Sensor Array Research Articles

Classification and identification of perfume scents by an enantioselective colorimetric sensor array of chiral metal-organic-framework-based Fabry-Pérot films.

Many odors, like perfumes, are complex mixtures of chiral and achiral molecules where the cost-efficient (enantio-)selective sensing represents a major technical challenge. Here, we present a colorimetric sensor array of surface-mounted metal-organic-framework (SURMOF) films in Fabry-Pérot (FP) cavities. The optical properties of the FP-SURMOF films with different chiral and achiral structures are affected by the (enantio-)selective adsorption of the analytes in the SURMOF pores, resulting in different responses to the analyte molecules. The read-out of the sensor array is performed by the digital camera of a common smartphone, where the RGB values are determined. By analyzing the sensor array data with simple machine learning algorithms, the analytes are discriminated. After demonstrating the enantioselective response for a pair of pure chiral odor molecules, we apply the sensor array to detect and discriminate a large number (16) of common commercial perfumes and eau de toilettes. While our untrained human nose is not able to discriminate all perfumes, the presented colorimetric sensor array can classify all perfumes with great classification accuracy. Moreover, the sensor array was used to identify unlabeled samples correctly. We foresee such an FP-chiral-SURMOF-based sensor array as a powerful approach toward inexpensive selective odors sensing applications.

Machine-Learning-Assisted Aggregation-Induced Emissive Nanosilicon-Based Sensor Array for Point-of-Care Identification of Multiple Foodborne Pathogens.

How timely identification and determination of pathogen species in pathogen-contaminated foods are responsible for rapid and accurate treatments for food safety accidents. Herein, we synthesize four aggregation-induced emissive nanosilicons with different surface potentials and hydrophobicities by encapsulating four tetraphenylethylene derivatives differing in functional groups. The prepared nanosilicons are utilized as receptors to develop a nanosensor array according to their distinctive interactions with pathogens for the rapid and simultaneous discrimination of pathogens. By coupling with machine-learning algorithms, the proposed nanosensor array achieves high performance in identifying eight pathogens within 1 h with high overall accuracy (93.75-100%). Meanwhile, Cronobacter sakazakii and Listeria monocytogenes are taken as model bacteria for the quantitative evaluation of the developed nanosensor array, which can successfully distinguish the concentration of C. sakazakii and L. monocytogenes at more than 103 and 102 CFU mL-1, respectively, and their mixed samples at 105 CFU mL-1 through the artificial neural network. Moreover, eight pathogens at 1 × 104 CFU mL-1 in milk can be successfully identified by the developed nanosensor array, indicating its feasibility in monitoring food hazards.

Sparse Optimal Design of an Ultrasonic Sensor Array for Fast TFM Based on a Discrete Slime Mold Algorithm

Sparse Optimal Design of an Ultrasonic Sensor Array for Fast TFM Based on a Discrete Slime Mold Algorithm

All-solution-processed ultraflexible wearable sensor enabled with universal trilayer structure for organic optoelectronic devices.

All-solution-processed organic optoelectronic devices can enable the large-scale manufacture of ultrathin wearable electronics with integrated diverse functions. However, the complex multilayer-stacking device structure of organic optoelectronics poses challenges for scalable production. Here, we establish all-solution processes to fabricate a wearable, self-powered photoplethysmogram (PPG) sensor. We achieve comparable performance and improved stability compared to complex reference devices with evaporated electrodes by using a trilayer device structure applicable to organic photovoltaics, photodetectors, and light-emitting diodes. The PPG sensor array based on all-solution-processed organic light-emitting diodes and photodetectors can be fabricated on a large-area ultrathin substrate to achieve long storage stability. We integrate it with a large-area, all-solution-processed organic solar module to realize a self-powered health monitoring system. We fabricate high-throughput wearable electronic devices with complex functions on large-area ultrathin substrates based on organic optoelectronics. Our findings can advance the high-throughput manufacture of ultrathin electronic devices integrating complex functions.

Robust Tensor-Based DOA and Polarization Estimation in Conformal Polarization Sensitive Array with Bad Data

Partially impaired sensor arrays pose a significant challenge in accurately estimating signal parameters. The occurrence of bad data is highly probable, resulting in random loss of source information and substantial performance degradation in parameter estimation. In this paper, a tensor variational sparse Bayesian learning (TVSBL) method is proposed for the estimate of direction of arrival (DOA) and polarization parameters jointly based on a conformal polarization sensitive array (CPSA), taking into account scenarios with the partially impaired sensor array. First, a sparse tensor-based received data model is developed for CPSAs that incorporates bad data. Then, a column vector detection method is proposed to diagnose the positions of the impaired sensors. In scenarios involving partially impaired sensor arrays, a low-rank matrix completion method is employed to recover the random loss of signal information. Finally, variational sparse Bayesian learning (VSBL) and minimum eigenvector methods are utilized sequentially to obtain the DOA and polarization parameters estimation, successively. Furthermore, the Cramér-Rao bound is given for the proposed method. Simulation results validated the effectiveness of the proposed method.

Color-Shifting Iontronic Skin for On-Site, Nonpixelated Pressure Mapping Visualization.

Mimicking the function of human skin is highly desired for electronic skins (e-skins) to perceive the tactile stimuli by both their intensity and spatial location. The common strategy using pixelated pressure sensor arrays and display panels greatly increases the device complexity and compromises the portability of e-skins. Herein, we tackled this challenge by developing a user-interactive iontronic skin that simultaneously achieves electrical pressure sensing and on-site, nonpixelated pressure mapping visualization. By merging the electrochromic and iontronic pressure sensing units into an integrated multilayer device, the interlayer charge transfer is regulated by applied pressure, which induces both color shifting and a capacitance change. The iontronic skin could visualize the trajectory of dynamic forces and reveal both the intensity and spatial information on various human activities. The integration of dual-mode pressure responsivity, together with the scalable fabrication and explicit signal output, makes the iontronic skin highly promising in biosignal monitoring and human-machine interaction.

A terahertz meta-sensor array for 2D strain mapping

Large-scale stretchable strain sensor arrays capable of mapping two-dimensional strain distributions have gained interest for applications as wearable devices and relating to the Internet of Things. However, existing strain sensor arrays are usually unable to achieve accurate directional recognition and experience a trade-off between high sensing resolution and large area detection. Here, based on classical Mie resonance, we report a flexible meta-sensor array that can detect the in-plane direction and magnitude of preloaded strains by referencing a dynamically transmitted terahertz (THz) signal. By building a one-to-one correspondence between the intrinsic electrical/magnetic dipole resonance frequency and the horizontal/perpendicular tension level, arbitrary strain information across the meta-sensor array is accurately detected and quantified using a THz scanning setup. Particularly, with a simple preparation process of micro template-assisted assembly, this meta-sensor array offers ultrahigh sensor density (~11.1 cm−2) and has been seamlessly extended to a record-breaking size (110 × 130 mm2), demonstrating its promise in real-life applications.

A Modular Soft Sensing Skin for Fast Measurement of Wing Deformation in Small Unmanned Aerial Vehicles.

Insects, bats, and small birds show outstanding flight performance even under complex atmospheric conditions, which is partially due to the ability of these natural fliers to sense and react to disturbances quickly. These biological systems often use large numbers of sensors arrayed across their bodies to detect disturbances, but previous efforts to use large arrays of sensors in engineered fliers have typically resulted in slow responses due to the need to scan and process data from the large number of sensors. To address the challenges of capturing disturbances in a large sensing array with low latency, this work proposes and demonstrates a modular soft sensing system to quickly detect disturbances in small unmanned aerial vehicles. A large array of soft strain sensors with high sensing resolution covers the entire wingspan, providing rich information on wing deformation. Owing to the modular design, decentralized computation enables the sensing system to efficiently manage sensor data, resulting in sufficiently fast sampling to capture wing dynamics while all 32 sensors embedded in the modular soft sensing skin are used. This hardware architecture also results in significantly reduced noise in the sensing system, leading to a high signal-to-noise ratio. These methods can ultimately enable fast and reliable control of both soft and rigid robotic systems using large arrays of soft sensors.

Flexible calorimetric flow sensor with unprecedented sensitivity and directional resolution for multiple flight parameter detection

The accurate perception of multiple flight parameters, such as the angle of attack, angle of sideslip, and airflow velocity, is essential for the flight control of micro air vehicles, which conventionally rely on arrays of pressure or airflow velocity sensors. Here, we present the estimation of multiple flight parameters using a single flexible calorimetric flow sensor featuring a sophisticated structural design with a suspended array of highly sensitive vanadium oxide thermistors. The proposed sensor achieves an unprecedented velocity resolution of 0.11 mm·s−1 and angular resolution of 0.1°. By attaching the sensor to a wing model, the angles of attack and slip were estimated simultaneously. The triaxial flight velocities and wing vibrations can also be estimated by sensing the relative airflow velocity due to its high sensitivity and fast response. Overall, the proposed sensor has many promising applications in weak airflow sensing and flight control of micro air vehicles.

Food for Thought: Optical Sensor Arrays and Machine Learning for the Food and Beverage Industry.

Arrays of cross-reactive sensors, combined with statistical or machine learning analysis of their multivariate outputs, have enabled the holistic analysis of complex samples in biomedicine, environmental science, and consumer products. Comparisons are frequently made to the mammalian nose or tongue and this perspective examines the role of sensing arrays in analyzing food and beverages for quality, veracity, and safety. I focus on optical sensor arrays as low-cost, easy-to-measure tools for use in the field, on the factory floor, or even by the consumer. Novel materials and approaches are highlighted and challenges in the research field are discussed, including sample processing/handling and access to significant sample sets to train and test arrays to tackle real issues in the industry. Finally, I examine whether the comparison of sensing arrays to noses and tongues is helpful in an industry defined by human taste.

Fluorescent multichannel sensor array based on three carbon dots derived from Tibetan medicine waste for the quantification and discrimination of multiple heavy metal ions in water.

Afluorescent multichannel sensor array has been establishedbased on three carbon dots derived from Tibetan medicine waste for rapid quantification and discrimination of six heavy metal ions. Due to the chelation between metal ions and carbon dots (CDs), this fluorescence "turn off" mode sensing array can quantify six metal ions as low as "μM" level. Moreover, the six heavy metal ions display varying quenching effects on these three CDs owing to diverse chelating abilities between each other, producing differential fluorescent signals for three sensing channels, which can be plotted as specific fingerprints and converted into intuitive identification profiles via principal component analysis (PCA) and hierarchical cluster analysis (HCA) technologies to accurately distinguish Cu2+, Fe3+, Mn2+, Ag+, Ce4+, and Ni2+ with the minimum differentiated concentration of 5μM. Valuably, this sensing array unveils good sensitivity, exceptional selectivity, ideal stability, and excellent anti-interference ability for both mixed standards and actual samples. Our contribution provides a novel approach for simultaneous determination of multiple heavy metal ions in environmental samples, and it will inspire the development of other advanced optical sensing array for simultaneous quantification and discrimination of multiple targets.

Flexible Multimodal Magnetoresistive Sensors Based on Alginate/Poly(vinyl alcohol) Foam with Stimulus Discriminability for Soft Electronics Using Machine Learning.

Flexible foam-based sensors have attracted substantial interest due to their high specific surface area, light weight, superior deformability, and ease of manufacture. However, it is still a challenge to integrate multimodal stimuli-responsiveness, high sensitivity, reliable stability, and good biocompatibility into a single foam sensor. To achieve this, a magnetoresistive foam sensor was fabricated by an in situ freezing-polymerization strategy based on the interpenetrating networks of sodium alginate, poly(vinyl alcohol) in conjunction with glycerol, and physical reinforcement of core-shell bidisperse magnetic particles. The assembled sensor exhibited preferable magnetic/strain-sensing capability (GF ≈ 0.41 T-1 for magnetic field, 4.305 for tension, -0.735 for bending, and -1.345 for pressing), quick response time, and reliable durability up to 6000 cycles under external stimuli. Importantly, a machine learning algorithm was developed to identify the encryption information, enabling high recognition accuracies of 99.22% and 99.34%. Moreover, they could be employed as health systems to detect human physiological motion and integrated as smart sensor arrays to perceive external pressure/magnetic field distributions. This work provides a simple and ecofriendly strategy to fabricate biocompatible foam-based multimodal sensors with potential applications in next-generation soft electronics.

Formation of Anisotropic Conducting Interlayer for High-Resolution Epidermal Electromyography Using Mixed-Conducting Particulate Composite.

Epidermal electrophysiology is a non-invasive method used in research and clinical practices to study the electrical activity of the brain, heart, nerves, and muscles. However, electrode/tissue interlayer materials such as ionically conducting pastes can negatively affect recordings by introducing lateral electrode-to-electrode ionic crosstalk and reducing spatial resolution. To overcome this issue, biocompatible, anisotropic-conducting interlayer composites (ACI) that establish an electrically anisotropic interface with the skin are developed, enabling the application of dense cutaneous sensor arrays. High-density, conformable electrodes are also microfabricated that adhere to the ACI and follow the curvilinear surface of the skin. The results show that ACI significantly enhances the spatial resolution of epidermal electromyography (EMG) recording compared to conductive paste, permitting the acquisition of single muscle action potentials with distinct spatial profiles. The high-density EMG in developing mice, non-human primates, and humans is validated. Overall, high spatial-resolution epidermal electrophysiology enabled by ACI has the potential to advance clinical diagnostics of motor system disorders and enhance data quality for human-computer interface applications.

Modal acoustic emission-based circumferential crack feature extractions for pipeline welds with L-shaped flexible sensor array

ABSTRACT The collection and analysis of acoustic emission (AE) from weld circumferential cracks are crucial for ensuring pipeline safety. However, the unclear propagation characteristics and mode features of AE present challenges in array design and mode identification. In this manuscript, a novel mode feature extraction method for circumferential crack AE signals is proposed. This method involves three primary steps: firstly, a L-shaped flexible sensor array is designed to capture raw AE signals from both axial and circumferential directions. Next, the collected signals are filtered and evaluated to extract effective multi-mode components through the Kalman filtering and recursive plot (RP). Finally, the time–frequency features and mode types are extracted through combining the Hilbert-Huang transform (HHT) and dispersion curves. Results indicate that during the crack growths, both axial and circumferential direction AE signals contain multi-mode components, specifically L(0,1), F(1,1) and F(1,2) modes, with durations spanning 30–500 μs. Additionally, the axial modes predominantly occur within 200–300 kHz range, whereas the circumferential modes span both low and high frequency bands, specifically 40–50 kHz and 200–300 kHz, respectively. Modes across different frequencies indicate distinct structural behaviours, including crack growths and plastic deformations. The proposed method provides attempts for the pipe weld AE monitoring and multi-mode analysis.

Multi-Layer Polyurethane-Fiber-Prepared Entangled Strain Sensor with Tunable Sensitivity and Working Range for Human Motion Detection

The entanglement of fibers can form physical and topological structures, with the resulting bending and stretching strains causing localized changes in pressure. In this study, a multi-layer polyurethane-fiber-prepared (MPF) sensor was developed by coating the CNT/PU sensing layer on the outside of an elastic electrode through a wet-film method. The entangled topology of two MPFs was utilized to convert the stretching strain into localized pressure at the contact area, enabling the perception of stretching strain. The influence of coating mechanical properties and surface structure on strain sensing performance was investigated. A force regulator was introduced to regulate the mechanical properties of the entangled topology of MPF. By modifying the thickness and length proportion of the force regulator, the sensitivity factor and sensitivity range of the sensor could be controlled, achieving a high sensitivity factor of up to 127.74 and a sensitivity range of up to 58%. Eight sensors were integrated into a sensor array and integrated into a dance costume, successfully monitoring the multi-axis motion of the dancer’s lumbar spine. This provides a new approach for wearable biomechanical sensors.

A Dynamic Range Preservation Readout Integrated Circuit for Multi-Gas Sensor Array Applications

This study introduces a readout integrated circuit (ROIC) tailored for multi-gas sensor arrays featuring a proposed baseline calibration scheme aimed at mitigating the issue of sensor baseline variation. Unlike previous approaches, the proposed scheme stores each sensor’s baseline value and dynamically updates the signal extraction range accordingly during ROIC operation. This adjustment allows for the optimal use of the ROIC’s dynamic range, enhancing sensor uniformity and accuracy without the need for complex additional circuitry or advanced post-processing algorithms. We fabricated a prototype ROIC using a 180 nm CMOS process, achieving a low power consumption of 0.43 mW and a conversion rate of 50 kSPS. The prototype boasts an integrated noise level of 9.9 μVRMS across a frequency range of 0.1 Hz to 5 kHz and a dynamic range of 142.6 dB, coupled with superior linearity, indicated by a maximum integral non-linearity (INL) of −75.71 dB. This design significantly reduces sensor offset scattering to within 1 LSB of the A/D reference scale. In this study, the efficacy of the proposed scheme was validated using Figaro TGS-2600. The ROIC targets a sensitivity range from 0.54 to 0.23 for gas concentrations ranging from 5 ppm to 20 ppm and a resolution of 39 Ω for sensor resistance range from 10 kΩ to 90 kΩ. The enhancements in performance make the proposed ROIC a promising solution for precise gas concentration detection in sensor applications.

Enantiopure Corral[4]BINOLs as Ultrastrong Receptors for Recognition and Differential Sensing of Steroids.

The precise recognition and sensing of steroids, a type of vital biomolecules, hold immense practical value across various domains. In this study, we introduced corral[4]BINOLs (C[4]BINOLs), a pair of enantiomeric conjugated deep-cavity hosts, as novel synthetic receptors for binding steroids. Due to the strong hydrophobic effect of their deep nonpolar, chiral cavities, the two enantiomers of C[4]BINOLs demonstrated exceptionally high recognition affinities (up to 1012 M-1) for 16 important steroidal compounds as well as good enantioselectiviy (up to 15.5) in aqueous solutions, establishing them as the most potent known steroid receptors. Harnessing their ultrahigh affinity, remarkable enantioselectivity, and fluorescence emission properties, the two C[4]BINOL enantiomers were employed to compose a fluorescent sensor array which achieved discrimination and sensing of 16 structurally similar steroids at low concentrations.

Enantiopure Corral[4]BINOLs as Ultrastrong Receptors for Recognition and Differential Sensing of Steroids

The precise recognition and sensing of steroids, a type of vital biomolecules, hold immense practical value across various domains. In this study, we introduced corral[4]BINOLs (C[4]BINOLs), a pair of enantiomeric conjugated deep‐cavity hosts, as novel synthetic receptors for binding steroids. Due to the strong hydrophobic effect of their deep nonpolar, chiral cavities, the two enantiomers of C[4]BINOLs demonstrated exceptionally high recognition affinities (up to 1012 M−1) for 16 important steroidal compounds as well as good enantioselectiviy (up to 15.5) in aqueous solutions, establishing them as the most potent known steroid receptors. Harnessing their ultrahigh affinity, remarkable enantioselectivity, and fluorescence emission properties, the two C[4]BINOL enantiomers were employed to compose a fluorescent sensor array which achieved discrimination and sensing of 16 structurally similar steroids at low concentrations.

Real-Time 3D Routing Optimization for Unmanned Aerial Vehicle using Machine Learning

In the realm of Unmanned Aerial Vehicles (UAVs) for civilian applications, the surge in demand has underscored the need for sophisticated technologies. The integration of Unmanned Aerial Systems (UAS) with Artificial Intelligence (AI) has become paramount to address challenges in urban environments, particularly those involving obstacle collision risks. These UAVs are equipped with advanced sensor arrays, incorporating LiDAR and computer vision technologies. The AI algorithm undergoes comprehensive training on an embedded machine, fostering the development of a robust spatial perception model. This model enables the UAV to interpret and navigate through the intricate urban landscape with a human-like understanding of its surroundings. During mission execution, the AI-driven perception system detects and localizes objects, ensuring real-time awareness. This study proposes an innovative real-time three-dimensional (3D) path planner designed to optimize UAV trajectories through obstacle-laden environments. The path planner leverages a heuristic A* algorithm, a widely recognized search algorithm in artificial intelligence. A distinguishing feature of this proposed path planner is its ability to operate without the need to store frontier nodes in memory, diverging from conventional A* implementations. Instead, it relies on relative object positions obtained from the perception system, employing advanced techniques in simultaneous localization and mapping (SLAM). This approach ensures the generation of collision-free paths, enhancing the UAV's navigational efficiency. Moreover, the proposed path planner undergoes rigorous validation through Software-In-The-Loop (SITL) simulations in constrained environments, leveraging high-fidelity UAV dynamics models. Preliminary real flight tests are conducted to assess the real-world applicability of the system, considering factors such as wind disturbances and dynamic obstacles. The results showcase the path planner's effectiveness in providing swift and accurate guidance, thereby establishing its viability for real-time UAV missions in complex urban scenarios.

A photoactivated gas sensor of SnO2 ordered spherical pore array: Electrodeposition-based construction and application in room temperature detection of ethanol gas

The photoactivated semiconductor film gas sensor has received increasing attention in the room temperature detection of environmental gases, but the uncontrolled surface morphology and the associated light area still limit the application and the exploitation of the sensing response potential. Herein, a monolayer colloid crystal template strategy based on electrodeposition was introduced to in situ construct SnO2 ordered spherical macropore array (OSPA) sensors used for ethanol gas detection under a UV-LED. By adjusting the electrodeposition time, various OSPAs with regularly variable heights of pore-wall were obtained. The light area of the array could then be systematically regulated based on the spherical pore-walls’ curve characteristics. The OSPAs showed a nearly 5-fold change in response in detection of 100 ppm ethanol gas under a 265 nm UV-LED, corresponding to the light area from small to large. The highest sensing response is even higher than that of almost all SnO2-based room temperature sensors that have previously been reported. Moreover, the sensor has excellent selectivity and linearity, a 1 ppm lower detection limit, and exceptional long-term stability. These finds are corroborated by analyses of the electrodeposition mechanism, the sensing mechanism, optical simulations, and various characterizations. The strategy provides an efficient way to tune and enhance the sensing response of a photoactivated gas sensor, and is beneficial for exploiting the sensing potential of a semiconductor.