Sensor Array Research Articles

Enhancing cider quality through co-fermentation with acid protease and esterase-producing Metschnikowia species and Saccharomyces cerevisiae.

To date, cider production has primarily relied on Saccharomyces cerevisiae. Introducing novel non-Saccharomyces yeasts can enhance the diversity of cider properties. Among these, the Metschnikowia genus stands out for its ability to produce hydrolytic enzymes that may impact the sensorial and technological properties of cider. This study focused on evaluating the impact of three Metschnikowia species - Metschnikowia koreensis (Mk), M. reukaufii (Mr), and M. pulcherrima (Mp) - which exhibit acid protease and esterase activity, on the quality enhancement of cider. The research findings indicate that the overall quality of cider produced through co-fermentation with these species surpassed that of cider fermented with mono-fermentation of S. cerevisiae (Sc). The cider fermented with the Sc + Mk combination exhibited the lowest levels of harsh-tasting malic acid and higher levels of softer lactic acid. Sensory array analysis also demonstrated that the Sc + Mk fermented cider exhibited high sensor response values for compounds contributing to a complex overall olfactory composition and richness. Furthermore, the Sc + Mk fermented cider exhibited the highest total quantity and variety of volatile organic compounds (VOCs). Specifically, the concentrations of phenethyl alcohol, 3-methyl-1-butanol, ethyl octanoate, and decanoic acid were notably elevated in comparison with other groups. This study illustrates that Metschnikowia species, particularly M. koreensis, show significant potential as starters for cider due to their various technological properties, including acidity modulation, aroma enhancement, and color improvement. The findings of this study provide a foundation for improving cider quality by co-fermenting S. cerevisiae with innovative starter cultures. © 2024 Society of Chemical Industry.

Lamb wave based damage imaging in variable thickness plates

The researches on Lamb wave based damage localization for constant thickness plates have been widely investigated in the field of structural health monitoring, while the issue of Lamb wave based damage imaging in variable thickness plates is rarely addressed. This is because the Lamb wave propagation characteristics in plates with varying thickness is more complex than that with constant thickness. In this study, a Lamb wave based damage imaging method using a sensor array is proposed for damage localization in variable thickness plates. In the process of point-by-point imaging within the interested imaging area, a signal back-propagation operator based on a Lamb wave propagation model can deal with the problem of dispersion compensation for Lamb waves propagating in variable thickness plates. The Lamb wave propagation model, which is based on the assumption that Lamb waves propagating in a variable thickness plate can be seen as that propagating in a sequence of short and constant thickness plates with different thicknesses, can predict Lamb wave response waveform in variable thickness plates. Numerical simulations and experiments on aluminum plates with linearly varying thicknesses are implemented, and the imaging results validate the effectiveness of the proposed method.

Imaging of Volatile Organic Compounds Using a Single Nanowire-Based Electronic Nose for Future Biomedical Applications.

This study introduces an array of semiconductor oxide single nanowires fabricated using advanced semiconductor processing techniques, including electron beam lithography and thin-film deposition, which is well-suited for large-scale nanowire integration. A four-channel nanowire array consisting of tin oxide (SnO2), indium oxide (In2O3), ferric oxide (Fe3O4), and titanium oxide (TiO2) was developed. As a proof of concept, we converted the response curves of the sensor array to heat maps, enabling comprehensive feature representation. The fabricated electronic nose (E-nose) was utilized to detect three types of volatile organic compounds (VOCs), with the results visualized in a heat map format. Additionally, the performance of each individual sensor was quantitatively studied, highlighting the array's potential for enhanced gas detection and analysis. To further illustrate the interaction between gas molecules and the nanowires, we visualized the gas response results by mapping the sensor's signal changes. These visualizations provide a clear representation of how different gas molecules interact with specific nanowires. For example, the heat maps reveal distinct response patterns for each type of VOC, allowing for the identification and differentiation of gases based on their unique signatures. This visualization technique not only enhances the understanding of gas-nanowire interactions but also demonstrates the effectiveness of the E-nose in distinguishing between various VOCs. The SnO2 nanowire gas sensor showed enhanced gas response compared to other materials. The SnO2 and TiO2 gas sensors showed enhanced response (62 and 56 s) and recovery times (100 and 37 s).

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.

Enhanced Colorimetric Detection of Volatile Organic Compounds Using a Dye-Incorporated Photonic Crystal-Based Sensor Array.

Chemoresponsive dyes offer the potential to selectively detect volatile organic compounds (VOCs) unique to certain disease states. Among different VOC sensing techniques, colorimetric sensing offers the advantage of facile recognition. However, it is often challenging to discern the color changes by the naked eye. Here, highly sensitive colorimetric VOC sensor arrays from dye-incorporated colloidal photonic crystals (dye-cPhCs) are reported. cPhCs are scalably fabricated on a 4-inch wafer by spin-coating of silica nanoparticles (NPs) dispersed in a photo-cross-linkable monomer, where the gradient shear flow along the film thickness creates densely-packed square arrays of NPs in the top layers, whereas the bulk is quasi-amorphous with larger periodicities. The broadened reflection peak allows for augmented dye absorption originating from the overlap between the photonic bandgap edge of the cPhC and the dye absorption peak, leading to a more noticeable color change upon exposure to VOCs. The sensor array generates distinct color difference maps for acetaldehyde, acetone, and acetic acid, respectively, without any data amplification. The limit of detection for acetaldehyde, acetone, and acetic acid is 1, 0.1, and 0.02ppm, respectively. Moreover, VOC can be diagonalized by visually intuitive pattern recognition, and principal component analysis at reduced dimensionality is demonstrated.

A colorimetric sensor array consisted of CaxMn1-xWO4 with high oxidase-like activity to detect antioxidants

Antioxidant detection is highly significant in practical applications. This study establishes an optical sensor array capable of simultaneous and convenient detection of multiple antioxidants in real samples. CaxMn1-xWO4 (CMW) synthesized via hydrothermal method with abundant surface oxygen vacancies and high internal electron transfer efficiency exhibits strong oxidase-like activity, which can oxidize TMB to blue product oxTMB using oxygen as reaction substrate. The presence of antioxidants weakens TMB oxidation, leading to a lighter blue color in the system. Therefore, CMW can be employed for colorimetric detection of antioxidants. Then CMW samples with varying Ca to Mn ratios were prepared, followed by the establishment of an optical sensor array. The detection systems were photographed, and PCA analysis of the RGB parameters was performed, enabling simultaneous qualitative and quantitative detection of nine kinds of antioxidants.

Flexible Artificial Tactility with Excellent Robustness and Temperature Tolerance Based on Organohydrogel Sensor Array for Robot Motion Detection and Object Shape Recognition.

Hydrogel-based flexible artificial tactility is equipped to intelligent robots to mimic human mechanosensory perception. However, it remains a great challenge for hydrogel sensors to maintain flexibility and sensory performances during cyclic loadings at high or low temperatures due to water loss or freezing. Here, a flexible robot tactility is developed with high robustness based on organohydrogel sensor arrays with negligent hysteresis and temperature tolerance. Conductive polyaniline chains are interpenetrated through a poly(acrylamide-co-acrylic acid) network with glycerin/water mixture with interchain electrostatic interactions and hydrogen bonds, yielding a high dissipated energy of 1.58MJ m-3, and ultralow hysteresis during 1000 cyclic loadings. Moreover, the binary solvent provides the gels with outstanding tolerance from -100 to 60°C and the organohydrogel sensors remain flexible, fatigue resistant, conductive (0.27 S m-1), highly strain sensitive (GF of 3.88) and pressure sensitive (35.8 MPa-1). The organohydrogel sensor arrays are equipped on manipulator finger dorsa and pads to simultaneously monitor the finger motions and detect the pressure distribution exerted by grasped objects. A machine learning model is used to train the system to recognize the shape of grasped objects with 100% accuracy. The flexible robot tactility based on organohydrogels is promising for novel intelligent robots.

Nanocomposite Multimodal Sensor Array Integrated with Auxetic Structure for an Intelligent Biometrics System.

A multimodal sensor array, combining pressure and proximity sensing, has attracted considerable interest due to its importance in ubiquitous monitoring of cardiopulmonary health- and sleep-related biometrics. However, the sensitivity and dynamic range of prevalent sensors are often insufficient to detect subtle body signals. This study introduces a novel capacitive nanocomposite proximity-pressure sensor (NPPS) for detecting multiple human biometrics. NPPS consists of a carbon nanotube-paper composite (CPC) electrode and a percolating multiwalled carbon nanotube (MWCNT) foam enclosed in a MWCNT-coated auxetic frame. The fractured fibers in the CPC electrode intensify an electric field, enabling highly sensitive detection of proximity and pressure. When pressure is applied to the sensor, the synergic effect of MWCNT foam and auxetic deformation amplifies the sensitivity. The simple and mass-producible fabrication protocol allows for building an array of highly sensitive sensors to monitor human presence, sleep posture, and vital signs, including ballistocardiography (BCG). With the aid of a machine learning algorithm, the sensor array accurately detects blood pressure (BP) without intervention. This advancement holds promise for unrestricted vital sign monitoring during sleep or driving.

A Multifunctional Tactile Sensory System for Robotic Intelligent Identification and Manipulation Perception.

Humans recognize and manipulate objects relying on the multidimensional force features captured by the tactile sense of skin during the manipulation. Since the current sensors integrated in robots cannot support the robots to sense the multiple interaction states between manipulator and objects, achieving human-like perception and analytical capabilities remains a major challenge for service robots. Prompted by the tactile perception involved in robots performing complex tasks, a multimodal tactile sensory system is presented to provide in situ simultaneous sensing for robots when approaching, touching, and manipulating objects. The system comprises a capacitive sensor owning the high sensitivity of 1.11E-2pFmm-1, a triboelectricity nanogenerator with the fast response speed of 30ms, and a pressure sensor array capable of 3D force detection. By Combining transfer learning models, which fuses multimodal tactile information to achieve high-precision (up to 95%) recognition of the multi-featured targets such as random hardness and texture information under random sampling conditions, including random grasp force and velocity. This sensory system is expected to enhance the intelligent recognition and behavior-planning capabilities of autonomous robots when performing complex tasks in undefined surrounding environments.

Geometrically asymmetric pressure sensor with precious few electrodes achieves precise detection of both position and strength by machine learning

Wearable devices that can accurately detect the position and strength of applied pressure have been a focus of attention in the field of flexible tactile sensing. However, conventional sensor arrays with multiple units have many challenges in practical applications due to their complex circuit layouts and poor robustness, etc., which could be solved by minimizing the number of connecting electrodes among each unit. Herein, a pressure sensor with geometrically asymmetric structure has been proposed to remove all the connected inner electrodes. The sensor is constructed by three pieces of fabrics, with one insulating fabric sandwiched by two conductive fabrics coated with carbon nanotubes. Notably, the bottom fabric is specially designed in an optimized asymmetric shape with a rectangular hole located off-center of the square, assisting the sensor to generate different resistive and capacitive responses among pre-divided nine areas through only three external electrodes. Compared to conventional resistive sensor arrays, the number of electrodes is reduced by 83.3 %. With the assistance of machine learning, pressure-positioning with an accuracy of more than 98.1 % and precise strength measurements are achieved. Based on this sensor and the trained neural network, a two-level password system is constructed for more reliable role recognition. The asymmetric strategy proposed in this work provides a novel design for realizing precise detection of both position and strength using extremely simplified circuits, which has great potential in the field of wearable electronics.

Experimental and Numerical Investigation of Acoustic Emission Source Localization Using an Enhanced Guided Wave Phased Array Method.

To detect damage in mechanical structures, acoustic emission (AE) inspection is considered as a powerful tool. Generally, the classical acoustic emission detection method uses a sparse sensor array to identify damage and its location. It often depends on a pre-defined wave velocity and it is difficult to yield a high localization accuracy for complicated structures using this method. In this paper, the passive guided wave phased array method, a dense sensor array method, is studied, aiming to obtain better AE localization accuracy in aluminum thin plates. Specifically, the proposed method uses a cross-shaped phased array enhanced with four additional far-end sensors for AE source localization. The proposed two-step method first calculates the real-time velocity and the polar angle of the AE source using the phased array algorithm, and then solves the location of the AE source with the additional far-end sensor. Both numerical and physical experiments on an aluminum flat panel are carried out to validate the proposed method. It is found that using the cross-shaped guided wave phased array method with enhanced far-end sensors can localize the coordinates of the AE source accurately without knowing the wave velocity in advance. The proposed method is also extended to a stiffened thin-walled structure with high localization accuracy, which validates its AE source localization ability for complicated structures. Finally, the influences of cross-shaped phased array element number and the time window length on the proposed method are discussed in detail.

A Next-Generation Iot-Integrated Arduino System for Intelligent Home Environment Monitoring

To stay informed about home living conditions, a home environment monitoring device was developed using an Arduino platform. Utilizing the Arduino UNO hardware and an array of sensors, the device captures real-time data on indoor temperature, humidity, light levels, and the presence of combustible gases. This information is then uploaded to a monitoring platform, allowing for adjustments to the surrounding environment based on the collected data. Testing has demonstrated that the design effectively gathers and reliably uploads home environment data, meeting the requirements for remote monitoring of living conditions. The device offers several advantages, including low cost, minimal power consumption, a simple structure, and easy installation. DOI: https://doi.org/10.52783/pst.797

Magnetic Sensor Array for Electric Arc Reconstruction in Circuit Breakers.

Noninvasive imaging of circuit breakers under short-circuit testing is addressed by recording the magnetic field produced over an array of external sensors and by solving an inverse problem to identify the causing current distribution. The temporal and spatial resolution of the sensing chain are studied and implemented in a physical set-up. A wire model is adopted to describe electrical current distribution. Additionally, the simpler, more direct approach to evaluating the passage of electric current in front of sensors is proposed. The dynamics of suitable approximating models of the electric arc that forms across contacts is obtained and agrees with multi-physical simulations and with experimental time histories of current and voltage. The two methods are flexible and allow the analysis of different types of circuit breakers.

A wireless multiparameter cryogenic monitoring method using passive backscatter sensors

In this paper, we proposed the first wireless multiparameter monitoring method for cryogenic environments (−196 ℃). Present-day cryogenic industries implement a large array of wired sensors to monitor key parameters in fabrication processes. The wirings for a large sensor array greatly complicate the monitoring system. We presented the first wireless multiparameter monitoring method for cryogenic environments by making two efforts. First, a novel wireless passive vibration and pressure sensor is developed. We transfer vibrations and pressures to a relative displacement between a magnet and a tunnel magnetoresistor (TMR). This affects the magnetic field at the TMR and changes the TMR’s resistance. By integrating the TMR on a backscattering antenna, the antenna’s wireless return loss is modulated by both vibrations and pressures. We proposed a decoupling method for simultaneously monitoring vibration and pressure by a single sensor. Second, we demonstrated and evaluated the first wireless multiparameter cryogenic monitoring system. A wireless passive temperature sensor is developed by integrating a resistance temperature detector (RTD) on a backscattering antenna. The developed vibration and pressure sensor and temperature sensor are tuned to separate frequency bands, embedded in cryogenic environments, and calibrated in situ. Vibrations, pressures, and temperatures in cryogenic environments are synchronously obtained by our wireless cryogenic monitoring system, with an accuracy of 80∼90 %. This research provides a wireless option in large-scale automated cryogenic monitoring, thus it is desirable in implementing the Internet of Things in cryogenic industries.

Wavelength-dependent photoelectrochemical sensor array based on Bi2WO6/TiO2 electrospun nanoheterojunction for multiple antioxidants identification and total antioxidant capacity analysis in natural tea

Evaluating total antioxidant capacity (TAC) have been playing a vital role in health guidelines, the analysis of nutritional composition in foods, and the diagnosis and treatment of oxidative stress-induced disease. However, numerous antioxidants with similar physicochemical properties make them very difficult to be discriminated because of the similarly analytical signal output. In this work, a novel wavelength-dependent and three-channel photoelectrochemical (PEC) sensor array based on Bi2WO6/Vo-TiO2 electrospun nanostructures was established for the identification of antioxidants in natural teas. The formation of heterojunction interfaces between Bi2WO6 nanosheet and Vo-TiO2 NFs, and the introduction of the oxygen vacancies in TiO2 NFs promoted the visible light absorption ability and electron transfer rate of Bi2WO6/Vo-TiO2 NFs, causing the differences of photocurrent response to different antioxidants under different wavelengths. Furthermore, excited wavelengths, antioxidant types, and photocurrent were regarded as three-factors of fingerprint to accurately discriminate five antioxidants via linear discriminant analysis (LDA). Finally, the PEC sensor array was able to estimate TAC in five actual tea samples (green, yellow, black, white, and dark teas). This development of LDA-assisted multiplex PEC fingerprinting sensor array showed a good promise for the identification and authentication of agricultural products, and the establishment of the database.

Dual-Wavelength LiDAR with a Single-Pixel Detector Based on the Time-Stretched Method.

In the fields of agriculture and forestry, the Normalized Difference Vegetation Index (NDVI) is a critical indicator for assessing the physiological state of plants. Traditional imaging sensors can only collect two-dimensional vegetation distribution data, while dual-wavelength LiDAR technology offers the capability to capture vertical distribution information, which is essential for forest structure recovery and precision agriculture management. However, existing LiDAR systems face challenges in detecting echoes at two wavelengths, typically relying on multiple detectors or array sensors, leading to high costs, bulky systems, and slow detection rates. This study introduces a time-stretched method to separate two laser wavelengths in the time dimension, enabling a more cost-effective and efficient dual-spectral (600 nm and 800 nm) LiDAR system. Utilizing a supercontinuum laser and a single-pixel detector, the system incorporates specifically designed time-stretched transmission optics, enhancing the efficiency of NDVI data collection. We validated the ranging performance of the system, achieving an accuracy of approximately 3 mm by collecting data with a high sampling rate oscilloscope. Furthermore, by detecting branches, soil, and leaves in various health conditions, we evaluated the system's performance. The dual-wavelength LiDAR can detect variations in NDVI due to differences in chlorophyll concentration and water content. Additionally, we used the radar equation to analyze the actual scene, clarifying the impact of the incidence angle on reflectance and NDVI. Scanning the Red Sumach, we obtained its NDVI distribution, demonstrating its physical characteristics. In conclusion, the proposed dual-wavelength LiDAR based on the time-stretched method has proven effective in agricultural and forestry applications, offering a new technological approach for future precision agriculture and forest management.

Object classification through heterogeneous fog with a fast data-driven algorithm using a low-cost single-photon avalanche diode array

This study presents a framework for classifying a wooden mannequin’s poses using a single-photon avalanche diode (SPAD) array in dynamic and heterogeneous fog conditions. The target and fog generator are situated within an enclosed fog chamber. Training datasets are continuously collected by configuring the temporal and spatial resolutions on the sensor's firmware, utilizing a low-cost SPAD array sensor priced below $5, consisting of an embedded SPAD array and diffused VCSEL laser. An extreme learning machine (ELM) is trained for rapid pose classification, as a benchmark against CNN. We quantitatively justify the selection of nodes in the hidden layer to balance the computing speed and accuracy. Results demonstrate that ELM can accurately classify mannequin poses when obscured by dynamic heavy fog to 35 cm away from the sensor, enabling real-time applications in consumer electronics. The proposed ELM achieves 90.65% and 89.58% accuracy in training and testing, respectively. Additionally, we demonstrate the robustness of both ELM and CNN as the fog density increases. Our study also discusses the sensor’s current optical limitations and lays the groundwork for future advancements in sensor technology.

In‐Sensor Touch Analysis for Intent Recognition

AbstractTactile intent recognition systems, which are highly desired to satisfy human's needs and humanized services, shall be accurately understanding and identifying human's intent. They generally utilize time‐driven sensor arrays to achieve high spatiotemporal resolution, however, which encounter inevitable challenges of low scalability, huge data volumes, and complex processing. Here, an event‐driven intent recognition touch sensor (IR touch sensor) with in‐sensor computing capability is presented. The merit of event‐driven and in‐sensor computing enables the IR touch sensor to achieve ultrahigh resolution and obtain complete intent information with intrinsic concise data. It achieves critical signal extraction of action trajectories with a rapid response time of 0.4 ms and excellent durability of >10 000 cycles, bringing an important breakthrough of tactile intent recognition. Versatile applications prove the integrated functions of the IR touch sensor for great interactive potential in all‐weather environments regardless of shading, dynamics, darkness, and noise. Unconscious and even hidden action features can be perfectly extracted with the ultrahigh recognition accuracy of 98.4% for intent recognition. The further auxiliary diagnostic test demonstrates the practicability of the IR touch sensor in telemedicine palpation and therapy. This groundbreaking integration of sensing, data reduction, and ultrahigh‐accuracy recognition will propel the leapfrog development for conscious machine intelligence.

Recent Advances in Synthesis and Applications of Organic Ionic Saltsbased Sensor Arrays

Abstract: Sensor arrays contain a group of sensors, improve observations with new dimensions, provide better estimations, and additional parameters in comparison to the individual selective sensor. The array-based sensing technique provides good performance to respond to various gaseous or liquid analytes. Room temperature ionic liquids (RTILs) (melting point <25oC) and Group of uniform materials based on organic salts (GUMBOS) (melting point =25-250oC) are organic ionic salts, composed of an oppositely charged pair of bulky organic cations and bulky organic/inorganic anion and shows interesting tunable physicochemical properties. In this review article, we will discuss the sensing performance of ILs- and GUMBOS-based sensor arrays. ILs-based sensor arrays have been used in electrochemical gas sensing, solvent discrimination, colorimetric gas sensing, sensing of organic compounds, optoelectronic sensing of vapors and solutions, and vapour sensing through IL/QCM systems. GUMBOS-based sensor arrays have been employed in vapour sensing through the GUMBOS/QCM method, detection, and discrimination of proteins.

Machine learning-assisted colorimetric sensor array for rapid identification of adulterated Panax notoginseng powder

Panax notoginseng (PN) is a popular functional food worldwide, yet adulterated PN powders are prevalent in the commercial market. Herein, a method combining a low-cost colorimetric sensor array and machine learning is proposed to rapidly identify and quantify adulterated PN powder. We constructed a sensitive indicator displacement assay (IDA) sensor array by innovatively using Job's plot to optimize sensor units. The array was used to identify adulterated PN powders after its discriminatory ability was evaluated by amino acid analysis. Among the four machine learning models, the support vector machine (SVM) models achieved the highest accuracy, with above 98.3% for authenticity and 99.0% for adulteration types. The blending percentages of four PN adulterants were further analyzed quantitatively using support vector machine regression (SVR) models with good prediction ability (R > 0.93). Finally, our sensor array method was applied to identify commercially available PN powders with satisfactory results. This study offers a low-cost new method for the rapid identification of PN powder, contributing to quality control of powdered food.