Charge Sensor Research Articles

Detection of charge states of an InAs nanowire triple quantum dot with an integrated nanowire charge sensor

A linear triple quantum dot (TQD) integrated with a quantum dot (QD) charge sensor is realized. The TQD and the charge sensor are built from two adjacent InAs nanowires by the fine finger gate technique. The charge state configurations of the nanowire TQD are studied by measurements of the direct transport signals of the TQD and by detection of the charge state transitions in the TQD via the nanowire QD sensor. Excellent agreement in the charge stability diagrams of the TQD obtained by the direct transport measurements and by the charge-state transition detection measurements is achieved. It is shown that the charge stability diagrams are featured by three groups of charge state transition lines of different slopes, corresponding to the changes in the electron occupation numbers of the three individual QDs in the TQD. It is also shown that the integrated nanowire QD sensor is highly sensitive and can detect the charge state transitions in the cases where the direct transport signals of the TQD are too weak to be measurable. Tuning to a regime, where all three QDs in the TQD are close to being on resonance with the Fermi level of the source and drain reservoirs and co-existence of triple and quadruple points becomes possible, has also been demonstrated with the help of the charge sensor in the region where the direct transport signals of the TQD are hardly visible.

Predicting-Scheduling-Tracking: Charging Nodes With Non-Deterministic Mobility

In Wireless Sensor Networks (WSN), the energy problem plays the critical role in network performance and lifetime, because of the limited battery capacities of sensor nodes. Recently, emerging wireless charging technologies provide a promising approach to address the energy problem in WSN. Researchers construct Wireless Rechargeable Sensor Networks (WRSN), which introduce mobile chargers with high capacity batteries to charge sensor nodes. Most studies in WRSN have paid attention to charging static nodes or mobile nodes with deterministic trajectories. In this work, we explore how to charge nodes with non-deterministic mobility. We propose a novel approach, named Predicting-Scheduling-Tracking (PST), to perform charging tasks in this case. In the proposed scheme, different from the existing work, we guide the mobile charger to chase the sensor and recharge it. In our work, the base station runs an improved LSTM to predict the future locations of nodes periodically. Then, the mobile charger can select an appropriate node as the charging target by a charging scheduling algorithm. During the energy transferring, a Kalman-filter-based tracking algorithm is used to ensure the charging-required distance between the mobile charger and the target node. The simulation results show that the proposed charging scheme can fulfil the charging tasks in WRSN of nodes with non-deterministic mobility.

RETRACTED: Design of composite data acquisition gateway based on 5G network

The integrated data collection system hasn't changed much over the years. Their growth is dominated by digital filtering, and they get a new avionics bus. Time division multiplexing is still used to communicate with the system's data collector. These schemes make use of commands and data buses. While this method works, it has many drawbacks. These shortcomings are overwhelming the strict system architecture, system bandwidth limits, and expertise to only obtain an avionics bus, otherwise system bandwidth and one-way flow of data and control. This lead for high-end video recorders. To quietly monitor our actions and valuable information provides important warnings. However, it is usually completed by a device with this small processing power, which tracks the device, stores the data of the user's data, and is limited in processing. Therefore, it is important to store and process user data in the cloud to track 5 G network activity. This article proposes a simple 5G network gateway solution for custom user monitoring equipment. Collects charge data on Network 5G and Charging-aware multi-mode based routing protocol (CMRP). The CMRP routing mechanism is not yet stable, but the changes depend on the state of the energy charge sensor. This cannot be complemented by energy-efficient sensors and routing protocols that consume less energy.

Detection of tunneling events in physically defined silicon quantum dot using single-shot measurements improved by numerical treatments

We report the detection of a single tunneling event of electrons in a physically defined silicon quantum dot system. In the measurement, we observed single-shot tunneling events in a quantum dot using a charge sensor; however, the tunneling statistics are difficult to acquire because of their weak signals. Hence, two numerical treatments using a digital filter and a change point detection technique are applied, and the latter enables precise signal detection, which is confirmed in the simulation. We expect the combination of such digital processing with integrated circuits to enable a fast qubit readout from a noisy system.

Design and Optimization of Coupling Coils for Bidirectional Wireless Charging System of Unmanned Aerial Vehicle

To solve the battery power supply problem with wireless sensor networks (WSNs), a novel bidirectional wireless charging system is proposed, in which an unmanned aerial vehicle (UAV) can fly to the WSNs to charge sensors through high-frequency wireless power transfer (WPT) and also obtain energy for its own battery in the same way. To improve the performance of the UAV bidirectional wireless charging system, a lightweight design is adopted to increase its loading capacity and working time. Moreover, the radii and the numbers of turns and pitches of coupling coils were designed and optimized on the basis of simulations and experiments. Experimental results show that the weight of optimized UAV coil was reduced by 34.45%. The power transfer efficiency (PTE) of the UAV coil to sensor coil increased from 60.2% to 74.4% at a transmission distance of 15 cm, while that of the ground transmitting coil to UAV coil increased from 65.2% to 90.1% at 10 cm.

Virtual refrigerant charge sensor for booster refrigeration systems

Abstract In this paper a virtual refrigerant charge sensor (VRCS) for a booster refrigeration system with a receiver unit is developed and proposed. The VRCS estimates the liquid charge in the receiver unit by utilizing a combined mass and energy balance equation for mapping the change in the receiver unit’s time constant to the liquid volume in the receiver unit. The time constant for the receiver unit is found by utilizing the commonly available data in an online subspace identification method. The parameters estimated by the online subspace identification method are recast in a linear fractional transformation structure to identify parameter changes due to liquid volume change. Hence, the VRCS functions in a plug and play framework where only the initial refrigerant charge in percentage is required to be known. Results from utilizing the VRCS on experimental data from a charge test conducted on a test setup located at Danfoss A/S are shown, where the VRCS shows an accurate estimation of the liquid charge in the receiver. The results from the VRCS can be employed for: early prediction of low charge in the system, early detection of a too large leakage rate, estimation of the current charge on a daily basis, and estimation of the amount of charge lost.

Impact of degree of substitution of cationic xylan on strength of cellulose fiber networks along with medium conductivity

Hemicelluloses have been considered as an efficient component to improve the bonding or strength of fiber networks. The degree of substitution (DS) of cationic hemicelluloses and the ionic strength of applied medium are therefore two most important and crucial factors influencing its performance in wet-end of papermaking. Therein, the impact of DS of cationic xylan (CX) derived from corncob on the strength of fiber networks was systematically explored in aqueous media with various ionic strengths. Initially, a series of CXs with DS ranging from 0.051 to 0.113 were synthesized via a microwave-assisted method. The resulting CXs were applied as wet-end additives in the media with conductivity of 500, 2500, and 5000 μs/cm, representing the low, moderate, and high level of electrolytes in closed white water circulation, respectively. Comparing to the control, all CXs improved the tensile and tear strength of paper significantly, while not in burst strength. The medium conductivity had an adverse impact on the performance of CXs, and the optimum DS of CXs was down shifted to about 0.05 ∼ 0.06. To mimic the adsorption of CXs on fiber surface, the adsorption behavior of CXs on negatively charged gold sensor was monitored by SPR in an attempt to reveal the mechanism. It was found that CXs with low DS had a larger adsorption capacity, thicker and softer adlayer, which are beneficial to fiber bonding and paper strength. This study is meaningful to the applications of xylan into value-added products and to the valorization of agricultural and forestry wastes.

Reduction of charge noise in shallow GaAs/AlGaAs heterostructures with insulated gates

We study low-frequency charge noise in shallow GaAs/AlGaAs heterostructures using quantum point contacts as charge sensors. We observe that devices with an Al2O3 dielectric between the metal gates and semiconductor exhibit significantly lower charge noise than devices with only Schottky gates and no dielectric. Additionally, the devices with Schottky gates exhibit drift over time toward lower conductance, while the devices with the dielectric drift toward higher conductance. Temperature-dependent measurements suggest that in devices with Schottky gates, noise is dominated by tunneling from the gates to trap sites in the semiconductor, and when this mechanism is suppressed by inclusion of a dielectric, thermally activated hopping between trap sites becomes the dominant source of noise.

Fault diagnosis of coal-mine-gas charging sensor networks using iterative learning-control algorithm

To detect and estimate the faults of discrete linear time-varying uncertain systems, a discrete learning strategy is applied to fault diagnosis, and a new fault-detection and estimation algorithm is proposed. The algorithm adopts the threshold-limit technology. In the selected optimal time domain, a residual signal is used to perform iterative learning correction for the introduced virtual faults so that the virtual faults in an actual system approach the actual faults. The same method is repeated in the remaining optimal time domain to achieve the objective of fault diagnosis. The algorithm not only completes the fault detection and estimation of a discrete linear time-varying uncertain system but also improves the reliability of fault detection and reduces the false alarm rate. Finally, the simulation results verify the effectiveness of the proposed algorithm.

Adaptive Transmission Design for Rechargeable Wireless Sensor Network With a Mobile Sink

In this article, we aim at maximizing the data gathering performance of the rechargeable wireless sensor network, where a mobile sink moves along the predefined path to charge sensor nodes through a wireless energy transfer technique and gather data from them. First, we show how to transform the original time-average optimization problem into a queue stability one by using the Lyapunov optimization framework, then we show how to decompose it into multiple subproblems by using the optimization decomposition. A distributed speed control and routing algorithm was proposed to reduce the computing load of the mobile sink and to obtain the near-optimal solution for data collection. Our analysis shows that there is an inherent tradeoff between the network utility and the average data queuing size, and the proposed adaptive transmission scheme can achieve the near-optimal network utility when a certain queueing delay can be tolerated.

Probing on the Stable Structure of Silicon-Doped Charged Magnesium Nanomaterial Sensor: SiMgn±1 (N = 2−12) Clusters DFT Study

The stable structures of silicon-doped charged magnesium nanomaterial sensor, SiMg±1 n(n=2-12) clusters, were systematically investigated using the CALYPSO approach coupled with density functional theory (DFT). The growth mechanism of SiMg±1 n(n=2-12) nanosnsors shows that the tetrahedral and tower-like structures are two basic structures, and almost all other clusters’ geometries are based on their variants. Most importantly, the fascinating SiMg-1 8and SiMg+1 8clusters are obtained through stability calculations of all the lowest energy state of clusters. These two clusters show the strongest local stability and thus can be served as reliable candidates for experimentally fabricated silicon-doped magnesium nanosensor. Electronic structural properties and chemical bonding analysis are also adopted to further study the stability of SiMg-1 8and SiMg+1 8 nanosensors. The theoretical calculations of infrared (IR) and Raman spectra of SiMg-1 8and SiMg+1 8clusters show that their strongest spectral frequencies are distributed in the range of 80 – 240 cm-1. We believe that our studies will stimulate future synthesis of silicon-doped magnesium in IoT nanosensors.

A deep reinforcement learning-based on-demand charging algorithm for wireless rechargeable sensor networks

Wireless rechargeable sensor networks are widely used in many fields. However, the limited battery capacity of sensor nodes hinders its development. With the help of wireless energy transfer technology, employing a mobile charger to charge sensor nodes wirelessly has become a promising technology for prolonging the lifetime of wireless sensor networks. Considering that the energy consumption rate varies significantly among sensors, we need a better way to model the charging demand of each sensor, such that the sensors are able to be charged multiple times in one charging tour. Therefore, time window is used to represent charging demand. In order to allow the mobile charger to respond to these charging demands in time and transfer more energy to the sensors, we introduce a new metric: charging reward. This new metric enables us to measure the quality of sensor charging. And then, we study the problem of how to schedule the mobile charger to replenish the energy supply of sensors, such that the sum of charging rewards collected by mobile charger on its charging tour is maximized. The sum of the collected charging reward is subject to the energy capacity constraint on the mobile charger and the charging time windows of all sensor nodes. We first prove that this problem is NP-hard. Due to the complexity of the problem, then deep reinforcement learning technique is exploited to obtain the moving path for mobile charger. Finally, experimental simulations are conducted to evaluate the performance of the proposed charging algorithm, and the results show that the proposed scheme is very promising.

Algorithm for automated tuning of a quantum dot into the single-electron regime

We report an algorithm designed to perform computer-automated tuning of a single quantum dot with a charge sensor. The algorithm performs an adaptive measurement sequence of sub-sized stability diagrams until the single-electron regime is identified and reached. For each measurement, the signal processing module removes the physical background of the charge sensor to generate a binary image of charge transitions. Then, the image analysis module identifies the position and number of lines using two line detection schemes that are robust to noise and missing data.

Multi‐source ambient energy harvester based on RF and thermal energy: Design, testing, and IoT application

AbstractBillions of wireless sensing devices must be powered for IoT applications. Collecting energy from the ambient environment to power sensor nodes is a promising solution. Solar energy has been one of the main sources of ambient energy due to its availability, higher power density, and the maturity of the solar photovoltaic industry. However, there are many scenarios (indoor environment, outdoor environment during nighttime, poor weather conditions, underground, etc) where ambient solar energy is either not available or not sufficient for practical applications. For such scenarios, other renewable sources of energy must be sought. Typically, not enough power is collected from one ambient source to charge sensor nodes for continuous operation. In this work, we present a multi‐source energy harvester that collects RF and thermal energy (both available 24 hours) from the ambient environment simultaneously. The RF energy harvester is multi‐band and collects power from GSM (900, 1800 MHz) and 3G (2100 MHz). The thermal harvester converts diurnal temperature fluctuations to electrical energy using high thermal effusivity phase change material. Extensive field testing has been performed in three different conditions—outdoors, indoors, and buried underground—to highlight the usefulness of the multi‐source energy harvester in all these environments. When one source is disabled, the harvester still generates energy from the remaining active source and can enable continuous operation of futuristic IoT sensors. As a proof of concept, a real‐world IoT application is demonstrated, where temperature and humidity sensors are powered by the multi‐source energy harvester. Continuous robust operation of the sensors and wireless data transmission after each 3.7 seconds are expected when both harvesters operate in full mode. Scenarios, where only single thermal energy harvester or only single RF energy harvester operates, are also demonstrated and data transmission with average time intervals of 30 seconds and 9 minutes is achieved, respectively.

Understanding and Preventing Health Concerns About Emerging Mobile Health Technologies.

New technologies and innovations have often improved population well-being and societal function; however, these are also often initially accompanied by worry and fear. In some cases, such worries can impede, or even prevent entirely, the adoption of the technology. Mobile health (mHealth), a discipline broadly focused on employing ambulatory technologies to improve the affordability, reach, and effectiveness of health promotion and clinical intervention approaches, offers new innovations and opportunities. Despite emerging evidence supporting mHealth efficacy (eg, for improving health outcomes), some individuals have concerns about mHealth technology that may impede scalability, efficacy, and, ultimately, the public health benefits of mHealth. We present a review and conceptual framework to examine these issues, focusing on three overarching themes: biophysiological, psychological, and societal concerns. There are features of mHealth that lead to worries about the potential negative effects on an individual’s health (eg, due to exposure to electromagnetic or radio waves), despite evidence supporting the safety of these technologies. When present, such beliefs can lead to worry that gives rise to the experience of unpleasant and concerning physical symptoms—the nocebo effect. This may represent an important implementational barrier because of apprehension toward beneficial mHealth products (or features thereof, such as wireless charging, wearable or implantable sensors, etc) and may also have broader ramifications (eg, leading to economic, governmental, and legislative actions). In addition to reviewing evidence on these points, we provide a broad three-step model of implementation research in mHealth that focuses on understanding and preventing health concerns to facilitate the safe and effective scalability of mHealth (and that may be generalizable and applied to similar technologies): (1) evaluating and better discerning public perceptions and misperceptions (and how these may differ between populations), (2) developing theory-based public health communication strategies regarding the safety of mHealth, and (3) disseminating this messaging using evidence-based methods. Collectively, these steps converge on reviewing evidence regarding the potential role of worry and nocebo in mHealth and providing a model for understanding and changing attitudes and preventing unfounded negative perceptions related to mHealth technology.

Calibration of the Falcon Solid‐state Energetic Electron Detector (SEED)

AbstractThe Falcon Solid‐state Energetic Electron Detector (FalconSEED) is an energetic charged particle sensor that has been developed and tested at the United States Air Force Academy in an effort to monitor electron flux across the energy range of 14 to 145 keV in geosynchronous orbit. This sensor has been developed to complement ongoing efforts by the Air Force Research Laboratory to advance a comprehensive space environment sensor suite, Compact Environmental Anomaly Sensor (CEASE3), for anomaly resolution. In addition, FalconSEED is intended to demonstrate the ability to operate a radiation sensor based on predominantly commercial off the shelf components in the geosynchronous environment. This paper describes the design, development, and calibration of FalconSEED. The electron energy spectrum of interest for the FalconSEED sensor is 14 to 145 keV, and is intended to supplement data acquired from the CEASE3 instrument for anomaly resolution. The calibrations at the Space Atmospheric Research Center at the United States Air Force Academy and Kirtland Air Force Base were used to first establish the conversion between histogram bin number and deposited energy in the sensor, and second have been used to determine the geometric factor as a function of energy. The FalconSEED will be integrated as a science payload on the Space Test Program Satellite‐6 which also includes the first flight of the CEASE3 instrument and is scheduled to launch in 2021.

High-Density Immobilization of Tobacco Mosaic Virus Nanotubes As Enzyme Nanocarriers Onto Field-Effect Biosensor Structures

Introduction Viruses are not only infectious agents, they are also known as promising functional building blocks for application in nano- and biotechnologies. The tobacco mosaic virus (TMV) was the first studied plant virus and is widely distributed; it infects vegetables, like tomato, bell pepper, beans and other members of the family Solanaceae, while it is totally harmless for mammals [1]. It is one of the most studied plant viruses and its genome is completely sequenced, whereby genetical and chemical modification is easy. TMV has a nanotube-like shape with a length of 300 nm, an outer diameter of 18 nm and an inner diameter of 4 nm. Since it possesses a high chemical and physical robustness, it can be integrated with different electronic transducers for bio- and chemical sensing applications [2].Recently, we presented a TMV-based amperometric glucose biosensor [3] and a potentiometric penicillin biosensor [4]. TMV was used as enzyme nanocarrier for the enzyme glucose oxidase and penicillinase, respectively [3,4]. The sensitivity and detection limit of these biosensors, among others, depend on the density of TMVs on the sensor surface. The surface density of the immobilized TMVs is strongly influenced by the electrostatic interactions between the charged TMVs and sensor surface as well as by the inter-TMV-nanotubes repulsion, which could be changed by varying the pH value and the ionic strength of the TMV solution. In this study, we investigated an impact of the pH value and ionic strength of the TMV solution on the surface density of TMV nanotubes immobilized onto Ta2O5-gate capacitive field-effect electrolyte-insulator-semiconductor (EIS) sensors. Materials and Methods TMV particles modified with biotin-linker molecules (TMVBio), which serve as binding sites for streptavidin-conjugated enzymes, have been immobilized onto capacitive field-effect EIS sensors with Ta2O5 as transducer layer, as shown in Fig. 1. Immobilization was performed from TMVBiosolutions with different values of pH between pH 3.0 and pH 9.5 and ionic strength between 0.1 mM and 750 mM. The sensors have been electrochemically characterized before and after TMVBio immobilization by capacitance-voltage- and constant-capacitance methods, respectively. In addition, the density of the immobilized TMVs and morphology of the sensor surface has been investigated by means of scanning electron microscopy (SEM). Results and Conclusions The TMVBio density on the Ta2O5 sensor surface was influenced by varying the pH value (as exemplarily shown in Fig. 1) and ionic strength (not shown) of the TMVBio solution. The amplitude of the field-effect sensor signal correlates well with the density of the immobilized TMVBionanotubes. Thus, optimized conditions for the high-density immobilization of TMVBio nanotubes onto the Ta2O5 surface and thereby enhanced biosensing have been found. Details of the experiments and the obtained results will be presented and discussed.Figure 1: Measurement set-up with schematic layer structure of the capacitive EIS sensor modified with negatively charged TMVBio particles (a). SEM images of the Ta2O5-sensor surface modified with TMVBio nanotubes at pH 4.5 (b) and pH 7.0 (c), respectively. Acknowledgements The authors like to thank Dr. Claudia Koch and Rebecca Hummel, Stuttgart, for scientific and technical support.

Photon-assisted tunnelling of zero modes in a Majorana wire

Hybrid nanowires with proximity-induced superconductivity in the topological regime host Majorana zero modes (MZMs) at their ends, and networks of such structures can produce topologically protected qubits. In a double-island geometry where each segment hosts a pair of MZMs, inter-pair coupling mixes the charge parity of the islands and opens an energy gap between the even and odd charge states at the inter-island charge degeneracy. Here, we report on the spectroscopic measurement of such an energy gap in an InAs/Al double-island device by tracking the position of the microwave-induced quasiparticle (qp) transitions using a radio-frequency (rf) charge sensor. In zero magnetic field, photon assisted tunneling (PAT) of Cooper pairs gives rise to resonant lines in the 2e-2e periodic charge stability diagram. In the presence of a magnetic field aligned along the nanowire, resonance lines are observed parallel to the inter-island charge degeneracy of the 1e-1e periodic charge stability diagram, where the 1e periodicity results from a zero-energy sub-gap state that emerges in magnetic field. Resonant lines in the charge stability diagram indicate coherent photon assisted tunneling of single-electron states, changing the parity of the two islands. The dependence of resonant frequency on detuning indicates a sizable (GHz-scale) hybridization of zero modes across the junction separating islands.

DFT Analysis of Vanadium Tris(Dithiolene)-Based Double-Gated Single-Electron Transistor

A double-gated single-electron transistor (SET) based on metal–organic complex vanadium tris(dithiolene) has been modelled and investigated for the operation and electrostatics analysis. Density functional theory and non-equilibrium Green’s function method have been opted for first-principle calculations. For the enhancement of the electrostatic control in the SET operation, an addition of a second gate to the basic configuration of SET has been implemented. The results of the energy calculations have shown the expected enhanced electrostatic control in the SET operation with the incorporation of the second gate. The total energies are used to calculate ionisation energy, affinity energy and addition energies, and have shown reduced values for the double-gated SET. Total energies are also used to plot charge stability diagrams and energy surface plots for different gate voltages, which depict the improved conduction process. Because of its sensitivity towards the molecule’s individual charge states, the resultant double-gated SET can be used as a charge sensor.

Molecular Analysis of Membrane Targeting by the C2 Domain of the E3 Ubiquitin Ligase Smurf1.

SMAD ubiquitination regulatory factor 1 (Smurf1) is a Nedd4 family E3 ubiquitin ligase that regulates cell motility, polarity and TGFβ signaling. Smurf1 contains an N-terminal protein kinase C conserved 2 (C2) domain that targets cell membranes and is required for interactions with membrane-localized substrates such as RhoA. Here, we investigated the lipid-binding mechanism of Smurf1 C2, revealing a general affinity for anionic membranes in addition to a selective affinity for phosphoinositides (PIPs). We found that Smurf1 C2 localizes not only to the plasma membrane but also to negatively charged intracellular sites, acting as an anionic charge sensor and selective PIP-binding domain. Site-directed mutagenesis combined with docking/molecular dynamics simulations revealed that the Smurf1 C2 domain loop region primarily interacts with PIPs and cell membranes, as opposed to the β-surface cationic patch employed by other C2 domains. By depleting PIPs from the inner leaflet of the plasma membrane, we found that PIP binding is necessary for plasma membrane localization. Finally, we used a Smurf1 cellular ubiquitination assay to show that the amount of ubiquitin at the plasma membrane interface depends on the lipid-binding properties of Smurf1. This study shows the mechanism by which Smurf1 C2 targets membrane-based substrates and reveals a novel interaction for non-calcium-dependent C2 domains and membrane lipids.