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A radio-frequency (RF) surface ion trap chip includes an RF electrode and an integrated capacitive voltage divider in which an intermediate voltage node is capacitively connected between the RF electrode and a ground. A sensor output trace is connected to the intermediate voltage node.
A radio-frequency probe system with a transmitting or receiving element integrated into a cable assembly is disclosed. In some embodiments a preferred configuration may contain one or more sensing elements integrated into the transmitting or receiving element. In another embodiment, the radio frequency probe comprises an antenna body fixed to a coaxial cable, in which the center conductor of the coaxial cable serves as the transmitting or receiving element. A method for monitoring, transmitting, or detecting one or more parameters using a single radio frequency probe is also disclosed.
Recent advancements in radio frequency (RF) sensing technology can be attributed to the development of the Internet of Things (IoT), healthcare, RF-identification, and communication applications. RF sensing is a multidisciplinary research field that requires expertise in computing, electronics, and electromagnetics to cover all system features, including protocol development, antenna design, sensor integration, algorithm formulation, interconnection, data, and analytics. The overarching aim of this work is to present detailed information about RF technologies and their innovations and application diversity from the novel work carried out at CSI Lab1, together in one platform with an extensive survey. This study presents state-of-the art applications and RF sensing that include W-Fi, radar, and SDR and RFID-based sensing. A comprehensive survey and study of the advantages and limitations of each non-contact technology is discussed. Additionally, open research gaps have been identified as well. Decades of knowledge and experience have been put to use to meet new challenges and demands. The development and study of RF systems, IoT, RFID sensing, and research and deployment activities, are briefly discussed. The emerging research projects with industry, institutional research centers, and academic studies are also addressed. Finally, an outline of identified potential future research areas is provided, emphasizing opportunities and challenges.
Abstract Many health diagnostic systems demand noninvasive sensing of respiratory rate, respiratory volume, and heart rate with high user comfort. Previous methods often require multiple sensors, including skin-touch electrodes, tension belts, or nearby off-the-body readers, and hence are uncomfortable or inconvenient. This paper presents an over-clothing wearable radio-frequency sensor study, conducted on 20 healthy participants (14 females) performing voluntary breathing exercises in various postures. Two prototype sensors were placed on the participants, one close to the heart and the other below the xiphoid process to couple to the motion from heart, lungs and diaphragm, by the near-field coherent sensing principle. We can achieve a satisfactory correlation of our sensor with the reference devices for the three vital signs: heart rate ( r = 0.95), respiratory rate ( r = 0.93) and respiratory volume ( r = 0.84). We also detected voluntary breath-hold periods with an accuracy of 96%. Further, the participants performed a breathing exercise by contracting abdomen inwards while holding breath, leading to paradoxical outward thorax motion under the isovolumetric condition, which was detected with an accuracy of 83%.
Lean gasoline engine operation provides clear efficiency benefits relative to conventional stoichiometric combustion approaches. One of the key hurdles to the widespread, practical implementation of lean gasoline combustion remains the challenge of lean NOx control. One of the potential approaches for controlling NOx emission from lean gasoline engines is the so-called passive selective catalytic reduction (SCR) system. In such systems, periods of rich operation generate ammonia over a three-way catalyst (TWC), which is then adsorbed on the downstream SCR and consumed during lean operation. Brief periods of rich operation must occur in response to the depletion of stored ammonia on the SCR, which requires reliable measurements of the SCR ammonia inventory. Presently, lean exhaust system controls rely on a variety of gas sensors mounted up- and downstream of the catalysts, and which only provide an indirect inference of the operation state.In this study, a radio frequency (RF) sensor was used to provide a direction measurement of the amount of ammonia adsorbed on the SCR in real-time. The RF sensor was calibrated and deployed on a BMW N43B20 4-cylinder lean gasoline engine equipped with a passive SCR system. Brief periods of rich operation performed at lambda values between 0.98 and 0.99 generated the ammonia, subsequently stored on the SCR for consumption during periods of lean operation. The experiments compared real-time measurements of SCR ammonia inventory from the RF sensor with estimates of ammonia coverage derived from exhaust gas composition measurements upstream and downstream of the catalyst. The results showed a high degree of correlation between the RF measurements and SCR ammonia storage inventory, and demonstrated NOx conversion efficiencies above 98%, confirming the feasibility of the concept. Relative to stoichiometric operation, lean-gasoline operation resulted in fuel efficiency gains of up to 10%, which may be further improved through direct feedback control from the RF sensor to optimize lean–rich cycling based on actual, measured SCR ammonia levels.
The advent of nanotechnology has motivated a revolution in the development of miniaturized sensors. Such sensors can be used for radiation detection, temperature sensing, radio-frequency sensing, strain sensing, and more. At the nanoscale, integrating the materials of interest into sensing platforms can be a common issue. One promising platform is photonic crystal fibers, which can draw in optically sensitive nanoparticles or have its optical properties changed by specialized nanomaterials. However, testing these sensors at scale is limited by the the need for specialized equipment to integrate these photonic crystal fibers into optical fiber systems. Having a method to enable rapid prototyping of new nanoparticle-based sensors in photonic crystal fibers would open up the field to a wider range of laboratories that could not have initially studied these materials in such a way before. This manuscript discusses the improved processes for cleaving, drawing, and rapidly integrating nanoparticle-based photonic crystal fibers into optical system setups. The method proposed in this manuscript achieved the following innovations: cleaving at a quality needed for nanoparticle integration could be done more reliably (≈100% acceptable cleaving yield versus ≈50% conventionally), nanoparticles could be drawn at scale through photonic crystal fibers in a safe manner (a method to draw multiple photonic crystal fibers at scale versus one fiber at a time), and the new photonic crystal fiber mount was able to be finely adjusted when increasing the optical coupling before inserting it into an optical system (before, expensive fusion splicing was the only other method).
Radio frequency (RF) signals are frequently used in emerging quantum applications due to their spin state manipulation capability. Efficient coupling of RF signals into a particular quantum system requires the utilization of carefully designed and fabricated antennas. Nitrogen vacancy (NV) defects in diamond are commonly utilized platforms in quantum sensing experiments with the optically detected magnetic resonance (ODMR) method, where an RF antenna is an essential element. We report on the design and fabrication of high efficiency coplanar RF antennas for quantum sensing applications. Single and double ring coplanar RF antennas were designed with -37 dB experimental return loss at 2.87 GHz, the zero-field splitting frequency of the negatively charged NV defect in diamond. The efficiency of both antennas was demonstrated in magnetic field sensing experiments with NV color centers in diamond. In conclusion, an RF amplifier was not needed, and the 0 dB output of a standard RF signal generator was adequate to run the ODMR experiments due to the high efficiency of the RF antennas.
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To detect a specific radio-frequency (rf) magnetic field, rf optically pumped magnetometers (OPMs) require a static magnetic field to set the Larmor frequency of the atoms equal to the frequency of interest. However, unshielded and variable magnetic field environments (e.g., an rf OPM on a moving platform) pose a problem for rf OPM operation. Here, we demonstrate the use of a natural-abundance rubidium vapor to make a comagnetometer to address this challenge. Our implementation builds upon the simultaneous application of several OPM techniques within the same vapor cell. First, we use a modified implementation of an OPM variometer based on 87 Rb to detect and actively cancel unwanted external fields at frequencies ≲60 Hz using active feedback to a set of field control coils. In this experiment, we exploit this stabilized field environment to implement a high-sensitivity rf magnetometer using 85 Rb. Using this approach, we demonstrate the ability to measure rf fields with a sensitivity of approximately 9 fT Hz -1/2 inside a magnetic shield in the presence of an applied field of approximately 20 μT along three mutually orthogonal directions. This demonstration opens up a path toward completely unshielded operation of a high-sensitivity rf OPM.
The synthesis of functional graphene nanostructures on Ge(001) provides an attractive route toward integrating graphene-based electronic devices onto complementary metal oxide semiconductor-compatible platforms. In this study, we leverage the phenomenon of the anisotropic growth of graphene nanoribbons from rationally placed graphene nanoseeds and their rotational self-alignment during chemical vapor deposition to synthesize mesoscale graphene nanomeshes over areas spanning several hundred square micrometers. Lithographically patterned nanoseeds are defined on a Ge(001) surface at pitches ranging from 50 to 100 nm, which serve as starting sites for subsequent nanoribbon growth. Rotational self-alignment of the nanoseeds followed by anisotropic growth kinetics causes the resulting nanoribbons to be oriented along each of the equivalent, orthogonal Ge < 110 > directions with equal probability. As the nanoribbons grow, they fuse, creating a continuous nanomesh. In contrast to nanomesh synthesis via top-down approaches, this technique yields nanomeshes with atomically faceted edges and covalently bonded junctions, which are important for maximizing charge transport properties. Additionally, we simulate the electrical characteristics of nanomeshes synthesized from different initial nanoseed-sizes, size-polydispersities, pitches, and device channel lengths to identify a parameter-space for acceptable on/off ratios and on-conductance in semiconductor electronics. The simulations show that decreasing seed diameter and pitch are critical to increasing nanomesh on/off ratio and on-conductance, respectively. With further refinements in lithography, nanomeshes obtained via seeded synthesis and anisotropic growth are likely to have superior electronic properties with tremendous potential in a multitude of applications, such as radio frequency communications, sensing, thin-film electronics, and plasmonics.
We propose a novel learning-based system, LLOCUS, enabled by crowdsourcing on mobile devices, for localizing mobile transmitters with unknown transmit power. Our approach carefully tackles several challenges in learning and localizing using a crowdsourced system with both ends’ mobility, the ‘ends’ being the transmitters and the receivers. We decouple the problem of localizing a transmitter with unknown transmit power into two problems, 1) predicting the transmit power of an unknown transmitter, and 2) localizing a transmitter with known transmit power. LLOCUS first estimates the transmit power of the unknown transmitter and then scales the reported RSS values such that the unknown transmit power problem is transparent to the method of localization. We evaluate LLOCUS using four experiments, in different indoor and outdoor environments, with the size of the areas ranging from 225 to 5500 square meters. We find that LLOCUS reduces the localization error by 17-47%, over a number of non-learning methods. LLOCUS also estimates the transmit power of an unknown transmitter with average error in the range of 3-5 dB, across our experiments.
Systems, devices, and methods related to generating and/or transmitting sensor measurement data are described. A device may include a first conductive pad positioned on a first surface of a substrate. The device may also include a second conductive pad positioned on a second, opposite surface of the substrate. Further, the device may include an inductive coil coupled between the first electrical pad and the second electrical pad. Also, the device may include a third conductive pad positioned on a third surface of the substrate and configured to couple to a sensor. The device may include a fourth conductive pad positioned on a fourth surface of the substrate and configured to couple to the sensor. The device may be configured to wirelessly transmit a signal.
Sensing electric fields with high sensitivity, high spatial resolution, and at radio frequencies can be challenging to realize. Recently, point defects in silicon carbide have shown their ability to measure local electric fields by optical conversion of their charge state. Here, we report the combination of heterodyne detection with charge-based electric field sensing, solving many of the previous limitations of this technique. Owing to the nonlinear response of the charge conversion to electric fields, the application of a separate pump electric field results in a detection sensitivity as low as 1.1(V/cm)/ Hz , with a near-diffraction limited spatial resolution and tunable control of the sensor dynamic range. In addition, we show both incoherent and coherent heterodyne detection, allowing measurements of either unknown random fields or synchronized fields with higher sensitivities. Finally, we demonstrate in-plane vector measurements of the electric field by combining orthogonal pump electric fields. Overall, this work establishes charge-based measurements as highly relevant for solid-state defect sensing.
This research is motivated by the challenges faced during biomass processing in bioenergy plants. It has been observed that variations in biomass characteristics, such as moisture, ash, and carbohydrate contents cause variations in feeding of the system which led to underutilization of equipment and the reactor. The objective of this research is to ensure a continuous flow of biomass to the reactor in plants that use the biochemical conversion process to generate liquid fuels. The overall goal is to lower the cost of producing biofuels, which could lead to improving US’s energy independency and growing US’s rural economy. The research team developed analytical models, such as discrete element method (DEM) models and mathematical models. The DEM models are unit-level models that explicitly capture biomass characteristics and quantify the impacts of biomass characteristics on bulk material properties and the performance of specific equipment. The mathematical models are system-level models that capture the impacts of system infeed rate, equipment processing rate, storage location and capacity, and biomass characteristics on system throughput. The functional relations predicting the bulk material properties from DEM models are incorporated to the mathematical models. The models developed were validated and evaluated using data collected at Idaho National Laboratory’s biomass processing facility. Via these models, we identified process control strategies that ensure a continuous flow of biomass to the reactor, while meeting the requirements of biochemical conversion process. Our analysis indicates that sequencing of biomass bales based on moisture level, and carbohydrate contents could have a positive impact on reducing processing time and inventory level and increasing throughput rate. Short bale sequences that repeat frequently, seem to have the greatest impact on improving system’s performance. Based on our experiments, the total annual system operating costs reduced by 20-30%, and the maximum inventory level reduced by 3 to 4 times. The operating costs include the annual equipment amortization cost and processing cost. The implementation of the models developed requires the use of standardized bale format, Radio Frequency Identification technology, sensing and real time monitoring of material attributes, automated material handling equipment, and automated process control. The scope of the model proposed can be extended to include the whole supply chain. The supply chain models help identify how many bales of different biomass feedstock to purchase given biomass availability in the region, biomass price and quality, and the biomass processing capabilities of the biorefinery. Thus, the outcomes of supply chain models can be used to inform the design of long-term contracts among farmers and the biorefinery.
Here, we investigate an approach for parallel high-frequency magnetic sensing based on a multi-channel radio frequency (RF) optically pumped magnetometer (OPM) coupled to multiple flux transformers (FTs) with a focus on hand magnetic resonance imaging (MRI) application at ultra-low field (ULF). Multiple RF OPM sensing channels are realized by using a single large-area alkali-metal vapor cell and two laser beams for pumping and probing, shared for all the channels. This design leads to significant cost reduction when multi-channel sensing is desirable, as in the case of ULF MRI. The FT, composed of two connected coils, serves as a transmitter of a target magnetic field to the OPM, while decoupling the OPM from untargeted magnetic fields in the sensing area that can limit the OPM performance. For hand MRI application, theoretical and numerical analysis is performed to determine an optimal geometry for the FT array that could improve signal-to-noise ratio (SNR) and sufficiently reduce crosstalk between FTs. We estimate that the optimized multi-channel FT-OPM sensor can achieve a magnetic field sensitivity of the order of 1 fT / Hz 1 / 2 above 100 kHz, which would be sufficient for 1 mm resolution MRI. In general, the multi-channel capability enables simultaneous magnetic measurements, thus reducing the sensing time and improving the SNR, and we anticipate many applications of the multi-channel FT-OPM sensor beyond the targeted here hand MRI: anatomical parallel ULF MRI of the human brain and other parts of the body, airport security screening, magnetic material imaging, and many others.
Abstract The Radio Frequency Sensor (RFS), a new radio frequency lightning detector, was launched into geosynchronous orbit in December 2021, and first collected data in January 2022. RFS is a specialized software‐defined radio receiver that detects, records, and reports impulsive broadband radio‐frequency (RF) signatures from lightning in the very high frequency (VHF; 30–300 MHz) range. Its vantage point from a Western hemisphere geosynchronous orbit provides unique opportunities to study evolution of RF lightning signatures over the durations of thunderstorms over the Americas and Pacific Ocean. Its overlapping view with the Geostationary Lightning Mappers (GOES‐16 & 17) enables additional comparisons between the sources of optical emissions and associated VHF emissions that were not possible with previous sensors. We find that RFS preferentially detects bright VHF signals called transionospheric pulse pairs (trans‐ionospheric pulse pairs (TIPPs)). It is estimated that more than 85% of the RFS‐detected lightning events are TIPPs. This paper presents initial results from the first year and a half of on‐orbit operation.
The Superconducting Quantum Materials and Systems (SQMS) Center, led by Fermi National Accelerator Laboratory, is one of five U.S. Department of Energy-funded research centers dedicated to advancing quantum computing and quantum sensing. SQMS leverages its expertise in superconducting radio-frequency (SRF) cavities and transmon qubits to develop sensitive quantum sensors to probe fundamental physics. This presentation will highlight the progress and innovations in SQMS’s quantum sensing thrust, with a particular focus on searches for particles beyond the Standard Model, dark matter candidates, and gravitational waves. Recent developments include the search for dark photon dark matter using transmon qubits and widely tunable superconducting cavities (4 GHz–7 GHz), a quantum-enhanced axion dark matter search employing an itinerant single microwave photon counter, and efforts to detect low-mass axions and high-frequency gravitational waves using multimode superconducting cavities.
The overall objective of the proposed Phase I program was to design, construct and demonstrate a fiber optic sensing system capable of providing temperature and magnetic field measurements with an enhanced spatial resolution that can be implemented over a large surface area (cryomodules) to survey superconducting radio frequency cavities and magnets. A magnetic field sensor capable of detecting fluxes on the order of 1 μT is required to detect the distribution of trapped flux on the cavity surface. A unique distributed magnetic field sensor was successfully designed and constructed to demonstrate the detection of magnetic fluxes less than 500 nT. The sensor leveraged the ultra-high sensitivity of Sentek’s picoDAS to measure the magnetostriction induced vibrations in a commercially available Metglas 2605 SC ribbon that was in physical contact with sensing fiber. Static magnetic fields were detected by applying an alternating current a copper wire proximate to the Metglas 2605SC ribbon to create an AC bias magnetic field. In an alternative approach, an AC bias magnetic field was applied to a special magnetic field sensing fiber with Metglas 2605SC cladding successfully detect a magnetic field flux of a 3 μT. Exhaustive testing was performed to characterize the dependency of sensor response on the direction of the applied magnetic field. Although the special sensing fiber based magnetic field sensor did not exhibit an observable dependence on the direction of the magnetic field, the Metglas 2605SC ribbon sensor exhibited a clear directional dependence. A wide variety of polymer materials were evaluated to enhance the temperature response of an FBG based sensor at cryogenic temperatures. The processing and performance challenges provided the motivation to develop a new simple cryogenic temperature sensor that uses a commercially available fiber optic splice protector. The EVA hot melt tube that becomes adhered to the optical fiber and the polyolefin outer tube that shrinks upon heating in the fusion splicer heater provide the high thermal expansion coefficient necessary to impart a significant strain on the FBG when exposed to cryogenic temperatures. The temperature sensitivity (Δ𝜆𝐵𝑟𝑎𝑔𝑔~ 62 𝑝𝑝𝑚/℃) of the FBG-based sensor was on par with the best reported to date. The simple design, use of readily available cost-effective materials, and well-established processing techniques lends this approach to the creation of hundreds to thousands of temperature sensors on one single optical fiber length. The inherently small form factor also allows for co-location with the distributed magnetic field sensor. In preparation for field testing of the prototype sensing system at the Jefferson Labs in potential Phase II program, several different cable designs were evaluated to package the sensors. The preliminary successful demonstration of fully functional sensing cables provides the foundation for subsequent development efforts to advance the Technology Readiness Level of the technology. The technical feasibility of the proposed approach was successfully demonstrated in this Phase I effort.