Power Sensor Research Articles

Triboelectrification Based on the Waste Waterproof Textiles for Multisource Energy Harvesting

AbstractThe demand for sustainable energy resources to power sensor networks such as consumer electronics, agricultural technologies, digital forest management, and home automation is rapidly increasing. There are sustainability challenges to consider, where waste waterproof textiles are critical to encourage the development of a circular economy in the development of new energy technologies. This present work focuses on the utilization of direct waste waterproof textiles to design two types of triboelectric nanogenerator (TENG), which include a liquid‐solid based TENG (L‐S TENG) and a flapper‐type TENG. The bottom electrode configuration for the L‐S TENG and single electric mode working mechanism is considered for the flapper‐type TENG. Waste waterproof textiles can lead to a possible expansion of sustainable material for energy harvesters. The raincoat textile‐based L‐S TENG (L‐STENG‐R) is able to generate 0.5 V at a tilt angle of 50 degrees and power of 0.41 nW. TENGs based on discarded waterproof textiles are further utilized to demonstrate their phase change sensing, along with wind and water energy harvesting. This approach focus on decreasing waste and lower dependency on traditional resources to support environmentally responsible energy alternatives.

Design and Measurement of Near-Zero Thermopile RF Power Sensors for GaAs MMIC Applications

Design and Measurement of Near-Zero Thermopile RF Power Sensors for GaAs MMIC Applications

Achieving High Thermoelectric, Stretchable, and Self-Healing Capabilities in Self-Supported PEDOT:PSS/Nafion/Poly(vinyl Alcohol) Composites for Wearable Thermoelectric Power Generators and Sensors

Achieving High Thermoelectric, Stretchable, and Self-Healing Capabilities in Self-Supported PEDOT:PSS/Nafion/Poly(vinyl Alcohol) Composites for Wearable Thermoelectric Power Generators and Sensors

Performance of vibration and current signals in the fault diagnosis of induction motors using deep learning and machine learning techniques

Induction motors (IMs) play a pivotal role in various industrial applications, powering critical systems such as pumps, compressors, fans, blowers, and refrigeration and air conditioning systems. Monitoring the health of these IMs is essential for ensuring reliable operation. Numerous sensors, including vibration, current, temperature, acoustic, and power sensors, can be employed for their health monitoring. This article conducts a comprehensive comparative analysis of two widely used sensors—vibration and current, for classifying different health states of IMs, such as a healthy condition, bearing fault, and misalignment. The study employed deep learning techniques, specifically 1D and 2D convolutional neural networks, trained on raw data. Additionally, machine learning techniques, including random forest and XGBoost, were utilized and trained on features derived from preprocessed signals using fast Fourier transform and discrete wavelet decomposition. Comparative results indicated that vibration signals achieved remarkably high accuracy, nearly 100%, in detecting the investigated mechanical faults, while current signals, after signal processing and manual feature extraction, achieved an accuracy of 87.41%. These results demonstrate that, though current sensors are a viable alternative to vibration sensors, their performance can be affected by the type and degree of the considered faults. This study also highlights the attributes of vibration and current signals in the health monitoring of rotating machinery such as IMs.

Design and Analysis of Sierpinski Fractal Antennas for Millimeter‐Wave 5G and Ground‐Based Radio Navigation Applications

ABSTRACTThe paper describes compact fractal antennae combined with ground defected structures (DGS) and substrate integrated waveguide (SIW) techniques for enhancing resonate and multiband behavior concerning bandwidth, gain, and radiation in the desired mm‐wave region. Based on the proposed antennae, multiple good notches over wide bands depict a good radiation pattern. The antennae's ultra wide bandwidths (UWB) are 16, 16, and 11.4 GHz concerning resonant frequencies 26.34, 30.35, 34.5, 36.65 GHz; 25.8, 32.2, 35.3 GHz; 30.22, 37.68 GHz, of concerning antennae ant 1 (FA1), 4 (FA2), and 6 (FA3), respectively. The proposed antenna (FA3) with the SIW technique has a peak gain of 5.9 dBi and a peak front‐to‐back ratio of 20 dB, respectively. The proposed antennas are targeted to cover the practical contribution aspect, including advanced 5G bands support (24–40 GHz), broadband spectrum, frequency agility, low 3 dB beamwidth, interference reduction, and medical application suitability, respectively. The proposed antennae also cover the desired mm wave 5G region with different resonating notches over ultra‐wide bandwidth, such as n257, n258, n259, n260, and n261, and ground‐based radio navigation applications. Results are validated with vector network analyzer, spectrum analyzer, and power sensor in the presence of absorber, respectively.

Reconfiguration of PV array for improved performance under different partial shading conditions using Roulette Barrel Shifter approach

This article proposes an innovative reconfiguration technique called Roulette Barrel Shifter (RBS) for Total Cross Tied (TCT) connected PV arrays. Inspired by a spinning roulette wheel and the barrel shifter from digital signal processing, it shifts or rotates input data bits by a number of bits. Unlike other methods, RBS does not require advanced maximum power point tracking devices, sensors, or complex switching mechanisms, making it more cost-effective. Simulation studies on 9 × 9 and 10 × 10 PV arrays under Partial Shading (PS) conditions, as well as experimental validation on a 5 × 5 array, demonstrate that RBS increases power by up to 15.45 % compared to TCT. The algorithm is also tested for scalability and adaptability on a large solar plant (4 MW, 2175 V, 25 × 750 PV array). For the first time, the article introduces a unique wiring loss (WL) performance metric using the k-means algorithm. Based on this metric, RBS is shown to reduce WL by up to 6.98 % compared to other recently published methods. Comparative analysis reveals that RBS reduces mismatch loss (ML) by up to 61.41 % compared to TCT, establishing its superiority over existing dynamic reconfiguration approaches in both performance and efficiency across various scales of solar arrays.

Vibrational coupling-based multilayer flexible triboelectric nanogenerator for wide-amplitude omnidirectional wave energy harvesting

Wave energy harvesters are viewed as potential foundations for self-powered wireless sensor devices. In this study, a multilayer flexible triboelectric nanogenerator (MFLU-TENG) is proposed and evaluated, which combines the stretching properties of springs and the deformation properties of flexible materials to enable wide-wavelength energy harvesting. It captures wave energy even at very low amplitudes, requiring a minimum wave amplitude of 5 cm for energy recovery. The study explores how geometric parameters of the MFLU-TENG affect vibration behavior and output performance. Optimal performance is achieved when varying the thickness of the PETG cushioning layer, resulting in a peak power density of up to 7.7 mW/m2. Three MFLU-TENGs connected in parallel successfully power 15 LEDs. And four parallel groups of MFLU-TENGs charged a 100µF capacitor to 5.3 V in 198 s and successfully powered the temperature and humidity sensor. And the MFLU-TENGs charge the 100uf capacitor to 4.0v and power the clock in 153 s. These findings underscore the MFLU-TENG’s potential as a cost-effective and efficient wave energy harvesting device, using renewable wave energy to power sensors or microelectronic devices in the Internet of Things.

Design of Variation Tolerant Near Threshold Processor Using Artificial Ecosystem Optimizer with Hybrid Deep Learning

Recently, several applications of data mining and pattern recognition require statistical signal processing (SP) to be used and machine learning (ML) techniques for processing massive data volumes in energy-constrained contexts. It is developing interest in executing difficult ML techniques like convolutional neural network (CNN) on lesser power embedding environments to allow on-device learning and inference. Several of these platforms are that utilized as lower power sensor nodes with lower to medium throughput conditions. Near threshold processor (NT) proposals are appropriate for these applications where as affected by a vital enhancement in variants. This research offers an Artificial Ecosystem Optimizer with Hybrid Deep Learning for Variation-Tolerant Near-Threshold Processor (AEOHDL-VTNT). The inference of embedded systems at the network edge serves as the foundation for the AEOHDL-VTNT approach that is being discussed. In the described AEOHDL-VTNT approach involves two primary processes: HDL-based VTNT design and hyper parameter tweaking. Initially, the HDL model is used to develop the VTNT. Next, in the second step, the AEO method is used for hyper parameter tweaking of the HDL model, which improves the HDL's overall performance. A number of simulations were carried out to show how the AEOHDL-VTNT approach improved performance. The simulation results showed that the AEOHDL-VTNT approach outperformed other models.

Folded flexure MOEMS for the detection of PSA and hepatitis DNA as biosensor for prostate cancer and viruses

Micro-opto-electro-mechanical systems (MOEMS) biosensors are employed in various applications such as disease monitoring, drug investigation, detection of pollutants, and biological fluid studies. In this paper, a novel MOEMS biosensor based on a differential folded-flexure structure is introduced. The designed device is employed to detect prostate-specific antigen (PSA) protein and Hepatitis DNA. The target molecules cause a mechanical deflection in the folded-flexure; subsequently, the transmitted optical power across the finger, attached to the flexure, is modulated in proportion to the input concentration. Then, a photodiode power sensor measures the modulated optical power, where the output of the sensor is simply a current related to the target molecules’ concentrations. The employed readout circuit operates at a wavelength of λ = 1550 nm with a laser power of 1 µW. The dimensions of the proposed biosensor are considered to be 365 × 340 × 2 μm³, making this sensor small enough and suitable for integration. The designed biosensor provides notable features of mechanical deflection sensitivities of 0.2053 nm/(ng/ml) and 7.2486 nm/nM, optical transmittance sensitivities of 0.535504 × 10−3 1/(ng/ml) and 18.91 × 10−3 1/nM, total output sensitivities of 0.5398 (mA/W)/(ng/ml) and 19.059 (mA/W)/nM, and measurement ranges of 0-1000 ng/ml and 0-28.33 nM for PSA and Hepatitis DNA, respectively. The proposed system is a sensitive and powerful sensor that can play an important role in diagnosing many diseases.

Dioxythiophene/Nafion Polymer Composite Membranes for Tunable Size-Based Selectivity in the Voltammetric Detection of Small Neuropeptides.

Carbon-fiber microelectrodes are proven and powerful sensors for electroanalytical measurements in a variety of environments, including complex systems such as the brain. They are used to detect and quantify a range of biological molecules, including neuropeptides, which are of broad interest for understanding physiological function. The enkephalins (met- and leu-) are endogenous opioid peptides that are involved in both pain and motivated behavior. Each is comprised of only five amino acids including tyrosine, an electroactive species. Electroanalytical measurements targeting tyrosine can reveal the dynamics of endogenous enkephalin transients in live tissue. However, when using electrochemistry in a biological system, selectivity is always a concern. Many larger neuropeptides also contain tyrosine. As such, they could generate a redox signature similar to that of the enkephalins, potentially confounding the measurement. In this work, three distinctly sized dioxythiophene monomers were mixed with Nafion and electrodeposited onto cylindrical carbon-fiber microelectrodes to form composite polymer films that allow for the tunable, size-based exclusion of larger molecules. The dioxythiophene monomers 3,4-ethylenedioxythiophene (EDOT), 3,4-propylenedioxythiophene (ProDOT), and 3,4-(2',2'-diethylpropylene) dioxythiophene (ProDOT-Et2) were used to create nanostructured pores of increasing size. The dioxythiophene/Nafion modified electrodes were characterized in the voltammetric detection of dopamine, a classic small molecule neurotransmitter, and a series of tyrosine containing neuropeptides of increasing size: met-enkephalin (M-ENK; 5 residues), oxytocin (OXY; 9 residues), neurotensin (NT; 13 residues), and neuropeptide Y (NPY; 36 residues). The modified electrodes exhibited enhanced selectivity for smaller peptide species over larger peptides in a manner consistent with the size of the dioxythiophene monomer incorporated into the polymeric film, allowing for tunability in terms of size-based selective detection.

Sediment microbial fuel cells capable of powering outdoor environmental monitoring sensors

In this study, Sediment Microbial Fuel Cells (SMFCs) prototypes have been developed to operate under open-air conditions and power sensors for environmental monitoring. Two SMFCs with a volume of 50 l each, consisting of two types of anodic materials – graphite and coke, were operated on-field for over a year. The electrical outputs have been recorded and compared with the measured environmental parameters such as temperature, light illumination, atmospheric pressure, humidity, etc. The statistical analysis of the obtained data shows that temperature changes between 0 and 14 °C do not affect the power achieved. On the contrary, the sunlight irradiation showed a second-order polynomial correlation with the current generated by the SMFCs, increasing the latter during the days. The cathode reactions significantly impacted the power density achieved by both explored SMFCs and the system's sustainability. The metallurgical coke is suggested to be used as an inexpensive and convenient anode material for SMFCs giving compatible results to the widely used graphite.

Virtual active power sensor for eolic self-consumption installations based on wind-related variables

Abstract Green energy production is expanding in individual and large-scale electricity grids, driven by the imperative to reduce greenhouse gas emissions. This research performs a comparative analysis of several linear and non-linear regression models, intending to identify the most effective method to estimate the active power produced for a mini wind turbine using meteorological variables, looking for a reliable virtual sensor. The modeling process followed a feature selection step before applying eight machine learning techniques whose results were statistically analysed to determine the best performance. The implemented virtual sensor accurately estimated the active power, being an interesting tool for anomaly detection, maintenance management or decision-making.

Study on Static Deflection Model of MEMS Capacitive Microwave Power Sensors

Study on Static Deflection Model of MEMS Capacitive Microwave Power Sensors

Aurivillius-Type SrBi4Ti4O15/PGA Composite Film-Based Flexible Triboelectric Nanogenerators for Energy Harvesting/Storage and Multipurpose Tap-Indication Transducer Applications.

Recent advancements in flexible triboelectric nanogenerators (TENGs) have widely focused on converting mechanical energy into electrical energy to power small wearable electronic gadgets and sensors. To effectively achieve this, an efficient energy-converted power management circuit is required. Herein, we report on Aurivillius-type strontium bismuth titanate (SrBi4Ti4O15) nanoparticles (SBT NPs)-loaded polyglucosamine (PGA) composite film-based flexible TENG to be used for energy harvesting/storage and biomechanical applications. Initially, SBT NPs were synthesized and then, different weight concentrations were loaded into PGA. The TENG devices were fabricated using different wt % composite films (SBT/PGA) and polydimethylsiloxane as positive and negative triboelectric layers, respectively, and aluminum was used as a conductive electrode attached to two tribo films. To evaluate the electrical output from the device, contact-separation operation mode was used. An optimized TENG consisting of 2 wt % SBT/PGA composite film produced the maximum electrical output voltage and current of approximately ∼239 V and ∼7.5 μA, respectively. Efficient TENG energy harvesting and storage circuits have been proposed for storing charges in capacitors and for operating electronic gadgets. The optimized TENG was employed to generate electrical energy from various biomechanical movements. Thereafter, the biodegradability of the composite film was also tested. The fabricated films were completely biodegraded within a few hours. Furthermore, the TENG was utilized as a tap-indication transducer for multipurpose switching applications.

Results from Cryo-PoF: Power over fiber for fundamental and applied physics at cryogenic temperature

The Power over Fiber (PoF) technology delivers electrical power by sending laser light through an optical fiber to a photovoltaic power converter, in order to power sensors or electrical devices. This solution offers several advantages: removal of noise induced by power lines, robustness in a hostile environment, spark free operation when electric fields are present and no interference with electromagnetic fields.This technology is at the basis of the Cryo-PoF project: an R&D funded by the Italian Institute for Nuclear Research (INFN) in Milano-Bicocca (Italy). This project is inspired by the needs of the DUNE Vertical Drift detector, where the VUV light of liquid argon must be collected at the cathode, i.e. on a surface whose voltage exceeds 300 kV. We developed a cryogenic system, which is solely based on optoelectronic devices and a single laser input line, to power both the Photon Detection devices and its electronic amplifier. The Milano-Bicocca setup employ a commercial GaAs laser source with 2 W maximum power and a photovoltaic power converter with efficiency of ∼ 30% at liquid nitrogen temperature. The results obtained in Milano Bicocca will be presented with emphasis on performance and potential application in the field of applied physics.

Application of silicon nanowires in sensors of temperature, light and humidity

Resistive and capacitive types of sensors based on silicon nanowires (SiNWs) for measuring of physical quantities have been manufactured. Silicon nanowires were obtained through a two-step metal-assisted chemical etching (MACE) method. The surface morphology of the silicon nanowire array was investigated using atomic force microscopy (AFM) and scanning electron microscopy (SEM). The impact of SiNWs synthesis parameters on sensor sensitivity has been investigated. In particular, the influence of deposition time of AgNPs and etching time of silicon, as well as the content of H2O2 in the etchant, preliminary texturing of the substrate, and additional post-processing in isotropic/anisotropic etchant after the MACE process, on the characteristics of physical quantity sensors has been determined. Static and dynamic parameters were calculated for the obtained sensors. To compare the obtained sensors, physical sensors without SiNWs were manufactured. The highest response value for temperature sensors was 132, which is two orders of magnitude better than for sensors without SiNWs. The presence of SiNWs on the surface of the sensor led to an increase in the response value by about two orders of magnitude for all types of sensors. The speed of humidity sensors improved by 3–4 times, and for temperature and light sensors by an order of magnitude. Further device applications include of multi-parameter sensors, human breath sensors, and solar radiation power sensors.

Design and Implementation of Real-Time Sensors for Three-Phase Induction Motor Performance Monitoring using Internet of Thing (IoT)

This article delves into the design and implementation of cost-effective sensors tailored for real-time monitoring of three-phase induction motor performance. The research primarily centers on the assessment of the operational effectiveness of a suite of sensors, including the PZEM-004t power sensor, MLX90614 temperature sensors, NJK-5002C rotation sensors, and ADXL345 vibration sensors, within the context of three-phase induction motor performance analysis. The monitoring system for three-phase induction motor performance is built around the ESP-32 platform, incorporating the ADXL345 vibration sensor. This article successfully presents the individual sensor performance, which was systematically evaluated through testing on a three-phase induction motor that had been meticulously designed and closely observed. The unit tests reveal that all sensors employed in the experiments exhibit an average error rate of less than 5%. When assessed as an integrated system, the three-phase induction motor performance monitoring system demonstrates robust functionality, effectively measuring critical variables such as voltage, current, power factor, temperature, vibration, and rotational speed.

Average Age of Sensing in Wireless Powered Sensor Networks

Average Age of Sensing in Wireless Powered Sensor Networks

Calibrating the global network of gravitational wave observatories via laser power calibration at NIST and PTB

Current gravitational wave (GW) observatories rely on photon calibrators that use laser radiation pressure to generate displacement fiducials used to calibrate detector output signals. Reducing calibration uncertainty enables optimal extraction of astrophysical information such as source distance and sky position from detected signals. For the ongoing O4 observing run that started on 24 May 2023, the global GW detector network is employing a new calibration scheme with transfer standards calibrated at both the National Institute of Standards and Technology (NIST) and the Physikalisch-Technische Bundesanstalt (PTB). These transfer standards will circulate between the observatories and the metrology institutes to provide laser power calibration traceable to the International System of Units (SI) and enable assessment and reduction of relative calibration errors for the observatory network. The Laser Interferometer Gravitational-Wave Observatory (LIGO) project and the Virgo project are currently participating in the new calibration scheme. The Large-scale Cryogenic Gravitational-wave Telescope project (KAGRA) is expected to join in 2024, with the LIGO Aundha Observatory in India joining later. Before implementing this new scheme, a NIST-PTB bilateral comparison was conducted. It validated the scale representation by both laboratories, with a degree of equivalence of −0.2% and an associated expanded uncertainty of 0.32% (k = 2) which is significantly lower than previous studies. We describe the transfer of power sensor calibration, including detailed uncertainty estimates, from the transfer standards calibrated by NIST and PTB to the sensors operating continuously at the interferometer end stations. Finally, we discuss the ongoing calibration of Pcal-induced displacement fiducials for the O4 observing run. Achieved combined standard uncertainty levels as low as 0.3% facilitate calibrating the interferometer output signals with sub-percent accuracy.

Viscosity-activated carbon dots for noninvasive disease diagnosis, therapeutic efficacy assessment and anticancer drug screening

Abnormal levels of intracellular microviscosity have a close relationship with some diseases and pathologies. Therefore, quantitative imaging of viscosity at the cellular and organ levels is beneficial for disease diagnosis, curative effect evaluation, and anticancer drug screening. Herein, we synthesized viscosity-activated orange-red emitting carbon dots (named as LP-CDs) from lignin and p-phenylenediamine using a one-step method, whose fluorescence intensity, lifetime, and quantum yield significantly increase with rising viscosity. Meanwhile, the fluorescence intensity of LP-CDs also has a good linear relationship with the solution viscosity. Taking the advantages of high sensitivity, noninvasiveness, and rapid result output of fluorescence imaging, LP-CDs can visualize the cellular viscosity changes induced by inflammation and hyperglycemia, and image tumor evolution. More importantly, LP-CDs can be used as powerful sensors to screen anticancer drugs in vivo by evaluating therapeutic effects. This work provides a new scheme for constructing robust nanoprobes to achieve the diagnosis and imaging-guided surgery of viscosity related diseases.