Sensor For Recognition Research Articles

Nanofluidic ion rectification sensor for enantioselective recognition and detection

Nanofluidic ion rectification sensor for enantioselective recognition and detection

Bioinspired Photoluminescent "Spider Web" as Ultrafast and Ultrasensitive Airflow-Acoustic Bimodal Sensor for Human-Computer Interaction and Intelligent Recognition.

Nature provides massive biomimetic design inspiration for constructing structural materials with desired performances. Spider webs can perceive vibrations generated by airflow and acoustic waves from prey and transfer the corresponding information to spiders. Herein, by mimicking the perception capability and structure of a spider web, an ultrafast and ultrasensitive airflow-acoustic bimodal sensor (HOF-TCPB@SF) is developed based on the postfunctionalization of hydrogen-bonded organic framework (HOF-TCPB) on silk film (SF) through hydrogen bonds. The "spider web-like" HOF-TCPB@SF possesses light weight and high elasticity, endowing this airflow sensor with superior properties including an ultralow detection limit (DL, 0.0076 m s-1), and excellent repeatability (480 cycles). As an acoustic sensor, HOF-TCPB@SF exhibits ultrahigh sensitivity (105140.77 cps Pa-1 cm-2) and ultralow DL (0.2980 dB), with the greatest response frequency of 375 Hz and the ability to identify multiple sounds. Moreover, both airflow and acoustic sensing processes show an ultrafast response speed (40 ms) and multiangle recognition response (0-180°). The perception mechanisms of airflow and acoustic stimuli are analyzed through finite element simulation. This bimodal sensor also achieves real-time airflow monitoring, speech recognition, and airflow-acoustic interoperability based on human-computer interaction, which holds great promise for applications in health care, tunnel engineering, weather forecasting, and intelligent textiles.

KOALA: A Modular Dual-Arm Robot for Automated Precision Pruning Equipped with Cross-Functionality Sensor Fusion

Landscape maintenance is essential for ensuring agricultural productivity, promoting sustainable land use, and preserving soil and ecosystem health. Pruning is a labor-intensive task among landscaping applications that often involves repetitive pruning operations. To address these limitations, this paper presents the development of a dual-arm holonomic robot (called the KOALA robot) for precision plant pruning. The robot utilizes a cross-functionality sensor fusion approach, combining light detection and ranging (LiDAR) sensor and depth camera data for plant recognition and isolating the data points that require pruning. The You Only Look Once v8 (YOLOv8) object detection model powers the plant detection algorithm, achieving a 98.5% pruning plant detection rate and a 95% pruning accuracy using camera, depth sensor, and LiDAR data. The fused data allows the robot to identify the target boxwood plants, assess the density of the pruning area, and optimize the pruning path. The robot operates at a pruning speed of 10–50 cm/s and has a maximum robot travel speed of 0.5 m/s, with the ability to perform up to 4 h of pruning. The robot’s base can lift 400 kg, ensuring stability and versatility for multiple applications. The findings demonstrate the robot’s potential to significantly enhance efficiency, reduce labor requirements, and improve landscape maintenance precision compared to those of traditional manual methods. This paves the way for further advancements in automating repetitive tasks within landscaping applications.

E-waste driven eco-friendly highly sensitive wearable capacitive sensors for physical activity monitoring and sign language recognition

Owing to the continual and rapid advancement in technology, there has been a significant surge in electronic waste (e-waste) in recent decades, resulting in a harmful impact on our environment. Due to the growing demand for battery-powered devices, non-rechargeable batteries are a significant source of e-waste. This study describes fabricating a paper-based graphite strain sensor that is eco-friendly, inexpensive, biodegradable, and sensitive to the applied strain. Readily accessible materials are employed to design and fabricate MIDC strain sensors. The Modified Interdigital Capacitor (MIDC) structure is formed on basic printing paper, and the conductive substance made with the ink of a gel pen and graphite from a drained dry cell is painted on the paper and used as electrode material. The suggested sensor has a high gauge factor value of 1998.578 and fast response and recovery time of 112 ms and 100 ms, respectively. The proposed prototype has also been utilized to inspect fitness exercises and physical activities on the human body, inspiring the potential for its integration into wearable systems for various applications. Reliable detection and differentiation of finger positions are achieved, highlighting the sensor's potential for more advanced sign language applications. It’s a good option to integrate the suggested strain sensor in wearable systems, such as telemedicine systems, electromechanical detection, healthcare, and physical activity monitoring of humans.

Manufacturing strategies for highly sensitive and self-powered piezoelectric and triboelectric tactile sensors

Abstract Piezoelectric and triboelectric effects are of growing interest for facilitating high-sensitivity and self-powered tactile sensor applications. The working principles of piezoelectric and triboelectric nanogenerators provide strategies for enhancing output voltage signals to achieve high sensitivity. Increasing the piezoelectric constant and surface triboelectric charge density are key factors in this enhancement. Methods such as annealing processes, doping techniques, grain orientation controls, crystallinity controls, and composite structures can effectively enhance the piezoelectric constant. For increasing triboelectric output, surface plasma treatment, charge injection, microstructuring, control of dielectric constant, and structural modification are effective methods. The fabrication methods present significant opportunities in tactile sensor applications. This review article summarizes the overall piezoelectric and triboelectric fabrication processes from materials to device aspects. It highlights applications in pressure, touch, bending, texture, distance, and material recognition sensors. The conclusion section addresses challenges and research opportunities, such as limited flexibility, stretchability, decoupling from multi-stimuli, multifunctional sensors, and data processing.

Construction of a Defective Chiral Covalent Organic Framework for Fluorescence Recognition of Amino Acids.

The design and synthesis of chiral covalent organic frameworks (COFs) with controlled defect sites are highly desirable but still remain largely unexplored. Herein, we report the synthesis of a defective chiral HD-TAPB-DMTP COF by modifying the chiral monomer helicid (HD) into the framework of an achiral imine-linked TAPB-DMTP COF using a chiral monomer exchange strategy. Upon the introduction of the chiral HD unit, the obtained defective chiral HD-TAPB-DMTP COF not only displays excellent crystallinity, large specific surface area (up to 2338 m2/g) and rich accessible chiral functional sites but also exhibits fluorescence emission, rendering it a good candidate for discrimination of amino acids. Notably, the resultant defective chiral HD-TAPB-DMTP COF can be used as a fluorescent sensor for enantioselective recognition of both tyrosine and phenylalanine enantiomers in water, showing enhanced fluorescent responses for the L conformations over those of the D conformations with the enantioselectivity factors being 1.84 and 2.02, respectively. Moreover, molecular docking simulations uncover that stronger binding affinities between chiral HD-TAPB-DMTP COF and L-tyrosine/L-phenylalanine in comparison to those with D-tyrosine/D-phenylalanine play important roles in enantioselective determination. This work provides new insights into the design and construction of highly porous defective chiral COFs for enantioselective fluorescence recognition of amino acids.

Application of low energy building thermal energy optimization based on image recognition and light sensors in urban green environment planning

The paper analyzes the application of thermal energy optimization scheme based on image recognition and light sensor technology in low-energy buildings, in order to improve building energy efficiency, reduce energy consumption and provide scientific basis for urban green environment planning. In this paper, image recognition technology is used to monitor the light change in the external environment of the building, and the indoor light intensity data is obtained by the light sensor in real time. Combined with big data analysis, building heat optimization model is established, intelligent control system is designed, and the heating and cooling system of the building is dynamically adjusted, so as to achieve efficient use of heat energy. The experimental results show that the thermal energy optimization method based on image recognition and light sensor has a good application prospect in low-energy buildings, which not only effectively reduces energy consumption, but also contributes to the sustainable development of urban green environment.

A dual-template molecularly imprinted electrochemical sensor assisted with BO-XGBoost algorithm for simultaneous determination of dopamine and acetaminophen

The simultaneous detection of dopamine (DA) and acetaminophen (AP) is crucial for diagnosing and treating related mental disorders. However, accurately determining DA and AP in biological samples remains challenging due to the interference from other biomolecules, such as ascorbic acid (AA), uric acid (UA), epinephrine (EP), etc. Here, we present a dual-template molecularly imprinted electrochemical sensor for the selective recognition of DA and AP. Reduced graphene(rGO)-gold nanoparticles(AuNPs) composite were utilized to enhance the effective area and increase the electron transfer rate of glassy carbon electrode(GCE), thus the electrochemical response signals were amplified. This sensor combined the advantages of nanocomposites and molecularly imprinted polymer (MIP) to achieve highly sensitive and selective detection of DA and AP. However, decreases in the current response of some analytes and variations in relationships of the peak current versus analyte concentration will occur due to adsorption competition on active sites in multianalyte mixtures, rendering the detection range constrained and traditional linear fitting methods inaccurate for predicting analyte concentrations. Therefore, a concentration prediction model based on XGBoost was developed for the sensor to achieve the reliable multi-component determination of DA and AP within the identical measurement range as individual detection. In this model, nine characteristic parameters were extracted from the differential pulse voltammetry (DPV) response curve and Bayesian optimization (BO) was adopted for automatic hyperparameter search, thereby the prediction errors were reduced and the generalization ability was improved. Experiments indicated that the MIP/AuNPs/rGO/GCE exhibited a wide detection range of 2–240 μM and 3–240 μM for DA and AP, with detection limits down to 0.26 μM and 0.33 μM, respectively. In addition, the intelligent MIP/AuNPs/rGO/GCE sensor based on BO-XGBoost model provides more accurate prediction results for untrained concentration combinations obtained from fetal bovine serum samples, indicating that the proposed detection scheme holds potential in future clinical diagnosis.

Advancing Human-Computer Interaction: Smartphone-Integrated POF Speckle Sensor for Gesture and Identity Recognition

Advancing Human-Computer Interaction: Smartphone-Integrated POF Speckle Sensor for Gesture and Identity Recognition

Multifunctional Self-Powered Sensors Integrated on a Robot Hand for Detecting Temperature-Pressure Stimuli and Recognizing Objects.

Tactile sensing, especially pressure and temperature recognition, is crucial for both humans and robots in identifying objects. The general solutions, which use piezoresistive, capacitive, and thermal resistance effects, are usually subject to single-mode sensing and an energy supply. Here, we propose a multimode self-powered sensor. The sensor can respond to pressure and temperature stimuli using triboelectric and thermoelectric effects. Furthermore, we developed a sensing system comprising sensors, a deep learning block, and a smart board. The deep learning model can fuse features of triboelectric and thermoelectric signals, enabling a high accuracy of 99.8% in recognizing ten objects. This method may provide the future design of self-powered sensors for object recognition in robotics.

Smart Dispenser Using Voice Recognition as an Assistive Device for the Visually Impaired with a Second-Order IIR Filter Algorithm

This research developed a Smart Dispenser with Voice Recognition technology as a tool to help the visually impaired in taking drinking water. The system is designed using a Voice Recognition sensor, an ISD 1820 sensor, and a microcontroller to enable the operation of the dispenser via voice commands. The test was carried out to analyze the performance of the system in recognizing voice commands and removing water. The results showed a voice detection accuracy rate of 28%, with factors such as environmental noise and sound intonation affecting the effectiveness of the system. The study also identified several areas of improvement, including the use of a DC water pump to increase the flow rate, the addition of an automatic feature to stop the flow of water when a glass is taken, and the use of a DC fan to maintain the sensor's optimal temperature. Further development is needed to improve the accuracy and efficiency of the system so that it can more effectively help the visually impaired in daily life.

Self‐Powered and 3D Printable Soft Sensor for Human Health Monitoring, Object Recognition, and Contactless Hand Gesture Recognition

AbstractA new galvanic cell design of a self‐powered and 3D‐printable soft sensor showing health monitoring, object recognition, and contactless hand gesture recognition, is reported. The soft sensor consists of a 3D‐printed poly(acrylic acid) (PAA) hydrogel electrolyte layer, a soft anode layer, and a soft cathode layer. The anode layer is a 3D‐printed and Cu2+ cross‐linked poly(N,N‐dimethylacrylamide‐co‐3‐alanine‐2‐hydroxypropylmethacrylate) (PDA) hydrogel dispersed with Cu metal particles (PDA/Cu2+/Cu hydrogel), while the cathode layer is a bottom thin layer of the PAA hydrogel containing MnO2 (PAA/MnO2). Using graphite films as the soft electrodes, the soft sensor is finally assembled. The soft sensor has high force and temperature sensitivities. It gives different electric current responses under stretching, bending, pressing, and impact loading. The soft sensor is demonstrated to be useful in detecting human motion and physiological activities, e.g., breath. Based on the force and contactless temperature sensitivities, the soft sensor is used to recognize human hand gestures and plastic balls with different diameters. This 3D printable soft sensor with self‐powering, contactless motion capturing, and multi‐pimulus sensing capabilities illustrates a new pathway to make soft sensory devices for healthcare and human‐machine interaction applications.

On the morphology of urosome appendages and their potential function of marine planktonic copepods

Abstract The goal of this paper is to draw the attention of biological oceanographers to the urosome of zooplankton. The methodology to determine the utilization of urosomes and their sensors for predator recognition is in its infancy. We here describe the potential utilization of setae and their activity of mostly juvenile copepods for the purpose of perceiving predators. Our morphological observations let us assume that calanoid nauplii have few means to perceive predators approaching from the rear and most likely from frontal, as their appendages are moving most of the time. This is being compared to post-nauplii, i.e. copepodid stages, and adults who possess a 2D array of setae on their urosomes, potentially able to perceive predators approaching from the rear. Detailed small-scale observations should reveal the sensor potential of urosomes of nauplii and older stages of planktonic copepods.

A self-filtering liquid acoustic sensor for voice recognition

A self-filtering liquid acoustic sensor for voice recognition

Versatile fluorenone sensor for swift identification of nerve agent mimic DCP and hazardous Cu2+/Hg2+ ions

A fluorenone and indole conjugated Schiff base (1) has been designed and synthesized through a one-pot reaction, and utilized as a multifunctional sensor for recognition of various analytes, DCP, Cu2+, Hg2+, and Cysteine. The UV–vis absorption spectra demonstrated the detection of the nerve agent mimic, DCP, indicated by a blue shift in the spectra in the solution of CH3CN. The sensor 1 showed high efficiency in detecting DCP among other phosphates and ions in absorption spectrum, with a detection limit of 1.16 µM. Additionally, when various anions and metal ions were introduced to the CH3CN solution of sensor 1 (λex 260 nm), the fluorescent probe was quenched completely in the presence of Cu2+ and Hg2+ ions. The association constants for Cu2+ and Hg2+ ions with sensor 1 were calculated to be 6.68 × 104 and 9.10 × 103 M−1, respectively, with a 1:1 binding stoichiometry. Moreover, the in-situ formed complex of sensor 1 and Cu2+ (1 + Cu2+) has been further utilized to synergistically recognize cysteine. The practical applications of sensor 1 were also investigated for detecting Cu2+ and Hg2+ ions in real water samples collected from different sources.

Machine Learning-Assisted Gesture Sensor Made with Graphene/Carbon Nanotubes for Sign Language Recognition.

Gesture sensors are essential to collect human movements for human-computer interfaces, but their application is normally hampered by the difficulties in achieving high sensitivity and an ultrawide response range simultaneously. In this article, inspired by the spider silk structure in nature, a novel gesture sensor with a core-shell structure is proposed. The sensor offers a high gauge factor of up to 340 and a wide response range of 60%. Moreover, the sensor combining with a deep learning technique creates a system for precise gesture recognition. The system demonstrated an impressive 99% accuracy in single gesture recognition tests. Meanwhile, by using the sliding window technology and large language model, a high performance of 97% accuracy is achieved in continuous sentence recognition. In summary, the proposed high-performance sensor significantly improves the sensitivity and response range of the gesture recognition sensor. Meanwhile, the neural network technology is combined to further improve the way of daily communication by sign language users.

Development of a novel humidity sensor for breath pattern recognition using large dislocation hollow core fiber infused with nano-carbon quantum water gel microfiber

This study presents a humidity sensor based on a large-core-offset hollow core optical fiber (LD-HCF) combined with polyvinyl alcohol carbon quantum dots (PVA-CQDs) nanocomposite microfiber for monitoring human respiratory. PVA-CQDs was introduced into the hollow core structure of LD-HCF to develope the respiratory sensor. The sensor has been integrated into a respiratory monitoring mask, exposed to ultraviolet (UV) light and demonstrated with a humidity sensitivity of 0.1721 nm/%RH as well as response and recovery times of 0.232 s and 0.252 s, respectively. The mask provides the real-time monitoring of human respiratory rates during various activities, including walking, standing, coughing, running, apnea 1 time and apnea 3 times. The typical respiratory patterns have been classified with a remarkable accuracy of 97.62 % using a WOA-1D CNN-LSTM neural network. The respiratory monitoring device is proposed by optical fiber, offering a promising potential in the real-time monitoring for healthcare process.

Method for enhanced gesture recognition under low light conditions based on wearable mechanoluminescence sensors

Gestures, as one of the key modes of human–computer interaction, are currently identified and processed mainly through two methods: machine vision imaging and wearable stress/strain sensing.However, the former has high requirements for lighting and background.This paper innovatively proposes a feature fusion algorithm that combines mechanoluminescent sensors for gesture recognition under low-light conditions. Unlike existing methods, this algorithm creates a system that integrates mechanoluminescent sensors for visual gesture recognition. By triggering the sensor’s luminescence through mechanical stress, it provides additional light source information to enhance image quality in low-light environments.For this purpose, this study employed the fluorescent emission properties of ZnS:Cu to fabricate a mechanoluminescence sensor. Its mechanoluminescence response characteristics were measured. Based on these findings, an algorithm for the fusion of gesture information features was developed for low illumination backgrounds. This algorithm decomposes the gesture visual images through YCbCr, extracting the HOG features of the Cr channel (Cr-HOG). Simultaneously, feature fusion is performed using the pooling layer (VGG16-Conv5-pool) following the convolutional layer (Conv5-3) in the VGG16 model. A one-to-one composite SVM classifier is constructed to complete the training and testing of the gesture recognition model. Ultimately, the feasibility of this enhanced gesture recognition method was validated through unmanned vehicle control experiment. Experimental results demonstrated that in environments with an illuminance level below 10 LUX measured by a light meter, the average recognition rate of the wearable mechanoluminescence sensor feature fusion algorithm reached 95.82 %. This is 32.24 % higher than the recognition rate of the single Cr-HOG feature classification and 28.27 % higher than the single VGG16-Conv5-pool feature classification. Compared with feature extraction using ResNet-50 and DenseNet-121 networks, the recognition rate for classifying gestures under low-light conditions is approximately 30 % higher.Compared with other image enhancement recognition methods, the recognition can be increased by up to 30.88 %.

Ultrasensitive Flexible Strain Sensor Made with Carboxymethyl-Cellulose-Anchored Carbon Nanotubes/MXene for Machine-Learning-Assisted Handwriting Recognition.

The combination of wearable sensors with machine learning enables intelligent perception in human-machine interaction and healthcare, but achieving high sensitivity and a wide working range in flexible strain sensors for signal acquisition and accurate recognition remains challenging. Herein, we introduced carboxymethyl cellulose (CMC) into a carbon nanotubes (CNTs)/MXene hybrid network, forming tight anchoring among the conductive materials and, thus, bringing enhanced interaction. The silicone-rubber-encapsulated CMC-anchored CNTs/MXene (CCM) strain sensor exhibits an excellent sensitivity (maximum gauge factor up to 71 294), wide working range (200%), ultralow detection limit (0.05%), and outstanding durability (over 10 000 cycles), which is superior to most of the recently reported counterparts also based on a conductive composite film. Moreover, the sensor achieves seamless integration with human skin with the help of a poly(acrylic acid) adhesive layer, successfully obtaining stable and clear waveforms with meaningful profiles from the human body. On this basis, we proposed and realized a novel in-air handwriting recognition method via extracting multiple features of high-quality strain signals assisted by deep neural networks, achieving a high classification accuracy of 98.00 and 94.85% for Arabic numerals and letters, respectively. Our work provides an effective approach for significantly improving strain sensing performance, thereby facilitating innovative applications of flexible sensors.

Fluorenone-naphthyl encapsulated dual sensor for recognition of F− and Hg2+: Syngenetic effect with drug sobisis and molecular docking studies

A novel fluorenone-naphthyl pendant sensor (FTU) possessing thiourea functionality has been synthesized via a simple condensation method and utilized for the recognition of F− and Hg2+ ions in the solution of CH3CN. The addition of F− and Hg2+ ions to the FTU solution led to the appearance of red-shifted absorption bands at 340 and 315 nm, respectively. On the other hand, in the fluorescence spectrum, the two-fold decrease in fluorescence intensity of probe FTU was observed with F− ions; while complete quenching of the fluorescence intensity was noticed with Hg2+ ions at 423 nm. The limit of detection values of F− and Hg2+ ions were found to be 1.02 & 29.1 nM, respectively, measured by UV–vis studies and 0.0185 & 0.81 nM, respectively, measured by fluorescence studies, which are less than recommended by WHO. DFT computational assessments and 1H NMR titration experiments pointed to F− induced deprotonation of thiourea NH signals. However, the chelation-enhanced quenching effect (CHEQ) was held responsible for fluorescence quenching with Hg2+ addition. Moreover, the in-situ formed FTU + F− complex was utilized for secondary sensing of drug sobisis. Furthermore, the real-world applicability of sensor FTU has been successfully scrutinized for the recognition of F− ions in the toothpaste samples. In addition, molecular docking studies revealed that FTU exhibited excellent antibacterial potency towards different gram-positive as well as negative strains.