Commercial Sensors Research Articles

Performing hardness classification using diffusive memristor based artificial neurons

Abstract Artificial neurons and synapses are the building blocks for constructing a neuromorphic system such as Spiking Neural Network (SNN) or Artificial Neural Network (ANN). Recently, there has been tremendous interest in using memristors to develop neuromorphic technologies that can be used in advanced SNNs and ANNs. Memristors, because of their simple device structure, easy and high-density fabrication, and integration with other semiconductor electronics are suitable candidates for the construction of neuromorphic concepts. However, not much has been discussed about using memristors for the development of sensors that can be utilized for object- classification especially their rigidity, shape and structure. In this article, we propose the application of memristors, specifically silver nanoparticle based diffusive memristor, in conjunction with a piezoelectric sensor within a robotics gripper, serving as one receptor (a tactile sensor) that triggers neuron circuitry with memristors to generate spikes. Furthermore, to perform hardness classification, we utilized various objects to collect data and generated multiple spikes corresponding to each object. This data was then utilized with a machine learning algorithm. The outcomes were compared with the accuracy of commercial FSR tactile sensors. Our approach demonstrated the capability of diffusive memristors in generating neuron spikes from tactile stimuli for hardness classification, achieving accuracy ranging from 82% to 100% during the validation of 20% test data across various algorithms, while the FSR sensors achieved an accuracy range of 95% to 98%.

Space Debris In-Orbit Detection with Commercial Automotive LiDAR Sensors

This article presents an alternative approach to detecting and mapping space debris in low Earth orbit by utilizing commercially available automotive LiDAR sensors mounted on CubeSats. The main objective is to leverage the compact size, low weight, and minimal power consumption of these sensors to create a “Large Cosmic LiDAR” (LCL) system. This LCL system would operate similarly to a giant radar circling the Earth, with strategically positioned LiDAR sensors along the target orbit. The article examines the feasibility of this concept by analyzing the relative orbital velocity between the sensor and debris objects, and calculating the time required to scan a complete orbit.

Custom chromatic confocal microscope for surface profilometry

In this work, a surface profiling system is introduced, employing a custom-designed chromatic confocal (CC) distance sensor. Today’s commercial sensors based on the CC principle have a major drawback of effectively coupling light from the illumination source into the optical imaging system, mainly due to etendue mismatch. Consequently, a substantial amount of light is lost, significantly reducing the overall SNR of the system. This limitation restricts its applicability primarily to highly reflective surfaces. To address this issue, an etendue-matched optical system is proposed, that incorporates an innovative superluminescent light source. Through this approach, the system achieves 4 orders of magnitudes higher illumination intensity concentrated within the focus spot compared to existing devices in the current market.

Non-Destructive Pest Detection: Innovations and Challenges in Sensing Airborne Semiochemicals.

Pests, especially invasive ones, pose significant threats to the global ecosystem, crop security, and agriculture economy. Sensing airborne semiochemicals as a nondestructive detection method has been recognized as a promising strategy to detect the presence of these living pests on site. However, sensing airborne semiochemicals in fields is challenging, as they are transmitted in concentrations as low as several nanograms per cubic meter in chemically diverse environments. This low vapor pressure together with similarity in functional groups of pheromones among different species have curtailed the practical deployment of corresponding sensors for real world applications. This review describes the advances in semiochemical detection methods and technologies including traditional analytical instruments, trained animals, and electroantennography with a focus on electronic noses (e-noses). Several key types of volatile organic compound (VOC) sensors used in e-noses are summarized, including their transduction methods, sensing materials, and sensing performance for semiochemical and simulants detection. Notably, it was found that many commercial VOC sensors failed to respond to airborne semiochemicals effectively, leading to a reduced efficiency of e-noses. Future work may focus on developing stable and robust sensing materials with higher sensitivity and selectivity to pheromones and understanding the feasibility of the deployment of the sensors under field conditions.

Thermal Runaway Warning of Lithium Battery Based on Electronic Nose and Machine Learning Algorithms

Characteristic gas detection can be an efficient way to predict the degree of thermal runaway of a lithium battery. In this work, a sensor array consisting of three commercial MOS sensors was employed to discriminate between three target gases, CO, H2 and a mixture of the two, which are characteristic gases released during the thermal runaway of lithium batteries. In this work, an integrated model that makes the classification stage results one of the feature inputs for the concentration regression stage was employed, successfully reducing the RMSE of the concentration regression results. In addition, we also explored the influence of the selection of the response time length on the classification and regression tasks, achieving the best results in a short time through the optimum algorithm. To assess the impact of time duration sensor data on the results, we selected four time windows of different length and extracted the corresponding sensor response data for subsequent processing. Initially, principal component analysis (PCA) was used to visualise the clustering of the three target gas samples at room temperature, providing a preliminary data analysis. For the classification phase, we chose three classification algorithms—MLP (Multilayer Perceptron), ELM (Extreme Learning Machine), and SVM (Support Vector Machine)—and performed a comprehensive comparison of their classification and generalisation abilities using grid search for hyperparameter optimisation and five-fold cross-validation. The results demonstrated that MLP achieved 99.23% classification accuracy during the 20 s response period. In the concentration regression phase, we combined the classification results with the raw features to create a new feature set, which was then input into a multi-output MLP regression model. The root mean square error (RMSE) employing the new feature set was used to measure the prediction error. Ultimately, the findings showed that the input of combined features significantly reduced the regression error for the mixed gas.

High‐Accuracy Liquid Flow Monitoring via Triboelectric Nanogenerator Combined with Bionic Design and Common‐Mode Interference Suppression

AbstractIn the development of smart cities, accurate liquid flow monitoring is essential for the efficient operation of water supply systems. Current flow sensors often face limitations in sensitivity and environmental adaptability, affecting measurement accuracy, and restricting their application in smart city infrastructure. To address these challenges, this study proposes a high‐accuracy flow monitoring method. Specifically, by combining the bionic design with advanced signal processing techniques, the sensitivity and anti‐interference ability are improved, respectively, to enhance the measurement accuracy. Based on this method, a self‐powered flow sensor (SPFS) is developed using noncontact triboelectric nanogenerators (NC‐TENGs) as the sensing unit. The SPFS achieves a sensitivity of 2.07 Hz L−1 min−1 and improves the signal‐to‐noise ratio by more than 13 times over the initial sensing signal. In addition, an intelligent system is developed to accurately measure water resources. The maximum flow rate error rate is less than 0.97% compared to commercial flow sensors. The SPFS demonstrates higher sensitivity and accuracy compared to the existing TENG flow sensors. This study addresses the limitations of existing flow sensors and pioneers a novel solution for enhanced water resource management in smart cities.

In situ generation of cuprous oxide in metal-organic framework for significantly enhanced room-temperature ammonia sensing: Synergy enabling excellent sensitivity and selectivity

Development of effective strategies to improve the response sensitivity of metal-organic frameworks (MOFs) to target gases is imperative to further enhancing their application potential in gas sensing. In this work, series of CuHHTP/Cu2O (HHTP = 2,3,6,7,10,11-hexahydroxytriphenylene) composites are successfully constructed by in-situ electrochemical reduction treatment of CuHHTP, and the changing trend of first increasing and then decreasing with increase of CuHHTP electrochemical reduction degree for their sensitivity to NH3 detection is revealed. When the average size of Cu2O nanoparticles generated in CuHHTP is 3.6 nm, the corresponding CuHHTP/Cu2O-A composite exhibits the best NH3 sensing performance compared to pristine CuHHTP, including significantly enhanced response sensitivity, ultralow experimental detection limit of 10 ppb, superior selectivity, good reproducibility, and excellent response stability even after placed for 90 days. More impressively, thanks to integration and synergy of the uniformly-dispersed small-sized Cu2O nanoparticles with significantly enhanced adsorption activity and capacity for NH3, the released large amount of uncoordinated hydroxyl groups that can interact with NH3, as well as the active sites of the CuHHTP substrate itself, the 10 ppb experimental detection limit for the CuHHTP/Cu2O-A-based sensors is not only the lowest one reported in the past 10 years for NH3 sensors based on copper-containing MOF and oxide materials, but also superior to that of 0.1–1 ppm for the existing commercial NH3 sensors. This work opens the avenue for design and development of novel MOF-based sensing materials.

Development and Field Test of Integrated Electronics Piezoelectric Accelerometer Based on Lead-Free Piezoelectric Ceramic for Centrifugal Pump Monitoring.

In this study, an Integrated Electronics Piezoelectric (IEPE)-type accelerometer based on an environmentally friendly lead-free piezoceramic was fabricated, and its field applicability was verified using a cooling pump owned by the Korea Atomic Energy Research Institute (KAERI). As an environmentally friendly piezoelectric material, 0.96(K,Na)NbO3-0.03(Bi,Na,K,Li)ZrO3-0.01BiScO3 (0.96KNN-0.03BNKLZ-0.01BS) piezoceramic with an optimized piezoelectric charge constant (d33) was introduced. It was manufactured in a ring shape using a solid-state reaction method for application to a compression mode accelerometer. The fabricated ceramic ring has a high piezoelectric constant d33 of ~373 pC/N and a Curie temperature TC of ~330 °C. It was found that the electrical and physical characteristics of the 0.96KNN-0.03BNKLZ-0.01BS piezoceramic were comparable to those of a Pb(Zr,Ti)O3 (PZT) ring ceramic. As a result of a vibration test of the IEPE accelerometer fabricated using the lead-free piezoelectric ceramic, the resonant frequency fr = 20.0 kHz and voltage sensitivity Sv = 101.1 mV/g were confirmed. The fabricated IEPE accelerometer sensor showed an excellent performance equivalent to or superior to that of a commercial IEPE accelerometer sensor based on PZT for general industrial use. A field test was carried out to verify the applicability of the fabricated sensor in an actual industrial environment. The test was conducted by simultaneously installing the developed sensor and a commercial PZT-based sensor in the ball bearing housing location of a centrifugal pump. The centrifugal pump was operated at 1180 RPM, and the generated vibration signals were collected and analyzed. The test results confirmed that the developed eco-friendly lead-free sensor has comparable vibration measurement capability to that of commercial PZT-based sensors.

Relation between measured noise levels and traffic flow

Road-traffic flow parameters are a critical input to urban noise pollution evaluation frameworks such as CNOSSOS-EU. The accuracy and resolution of the traffic flow improves the quality of the noise exposure assessment. Highly resolved traffic flow within an urban network is difficult to reliably measure or predict. The quality and availability of such traffic information may be improved using data from low-cost noise sensors that are mounted beside representative segments of the road network. A model is presented, which relates the noise levels measured at a single noise sensor with the local traffic flow conditions. The noise data is obtained from a noise sensor mounted alongside a road, which is part of a test-bed in Stockholm, Sweden. The local traffic flow is obtained from commercial vehicle-counting sensors mounted alongside the noise sensor. Effect of seasonality is observed when comparing models trained on different subsets of the training data. The effect of seasonality on the noise levels is investigated.

High-sensitivity acceleration sensor detecting micro-mechanomyogram and deep learning approach for parkinson’s disease classification

High-sensitivity acceleration sensors have been independently developed by our research group to detect vibrations that are > 10 dB smaller than those detected by conventional commercial sensors. This study is the first to measure high-frequency micro-vibrations in muscle fibers, termed micro-mechanomyogram (MMG) in patients with Parkinson’s disease (PwPD) using a high-sensitivity acceleration sensor. We specifically measured the extensor pollicis brevis muscle at the base of the thumb in PwPD and healthy controls (HC) and detected not only low-frequency MMG (< 15 Hz) but also micro-MMG (≥ 15 Hz), which was preciously undetectable using commercial acceleration sensors. Analysis revealed remarkable differences in the frequency characteristics of micro-MMG between PwPD and HC. Specifically, during muscle power output, the low-frequency MMG energy was greater in PwPD than in HC, while the micro-MMG energy was smaller in PwPD compared to HC. These results suggest that micro-MMG detected by the high-sensitivity acceleration sensor provides crucial information for distinguishing between PwPD and HC. Moreover, a deep learning model trained on both low-frequency MMG and micro-MMG achieved a high accuracy (92.19%) in classifying PwPD and HC, demonstrating the potential for a diagnostic system for PwPD using micro-MMG.

Dynamics of Bubble Plumes Produced by Breaking Waves

Abstract Bubble plumes play a significant role in the air–sea interface by influencing processes such as air–sea gas exchange, aerosol production, modulation of oceanic carbon and nutrient cycles, and the vertical structure of the upper ocean. Using acoustic Doppler current profiler (ADCP) data collected off the west coast of Ireland, we investigate the dynamics of bubble plumes and their relationship with sea state variables. In particular, we describe the patterns of bubble plume vertical extension, duration, and periodicity. We establish a power-law relationship between the average bubble penetration depth and wind speed, consistent with previous findings. Additionally, the study reveals a significant association between whitecapping coverage and observed acoustic volume backscatter intensity, underscoring the role of wave breaking in bubble plume generation. The shape of the probability distribution of bubble plume depths reveals a transition toward stronger and more organized bubble entrainment events during higher wind speeds. Furthermore, we show that deeper bubble plumes are associated with turbulent Langmuir number Lat ∼ 0.3, highlighting the potential role of Langmuir circulation on the transport and deepening of bubble plumes. These results contribute to a better understanding of the complex interactions between ocean waves, wind, and bubble plumes, providing valuable insights for improving predictive models and enhancing our understanding of air–sea interactions. Significance Statement This research contributes to understanding bubble plume dynamics in the upper ocean and their relationship with sea state variables. The establishment of a power-law relationship between the bubble penetration depth and wind speed, along with the association between whitecapping coverage and acoustic backscatter intensity, contributes to improved predictive capabilities for air–sea interactions and carbon dioxide exchange. The identification of the potential influence of Langmuir circulation on bubble plume dynamics expands our understanding of the role of coherent circulations in transporting bubble plumes. Additionally, this study presents a clear methodology using commercial sensors such as an ADCP, which can be easily replicated by researchers worldwide, leading to potential advancements in our comprehension of bubble plume dynamics.

Near-field terahertz electro-optical imaging based on a polarization image sensor

Abstract This paper presents a hyperspectral microscopy system that offers two-dimensional (2D) measurement of the spectral phase and amplitude information of terahertz (THz) radiation without the need for raster scanning. To achieve this, a new THz imaging method is introduced, wherein the distribution of the THz electric field is spatially measured using the electro-optic effect with a commercial polarization image sensor. This method enables the direct measurement of polarization components, eliminating the need for the polarization optics usually required in conventional electro-optical imaging. The performance of this imaging method is compared with a conventional 2D imaging system based on a standard visible camera. Finally, the sub-wavelength resolution capabilities of this new sensor are demonstrated by imaging a sample in the near field.

Continuous Microreactor‐Based Synthesis of SnO2 Nanoparticles for Sensor Applications

Tin oxide nanoparticles are well‐established materials with a wide range of applications, including optoelectronic devices and solid‐state gas sensors. Conventional synthesis methods of these systems are often based on batch processes. In this study, we compare batch and continuous synthesis methods for tin dioxide nanoparticles using precipitation and sol‐gel processes. For the continuous processes we applied the so called microjet reactor method. The nanoparticles were characterized by TEM, elemental analysis and XRD and exhibited particle sizes of 1.7‐3.0 nm and crystallite sizes of 1.7‐2.3 nm, consisting of tetragonal (P42/mnm) and orthorhombic (Pbcn) phases. We have evaluated different post‐synthesis purification methods to remove impurities such as chlorides and carbon‐hydrogen species. Each purification method exhibited unique advantages and side effects, providing insight into selecting the most appropriate method for specific applications. We also demonstrated the potential of these SnO2 nanoparticles as ethanol gas sensing materials and compared their performance with a commercial sensor.

A Multimodal Bracelet to Acquire Muscular Activity and Gyroscopic Data to Study Sensor Fusion for Intent Detection.

Researchers have attempted to control robotic hands and prostheses through biosignals but could not match the human hand. Surface electromyography records electrical muscle activity using non-invasive electrodes and has been the primary method in most studies. While surface electromyography-based hand motion decoding shows promise, it has not yet met the requirements for reliable use. Combining different sensing modalities has been shown to improve hand gesture classification accuracy. This work introduces a multimodal bracelet that integrates a 24-channel force myography system with six commercial surface electromyography sensors, each containing a six-axis inertial measurement unit. The device's functionality was tested by acquiring muscular activity with the proposed device from five participants performing five different gestures in a random order. A random forest model was then used to classify the performed gestures from the acquired signal. The results confirmed the device's functionality, making it suitable to study sensor fusion for intent detection in future studies. The results showed that combining all modalities yielded the highest classification accuracies across all participants, reaching 92.3±2.6% on average, effectively reducing misclassifications by 37% and 22% compared to using surface electromyography and force myography individually as input signals, respectively. This demonstrates the potential benefits of sensor fusion for more robust and accurate hand gesture classification and paves the way for advanced control of robotic and prosthetic hands.

Multi-pass, single-molecule nanopore reading of long protein strands

The ability to sequence single protein molecules in their native, full-length form would enable a more comprehensive understanding of proteomic diversity. Current technologies, however, are limited in achieving this goal1,2. Here, we establish a method for the long-range, single-molecule reading of intact protein strands on a commercial nanopore sensor array. By using the ClpX unfoldase to ratchet proteins through a CsgG nanopore3,4, we provide single-molecule evidence that ClpX translocates substrates in two-residue steps. This mechanism achieves sensitivity to single amino acids on synthetic protein strands hundreds of amino acids in length, enabling the sequencing of combinations of single-amino-acid substitutions and the mapping of post-translational modifications, such as phosphorylation. To enhance classification accuracy further, we demonstrate the ability to reread individual protein molecules multiple times, and we explore the potential for highly accurate protein barcode sequencing. Furthermore, we develop a biophysical model that can simulate raw nanopore signals a priori on the basis of residue volume and charge, enhancing the interpretation of raw signal data. Finally, we apply these methods to examine full-length, folded protein domains for complete end-to-end analysis. These results provide proof of concept for a platform that has the potential to identify and characterize full-length proteoforms at single-molecule resolution.

A microfluidic patch for wireless wearable electrochemical detection of sweat metabolites

Wearable sweat sensors provide an innovative detection method for analyzing human physiological and biochemical parameters and play an important role in detecting the potential risks of chronic diseases. Existing commercial wearable sensors (Apple iWatch, HUAWEI smart band) have problems such as single detection index (heart rate or blood pressure), and mainly focus on the physical information. This work studied an integrated microfluidic patch to simultaneously detect several biomarkers (pH, sodium ions, and uric acid) in sweat. We achieved the detection of pH level, sodium, and uric acid in linear ranges of 4–8, 0–160 mM, and 5–160 μM, respectively, covering well the clinically relevant ranges in the healthy human sweat. Laser engraving was chosen to prepare a microfluidic layer, and the sweat collection function was realized by combining hydrophilic and hydrophobic microfluidic synergy. It could efficiently collect the required sweat sample volume (80 μL) within 30 s while avoiding the contamination caused by directly contact with the skin. In addition, a wireless signal acquisition and transmission system was designed with the signal reading, processing, and wireless transmission functions, which can display the testing results in real time on Android phone-specific application software. We combined the wireless system with a microfluidic patch for on-body testing to verify the good wearability of the sweat sensor. This research realized the wireless data processing and transmission while improving sweat collection efficiency. In addition, it could detect various sweat physiological components in real-time, and provides a multiparameter, real-time, and portable sensing platform.

Crack-Based Composite Flexible Sensor with Superhydrophobicity to Detect Strain and Vibration.

Vibration sensors are widely applied in the detection of faults and analysis of operational states in engineering machinery and equipment. However, commercial vibration sensors with a feature of high hardness hinder their usage in some practical applications where the measured objects have irregular surfaces that are difficult to install. Moreover, as the operating environments of machinery become increasingly complex, there is a growing demand for sensors capable of working in wet and humid conditions. Here, we present a flexible, superhydrophobic vibration sensor with parallel microcracks. The sensor is fabricated using a femtosecond laser direct writing ablation strategy to create the parallel cracks on a PDMS film, followed by spray-coating with a conductive ink composed of MWCNTs, CB, and PDMS. The results demonstrate that the developed flexible sensor exhibits a high-frequency response of up to 2000 Hz, a high acceleration response of up to 100 m/s2, a water contact angle as high as 159.61°, and a linearity of 0.9812 between the voltage signal and acceleration. The results indicate that the sensor can be employed for underwater vibration, sound recognition, and vibration monitoring in fields such as shield cutters, holding significant potential for mechanical equipment vibration monitoring and speech-based human-machine interaction.

Rapid and Cost-Effective Fabrication and Performance Evaluation of Force-Sensing Resistor Sensors

In this study, we developed a cost-effective and rapid method for fabricating force-sensing resistor (FSR) sensors as an alternative to commercial force sensors. Our aim was to achieve performance characteristics comparable to existing commercial products while significantly reducing costs and fabrication time. We analyzed the material composition of two widely used commercial force sensors: Interlink FSR-402 and Flexiforce A201-1. Based on this analysis, we selected 4B and 9B pencils, which contain high concentrations of graphite, and silicone sealant to replicate these material properties. The fabrication process involved creating piezoresistive sheets by shading A4 copy paper with 4B and 9B pencils to form a uniform layer of graphite. Additionally, we prepared a mixture of 9B pencil lead powder and silicone sealant, ensuring a consistent application on the paper substrate. Measurement results indicated that the force sensor fabricated using a mixture of 9B pencil powder and silicone sealant exhibited electrical and mechanical characteristics closely resembling those of commercial sensors. Load tests revealed that the hand-made sensors provided a proportional voltage output in response to increasing and decreasing loads, similar to commercial FSR sensors. These results suggest that our fabrication method can produce reliable and accurate FSR sensors suitable for various applications, including wearable technology, robotics, and force-sensing interfaces. Overall, this study demonstrates the potential for creating cost-effective and high-performance FSR sensors using readily available materials and simple fabrication techniques.

Design and Performance Evaluation of an In Situ Online Soil Electrical Conductivity Sensor Prototype Based on the High-Performance Integrated Chip AD5941

Soil electrical conductivity has an important influence on the growth and development of plants. The existing real-time soil electrical conductivity detection device is affected by temperature, inconvenient to use, expensive, etc.; therefore, based on the classical four-terminal method of soil electrical conductivity detection principle, in this study, we aim to improve the limitations of the constant current source, selecting the high-performance integrated chip AD5941, optimizing the detection circuit and probe structure, improving the achievability of the detection circuit, and designing a type of in situ on-line real-time access to a soil electrical conductivity detection device, and improve the detection accuracy by temperature compensation. In this paper, dynamic performance, steady state performance, radial sensitivity range, and calibration test are carried out for the soil electrical conductivity detection prototype. The test results show that the dynamic response speed of the prototype is less than 50 ms, the steady state error is not more than ±2%, and the radial measurement sensitivity range is 8~10 cm. A comparison with the commercial sensor shows that the linear fit of the two measurements reaches 0.9995, and the absolute error ranges from −61.40 µS/cm to 23.90 µS/cm, with a relative error range of −1.94~1.86%. It shows that the performance of the two sensors is comparable, but the quality/price ratio of the prototype is much higher than that of the commercialized product. In this study, it is demonstrated that a high-precision, low-cost, and easy-to-use in situ online soil electrical conductivity detection device can be provided for agricultural and forestry production.

Adjusting the surface quality of printed components via controlling dispersant content to improve the electrical performance of DLP-printed PZT ceramics and devices

This paper aims to enhance the surface quality of digital light processing (DLP) printed lead zirconate titanate (PZT) components by controlling the content of BYK-111 dispersant, thereby improving the electrical properties of the resulting printed piezoelectric ceramics and devices. 55 vol% high solid loading PZT slurries with different dispersant contents were prepared and assessed for their dispersion stability, rheological behavior and curing characteristics. The slurry containing 1 wt% BYK-111 demonstrated ideal rheological properties, notably achieving a low viscosity of 1.37 Pa·s at 100 s−1. The surface roughness of the printed ceramic component was reduced to 69.3 nm, representing a 57.1 % improvement in surface quality compared to other dispersant levels. The printed PZT ceramic exhibited superior piezoelectric performance, with a piezoelectric constant d33 reaching 445 pC/N, surpassing most existing 3D-printed macro-sized piezoelectric ceramics. Furthermore, a DLP-printed 1–3 piezoelectric array was encapsulated to fabricate an acoustic emission (AE) sensor, which achieved a low substrate noise level of 31 dB and a high signal-to-noise ratio of 62 dB, outperforming commercial sensors. The optimal BYK-111 content not only ensures favorable dispersion stability and rheological properties in the slurry, but also significantly enhances the surface quality and density of the printed ceramic components, ultimately leading to the production of high-performance piezoelectric ceramics and devices.