppression (in the absence of extinguishing agents, using acoustic and electrostatic interactions). This work was helpful in understanding some of the basics of flames, but is entirely distinct from the BES focus in complexity. A growing interest is in the Lorentz effect. The Lorentz effect is the force exerted on charged particles (electrons, ions, charged molecules) when they move through a perpendicular magnetic field. The Lorentz effect is almost ubiquitous in modern technology: examples of applications include electric motors, dynamos, cathode ray tubes, many batteries, and most systems that control electrical currents with magnetic forces. We have begun to explore the Lorentz effect in electrochemical systems and heterogeneous catalytic systems involving charged organic species and inorganic ions. This work is still at an early stage, but initial studies that Lorentz effects can be large when ions move through magnetic fields, or magnetic fields move in the presence of ions.

36 MATERIALS SCIENCE↗
DOE OSTI2025

The CanBikeCO Full Pilot: Long-Term Results and Analysis From an E-Bike Program in Colorado, USA

Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1-3 miles, and e-bikes, cars, or shared rides for trips of 3-20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Development of Analysis Methods that Integrate Numeric and Textual Equipment Reliability Data

Within the Light Water Reactor Sustainability (LWRS) program, the Risk-Informed Systems Analysis (RISA) Pathway is performing collaborative research on the development and deployment of technologies designed to assist operating nuclear power plants (NPPs) to reduce operating costs improve plant reliability and availability. One of the RISA research areas is focusing on the development of methods and tools designed to optimize plant operations (e.g., maintenance/replacement schedules, optimal maintenance postures for plant structures, systems, and components [SSCs]) in a manner that is more cost effective than current approaches and makes better use of available SSC health data. The Risk-Informed Asset Management (RIAM) project targets this research area by creating a direct bridge between component equipment reliability (ER) data and system engineer decision making regarding maintenance activity scheduling and component aging management. In this respect, one challenge that NPP system engineers are facing is that the amount of ER data being continuously generated is not only extremely large in size, but it comes in different forms: textual (e.g., condition or maintenance reports) and numeric (e.g., generated by monitoring systems). All these data elements provide them with valuable insights and information regarding: 1) the discovery of anomalous behaviors or degradation trends, 2) the identification of the possible causes behind such behaviors/trends, and 3) the prediction of their direct consequences. However, several challenges have proved to be roadblocks to this process. While some of these challenges are technical in nature (i.e., data are often distributed over several physical servers/databases), others are conceptual in nature: data elements come in different formats (e.g., numeric or textual), and measured values have different scales (e.g., vibration spectra and oil temperature). The activities performed by the RIAM project during FY23 directly tackles the need to simultaneously integrate the analysis of ER data in all its forms, numeric and textual. Note that such task has never been performed before due to the complexity of the systems under consideration but, most importantly, because of the technical challenges behind the harmonization of ER data formats and the lack of adequate computational methods to analyze them. Our approach borrows ideas and concepts from the medical field where integration of several data sources is vital to assist medical practitioners to perform correct diagnosis and indicate optimal treatments. In our view a NPP asset is equivalent to a patient in a medical context. The main difference is the complexity of a human body is a magnitude more complex when compared to typical assets commonly present in NPPs (e.g., centrifugal pumps, or motor operated valves). This simplifies our first requirement when analyzing heterogenous ER data formats: to put data into “context”. Context is here intended as the additional piece of information that is needed by ER data analysis tools to understand what these data elements are referring to, i.e., which king of knowledge they are generating. In our context, this knowledge can be translated into models that capture the form and functional architecture of assets/systems, their dependencies, and how they interact. These models actually emulate the knowledge that that NPP system engineers possess about assets and systems; this is their key of success when analyzing ER data, their challenge is ability to handle large amount of data. Here, we employ model-based system engineering (MBSE) models of systems and assets to represent and capture their architecture and functional, i.e. cause-effect, relations. Then, ER data elements are processed by identifying first of all which elements of the developed MBSE elements they are referring to. For numeric ER data this task is fairly easy since it is possible to precisely pinpoint what MBSE elements the corresponding sensor are observing (e.g., bearing temperature of a centrifugal pump). Task is much harder for textual data since the information contained in issue or maintenance reports needs to “be understood” by a computational tool. Here we called this process as “knowledge extraction”. Once again, we borrow the experience in the medical field where methods to extract knowledge from textual data have been developed in the past decade. The missing element for us is the availability of a complete dictionary of NPP related concepts (in addition to the MBSE models presented earlier) that can put “text into context”. In FY23, such dictionary has been developed along with all the computational elements required for knowledge extraction. Lastly, once numeric and textual ER data elements have been processed and “understood”, then the last step is the discovery of possible cause-effect relations among them. This is performed by observing if a logical connection through the MBSE models exists, and if the

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Freewheeling: What Six Locations, 61,000 Trips, and 242,000 Miles in Colorado Reveal about How E-Bikes Improve Mobility Options

Personal micromobility modes such as bicycles, e-bikes and scooters offer low- or zero-emission transportation alternatives to single occupancy vehicles (SOVs). However, the lack of supporting data has led to a dearth of data-driven research on the usage of personally owned e-bikes, including variations due to weather, geography and demographics. In this paper, we present an overview of the longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use rates across different demographics, trip characteristics, and geographies. The CanBikeCO program recorded travel survey data from late July 2021 to December 31, 2022, from low-income Colorado households who were provided with e-bikes for personal use by the Colorado Energy Office (CEO). This data was collected in six different communities across Colorado following the mini-pilot program that was conducted in Fall 2020. To collect data for the survey, the program used the NREL OpenPATH application, which combines passive data collection with semantic information such as trip mode and purpose labels. To the best of our knowledge, there is no prior travel survey data on personally owned e-bikes with this range and scope. This unique dataset yielded several insights. One is that commute trips among participants had nearly 17% higher shares of e-bikes than all trips combined. E-bikes were stated to most often replace cars (34% of e-bike trips) and personal micromobility (22%). Participants favored walking for trips less than 1 mile, e-bikes for trips 1-3 miles, and e-bikes, cars or shared rides for trips 3-20 miles. Seasonality accounted for a 10% decrease and subsequent recovery in e-bike mileage on a per user basis. E-bikes are also appealing across age groups, even among older individuals, and see decreased utilization similar to regular bikes or walking during winter months. We also find that e-bike use may be related to characteristics of land use and urban form, occupation and income as well as household car ownership. We conclude that, for this population, who are mainly part of low-income households, the emissions added by the use of e-bikes (in the case of replacement of non-motorized modes) are outweighed by the strong single occupancy vehicle (SOV) travel replacement. As a whole, our findings suggest a considerable potential for energy savings and emissions reductions from personal e-bike ownership.

ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

EGS Collab Experiment 2: Continuous Broadband Seismic Waveform Data

Two broadband seismometers were installed on the 4100 level and recorded for the duration of EGS Collab Experiment #2. Inspired by published data from similar instruments installed in the Aspo Hard Rock Lab, these long-period instruments aimed to measure the tilting of the drift in response to the injection of fluid into the testbed. One instrument was installed underneath the wellheads in Site A (aka the "battery" alcove) and the other was installed along the east wall of the drift, south of Site B. Due to the feet of gravel (ballast) laid along the floor of the drift, we were unable to anchor the sensors directly to the rock. As a result, the coupling of the sensors to the experiment rock volume is likely poor. In addition, there are a number of noise sources that complicate the interpretation of the data. For example, sensor BBB is installed adjacent (within 3 ft) to the rail line that runs towards the Ross shaft. Trains (motors) run along this line almost daily and produce a large signal in these data. Careful extraction of periods of interest, as well as filtering for specific signals, is necessary. The sensors are Nanometrics Trillium Compact Posthole seismometers, sensitive down to 120 seconds period. They were installed as close to the drift wall and as deep as we could manually excavate (only about 1 ft or so). The holes were leveled with sand and the sensors were placed on a paver before backfilling with sand. The hole was then covered by a bucket filled with insulation to improve the sensor's isolation from daily temperature variations, which are minor but present due to drift ventilation from the surface. Data were recorded on Nanometrics Centaur digitizers at 100 Hz. The full response information is available in the StationXML file provided here, or by querying the sensors through the IRIS DMC (see links below). These instruments were provided free of charge through the IRIS PASSCAL instrument center. The network code is XP and the station codes are BBA and BBB. The waveform data can be queried through the IRIS FDSN server using any method the user likes. One convenient option is to use the Obspy python package: https://docs.obspy.org/packages/obspy.clients.fdsn.html

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

Laboratory Instrument Software Controlled Spread Spectrum Time Domain Reflectometry for Electrical Cable Testing

This research discusses development of a software-controlled laboratory instrument based spread spectrum time domain reflectometry system (SSTDR). This constitutes one task within PNNL’s Light Water Sustainability Program (LWRS) whose mission includes advancing nondestructive examination (NDE) techniques for off-line and on-line in-situ cable condition monitoring. In 2022, PNNL evaluated SSTDR for detection and characterization of a number of cable anomalies (Glass et al. 2022). The review included comparison of SSTDR to Frequency Domain Reflectometry (FDR) techniques which have enjoyed encouraging feedback and are starting to be used in nuclear power plants for periodic cable condition monitoring of cable systems as part of the plant’s overall cable aging management program. The FDR test introduces a broad-band chirp onto the cable at the cable end then listens for any reflection from a change of impedance along the cable caused by a damaged conductor or insulation, splices, contact with moisture, or other cable anomalies. The signal is captured in the frequency domain then transformed back to the time domain using an inverse Fourier transform (IFT). Based on the velocity of propagation, the impedance response signal is plotted against distance along the cable. Peak locations along the X-axis indicate the distance along the cable where a portion of the signal has been reflected back to the instrument as a result of a cable anomaly. The FDR test is considered the gold standard of reflectometry however it does require the cable to be de-energized to perform the test. The LIVEWIRE commercial SSTDR produces a similar plot to the FDR however all processing is in the time domain. A pseudo-random noise code (PN code) is input onto the cable conductor and the instrument listens for any reflected response from cable anomalies. The SSTDR processes the signal as an autocorrelation comparing the input PN code to any reflected signal detected. The autocorrelation analysis for thermal aging, water and water ingress detection, ground fault and phase-to-phase fault detection at various locations along the cable and with the cable attached and detached from a motor load, and on both energized and un-energized conditions were performed. These results were contrasted to Frequency Domain Reflectometry (FDR) measurements of the un-energized cable. Results were encouraging but indicated more work was warranted – particularly with the SSTDR, it seemed that the insulation damage would likely be better evaluated with multiple bandwidth cable tests particularly including larger bandwidths than were possible with the current commercial instrument. The commercial instrument’s bandwidth was set at 6, 12, 24, and 48MHz but note that SSTDR and FDR definitions of bandwidth trend similarly but are not the same. The FDR response could be more broadly adjusted, and the bandwidth of 100 to 500 MHz produced the best responses. FDR responses to anomalies were clearer than SSTDR responses and indications were that a broader bandwidth SSTDR may lead to improved SSTDR detection capability. This project used a laboratory instrument based SSTDR (primarily using an Arbitrary Waveform Generator (AWG) and a digital oscilloscope plus Python in-house software) that allowed software adjustment of the SSTDR bandwidth, window functions applied to the exciting Pseudo-random Noise (PN) code plus and other aspects of the SSTDR signal processing. Hereafter, this will be referred to as the PNNL SSTDR. Evaluating specific performance of the PNNL SSTDR is left to a separate report. This report documents hardware and software development to produce the SSTDR cable test system.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Building Environmental Justice and Equity into the Development of Critical Mineral Industries

The global transition toward low-carbon energy not only means an increase in demand for clean electricity and renewable resources, but also an increase in demand for the critical minerals (CMs) and rare earth elements (REEs) that these technologies rely upon. Indeed, low-carbon energy technologies, such as those used for wind turbines and electric vehicle motors, require significantly more lithium, nickel, cobalt, manganese, graphite, and other CMs than fossil-based energy technologies. The growing demand for new forms of low- carbon energy will necessitate a proportional scale-up of CM and REE extraction and processing. Presently, the majority of extraction and processing activities for CMs and REEs are concentrated in very few countries, primarily in China and the Global South. CM and REE supply chain activities conducted in or controlled by these countries are commonly associated with widespread and well- documented human rights abuses and environmental degradation. As a result of concerns related to supply chain security, worker welfare, and the overall need for additional sources of CMs and REEs, governments worldwide have begun to explore policy pathways for the development of new supply chains. The development of new supply chains for CMs and REEs re s a multifold opportunity to accelerate the global deployment of low-carbon energy fleets, reduce global carbon emissions, build political resilience—and also to enhance policy objectives of environmental justice and equity in the clean energy transition. Fulfilling these policy objectives requires energy producers to navigate a maze of global supply chain policies designed to shape and accelerate the growth of new markets. In the United States, for instance, President Biden recently implemented fiscal and trade policies that incentivize both domestic and global reliance on U.S.-produced CMs and REEs, leveraging key relationships in Asia and Europe to ensure the accelerated buildout of the U.S. supply chain. The Biden Administration’s framework also emphasizes that new supply chains for CMs, REEs, and other low-carbon energy pathways must generate benefits for marginalized and disadvantaged communities, build energy equity, and contribute to the Biden Administration’s vision of environmental justice. present CM and REE supply chains include several stages, including mining, processing, transport, utilization, and disposal, each of which involves different environmental justice considerations and potential injustices. This study explores how technical innovation, paired with responsible community engagement and empowerment, can help inform the development of energy equity and environmental justice at all stages of new supply chains. The study highlights these opportunities on a broad scale. We also lend a specific focus to research at the University of Wyoming School of Energy Resources that aims to guide the development of CM and REE industries in communities with high economic dependence on coal and other fossil fuel industries, identifying pathways to grow a U.S. domestic supply chain by producing CMs from coal, coal by-products, and coal waste streams. These production pathways represent a potential new supply for CMs across the U.S. and elsewhere, thereby enhancing national security and accelerating the widespread deployment of low-carbon energy technologies, while also generating alternative applications for remaining coal reserves and aiding in a just transition for rural energy-producing communities.

Gerace, Selena↗
DOE OSTI2025

Fuel Property Effects on Stochastic Preignition Events During Engine Load Transitions

Stochastic preignition (SPI) is an abnormal combustion phenomenon that can cause catastrophic engine damage. There have been several proposed mechanisms of SPI, where a uniform source is still not certain, however, SPI tendencies have been shown to be influenced by engine operating conditions, oil composition, engine age, and fuel chemical and physical properties. Laboratory research and testing for SPI propensity is challenging given the stochastic nature of events, as well as the potential for significant degradation of the engine platform and measuring equipment over time. Thus, SPI specific experiments are generally conducted under either sustained or cyclic patterning of steady-state operating conditions to avoid the influence of transient engine boundary conditions on test parameters of interest (e.g. oil additive package, fuel properties, engine speed/load, etc.). In this work a cyclically varying SPI test sequence involves a 5 min engine warmup period at a low engine load of around 4 bar gross indicated mean effective pressure (IMEPg), followed by a transition to high load (~20 bar IMEPg) at a constant 2000 rev/min engine speed for a total of 25 min. This individual test sequence load schedule is then sequentially repeated 10 times to generate significant statistical data for analysis. This work examines the influence of fuel chemical and physical properties on SPI tendency during the unsteady portion of the 10-cycle sequence (the first 5 min of the high load operation in each sequence of the loading cycle) which has been discarded from previous analyses due to the uncertainty in engine operating and thermal boundary conditions. Results from this analysis suggest an increasing trend in the ratio of SPI events during the unsteady test period relative to the steady test period with increasing fuel Reid Vapor Pressure (RVP), implying differences in uncontrolled ignition source terms, possibly from, fuel wall interactions and retention during the load transition phase of the test.

Splitter, Derek [ORNL] (ORCID:0000000174044047)↗
DOE OSTI2023

Self-Powered Autonomous Sensing System for Arctic Ocean using a Frequency-multiplied Cylindrical Triboelectric Nanogenerator

An autonomous sensing system for collecting environmental observations in the Arctic region is critical for the estimation and prediction of climate change. Ocean waves are a great source of energy for these sensing systems but there has been limited research done on small-scale energy harvesting applications in the Arctic Ocean (subsea or surface). The available wave energy in the Arctic Ocean is lower than the typical ocean wave energy due to the low wave frequency, height, and operating months. Although the available wave energy depends on the specific location in the Arctic Ocean, the wave height decreases everywhere during the winter (Jan-March) [1]. Our target location in this work is the Beaufort and Chukchi Seas, which are ice covered for about 195 days per year leaving only a few months (June-November) for wave energy harvesting [2]. During these few months, the average wave frequency is 0.2 Hz and the most common wave frequency is around 0.15 Hz. Many energy harvesting methods are unsuitable for use in the Arctic Ocean because of to the cold temperature, low wave frequency, and low wave height. Triboelectric nanogenerators (TENG) are one of the few energy harvesting methods that excel in these conditions. Zhong et al. [3] designed a stacked pendulum-structured TENG for low-frequency ocean wave energy and reported the device generated a peak power density of 11.2 W/m3 under the low wave frequency of 0.2 Hz. The autonomous sensing system we developed for the Arctic Ocean, the Arctic-TENG, is based on a frequency-multiplied cylindrical TENG (FMC-TENG) which is an optimized TENG configuration for Arctic Ocean conditions due to the high power density under low-frequency wave conditions [4]. Figure 1 shows an FMC-TENG with multiple pairs of free-standing triboelectric-layer mode materials (Aluminum and FEP). The mass and magnet attached to the rotor store gravitational potential energy which is released as kinetic energy when the potential energy overcomes the repulsive magnetic force generated by the opposing magnet attached to the stator. This force unbalance triggers a sudden rotation and swinging motion of the mass which increases the angular velocity of the system and therefore enhances the output power of the TENG system. The main components of the Arctic-TENG are a 3D-printed rotor and stator, electric and dielectric material adhesive tape, and bearings. All these materials were cold-soaked and tested at -40 °C in a chest freezer. Both the 3D-printed parts and adhesives were confirmed to have a minimal effect from the cold temperature. Multiple bearings were tested and the starting torque of each one was compared both at room temperature and -40 °C. The bearing with the lowest starting torque at -40 °C was selected for use in the Arctic-TENG. The Arctic-TENG was tested using an out-of-water motor-driven wave simulator (Figure 2). The wave simulator allows for controlled testing at a wave height of 0.2 m and frequencies between 0.1 Hz to 0.5 Hz. This wave simulator was used for both room temperature and -40 °C testing. The Arctic-TENG generated significantly more power at -40 °C compared to room temperature for each frequency tested. The system stored energy in a supercapacitor via a power management circuit, and the amount of energy stored per day was calculated at different wave frequencies. Based on the conditions at the proposed deployment location (days of non-ice-covered ocean and wave frequency), the amount of stored energy per year from Arctic-TENG was calculated to be enough energy for two transmissions every day. Durability testing was completed on the Arctic-TENG at room temperature to determine the lifetime. The rotor of the Arctic-TENG was attached to a DC motor and spun continuously for several millions of cycles without degradation of the electrical output, demonstrating the feasibility for long-term operation.

Jung, Hyunjun↗