Sensor For Detection Research Articles

A MOF-based array sensor for the differential detection of nitroaromatic compound isomers.

A MOF-based array sensor for the differential detection of nitroaromatic compound isomers.

Luminescent Re(I) sensors for copper ion detection in the lifetime based mode

Luminescent Re(I) sensors for copper ion detection in the lifetime based mode

Medium entropy-derived flower-like FeCeCu nanozyme with excellent oxidase-like activity for on-site and visual detection of carbosulfan.

Carbosulfan residues in environment is very harmful to human health. The rapid and high sensitive detection of carbosulfan residues is particularly important to guarantee human health and safety. The conventional chromatographic techniques and enzyme inhibition strategies cannot realize on-site and visual detection of carbosulfan. It is clear that a novel nanozyme-based method was need, which exhibited advantages in low-cost, rapidity, and portability with easy-to-use operation. A medium entropy-derived flower-like FeCeCu nanozyme (FeCeCuzyme) were prepared via the metal-organic coordination method with "Ostwal ripening" growth process. FeCeCuzyme exhibited an excellent oxidase-like activity due to an abundant active sites in the flower-like structure. Carbosulfan can produce the sulfide compound by hydrolysis reaction under acidic condition, which could easily form sulfide-metal bonds with FeCeCuzyme, thereby significantly quenching their oxidase-like activity and hindering nanozyme-mediated chromogenic reaction. Given this phenomenon, the FeCeCuzyme were used as a colourimetric sensor for highly-specific carbosulfan detection without using acetylcholinesterase and H2O2. The FeCeCuzyme colourimetric sensor showed a wide linear range from 0.15 to 50.00μM (R2=0.9970) with low detection limit of 0.13μM. Meanwhile, FeCeCuzyme sensor was integrated with smartphone to design a portable detection platform, which realizes the on-site and rapid detection of carbosulfan with easy-to-operation. A low detection limit of 0.14μM was obtained in the linear relation of 0.15-50.00μM. The satisfactory recoveries (91.17%-108.00%) from the spiking method were highly agreed with HPLC technique, and which further verified the feasibility of FeCeCuzyme-based strategies in real samples. This work provides a pioneer new avenue for design of novel nanozymes, and offers an efficient alternative to the conventional methods for specifical, fast, and on-sites detection of carbosulfan in environment.

Exceptional reliability and stability AuNis–AlGaN/GaN HEMT sensor for pH detection

Exceptional reliability and stability AuNis–AlGaN/GaN HEMT sensor for pH detection

A novel eco-friendly electrochemical fabrication of magnetic bimetallic-organic framework sensors for ultrasensitive detection of dual miRNAs

A novel eco-friendly electrochemical fabrication of magnetic bimetallic-organic framework sensors for ultrasensitive detection of dual miRNAs

A new 3D Pb(II)-based MOF as highly sensitive selective fluorescent probe for naked-eye detection of furazolidone

A new 3D PbII-based metal-organic framework (MOF) [Pb(SDBA)]n·1.75DMF (1) (SDBA = 4,4’-sulfonyldibenzoate) has been synthesized and characterized. The single crystal X-ray diffraction study reveals that 1 is a uninodal (3,6)-connected topology with a point symbol of {62.8}{46.67.82} based on 1D Pb–O–Pb chain. The MOF 1 has been employed as luminescent sensor for sensitive and selective detection of antibiotics and experiments suggest that 1 can selectively and sensitively recognize furazolidone (FZD) with limit of detection (LOD) of 1.01 × 10-6 M and shows recyclable sensing properties against FZD. Interestingly, FZD could be directly detected with naked eyes under 365 nm UV lamp using paper test strips impregnated with 1. Also, complete recovery in the emissive response of FZD@1 has been demonstrated in the presence of salicylic acid. This is only the second example of PbII-based MOF that has been employed as turn-off-on fluorescent sensor toward FZD. The plausible mechanisms for the decline and restoration of emissive responses of 1 have been explained with the help of Hirshfeld surface analysis and density functional theory.

A post-modified lanthanide metal-organic frameworks as ratiometric luminescent sensor for the visual detection of 5-hydroxytryptamine

5-Hydroxytryptamine (5-HT), a key neurotransmitter, is an important biomarker for carcinoid syndrome. We herein construct a ratiometric luminescent sensor by covalently coupling fluorescein 5-isothiocyanate (FITC) with lanthanide metal-organic frameworks (Ln-MOFs). In the presence of 5-HT, the emission of FITC increases while the emission of Eu3+ decreases, accompanied by a distinct color change of emission from orange to green. This sensor not only has the advantages of high sensitivity (LOD = 0.04μM), fast response (30s), excellent selectivity, and large ΔE⁎ab value (73), but can also be used for the detection of 5-HT in human serum and allow for instant visual detection with the assistant of smartphone. This ratiometric luminescent sensor offers an alternative avenue for early diagnosis of carcinoid syndrome.

An ammonia-responsive aerogel-type colorimetric sensor for non-destructive monitoring of shrimp freshness.

The colorimetric sensor for volatile amines (VA) detection can realize non-destructive monitoring of shrimp quality. However, its sensing performance still needs to be improved. In this study, we proposed an aerogel-type colorimetric sensor to improve VA sensing performance and realize early detection of shrimp spoilage. The sensor was composed of konjac glucomannan-based aerogel prepared by sol-gel and freeze-drying, and pH-responsive dyes (neutral red) which were adsorbed on the aerogel surface. The sensor could show color change from pink to orange under the exposure of NH3, a marker of spoiled shrimp and the Euclidean Distance (ΔE) of the sensor was positively correlated with NH3 concentration. Due the three-dimensional network of the aerogel and its adsorption performance, the sensitivity of the aerogel-type sensor in NH3 detection was ∼6 times higher and the maximum ΔE was ∼1.5 times higher compared with the film-type sensor. It also showed stable sensing performance under various environment and was further successfully used in shrimp freshness monitoring. It was found that ΔE higher than 23.88 indicated the spoilage of shrimp. The above results provided a general approach for the designing of high-performance colorimetric sensor for real-time and non-destructive monitoring of shrimp freshness.

Bimetallic yttrium-copper-metal-organic framework-based voltammetric sensor for detection of dopamine in the presence of uric acid

Bimetallic yttrium-copper-metal-organic framework-based voltammetric sensor for detection of dopamine in the presence of uric acid

IoT-Based Metal Detector Robot with Wireless Surveillance

Abstract: This project presents the design and development of an IoT-based metal detector robot equipped with wireless surveillance capabilities. The robot is designed to autonomously navigate its environment while detecting metallic objects underground, with real-time monitoring provided through a wireless camera system. The core components of the system include a metal detector sensor, an ESP8266 NodeMCU for wireless communication, an L298 motor driver for controlling the robot’s movement, and a surveillance camera for live video streaming. The system is powered by 3.7V battery cells, ensuring mobility and operational autonomy.The metal detector sensor scans for metallic objects, and upon detection, an alert is generated. Simultaneously, a wireless camera transmits real-time footage, allowing remote monitoring and control via a web interface or mobile device. The robot's movement is controlled through a second ESP8266 module, enabling wireless command inputs for navigation.This robot is envisioned for use in security applications, such as detecting landmines or hazardous metallic objects in sensitive areas, as well as for archaeological and construction projects. The integration of IoT technology enhances the robot’s operational range and effectiveness, providing a scalable solution for remote metal detection and surveillance.The integration of IoT technology significantly enhances the system's operational efficiency and range, enabling seamless remote control and monitoring. This innovation not only improves safety in hazardous conditions but also increases the effectiveness of metal detection and surveillance tasks in diverse environments

Data analysis and visualization of electric vehicle engineering

We have visualized data using Excel to understand the relationship between battery chemistries and capacity. Examined Python code for tuning a Proportional – Integral – Derivative (PID) controller to understand its role in improving control accuracy. The challenges and benefits of PID tuning are discussed, highlighting the importance of precise tuning for optimal performance, efficiency, and safety. Analysing the impact of various battery form factors, including cylindrical, pouch, and prismatic options, on system cost, safety, and durability. Furthermore, the paper explores design trade-offs in electric vehicle development, emphasizing the need for balancing competing parameters such as performance, durability, and cost-effectiveness. The characteristics of various battery form factors (cylindrical, pouch, and prismatic) and Light Detection and Ranging (LiDAR) sensors (mechanical, solid-state, and hybrid) are compared, providing insights into their suitability for different applications. Overall, this article offers a comprehensive overview of the intricate relationships between battery chemistries, PID controllers, and design trade-offs in the development of efficient and sustainable electric vehicles. Conducted a comprehensive cost-benefit analysis for different LiDAR sensor models, comparing mechanical scanning, solid-state, and hybrid LiDAR sensors, with a focus on durability, scanning speed, and cost implications. Beside the aforementioned, we have also presented technical findings in a concise format using tables, aiding the engineering division in making informed decisions for various projects.

Recent progress on nanomaterial-based electrochemical sensors for glucose detection in human body fluids.

In the modern age, half of the population is facing various chronic illnesses due to glucose maintenance in the body, major causes of fatality and inefficiency. The early identification of glucose plays a crucial role in medical treatment and the food industry, particularly in diabetes diagnosis. In the past few years, non-enzymatic electrochemical glucose sensors have received a lot of interest for their ability to identify glucose levels accurately. Electrochemical biosensors are developing as a propitious solution for personalized health monitoring due to their accuracy, specificity, and affordability. This review article provides an observation of a variety of non-enzymatic glucose sensor resources, such as carbon nanomaterials, noble metals gold and silver, transition metal and their oxides, and porous material composites. Moreover, basic knowledge of the reaction mechanism of enzymatic and nonenzymatic glucose sensors are outlined and recent advancements in glucose sensors applicationsto various human body biofluids such as sweat, tears, urine, saliva, and bloodare presented. Finally, this review summarizes electrochemical sensors for glucose detection in human body fluids, the challenges they faced, and their solutions.

Aerolysin Nanopore Electrochemistry.

ConspectusIons are the crucial signaling components for living organisms. In cells, their transportation across pore-forming membrane proteins is vital for regulating physiological functions, such as generating ionic current signals in response to target molecule recognition. This ion transport is affected by confined interactions and local environments within the protein pore. Therefore, the pore-forming protein can efficiently transduce the characteristics of each target molecule into ion-transport-mediated signals with high sensitivity. Inspired by nature, various protein pores have been developed into high-throughput and label-free nanopore sensors for single-molecule detection, enabling rapid and accurate readouts. In particular, aerolysin, a key virulence factor of Aeromonas hydrophila, exhibits a high sensitivity in generating ionic current fingerprints for detecting subtle differences in the sequence, conformation, and structure of DNA, proteins, polypeptides, oligosaccharides, and other molecules. Aerolysin features a cap that is approximately 14 nm wide on the cis side and a central pore that is about 10 nm long with a minimum diameter of around 1 nm. Its long lumen, with 11 charged rings at two entrances and neutral amino acids in between, facilitates the dwelling of the single analyte within the pore. This characteristic enables rich interactions between the well-defined residues within the pore and the analyte. As a result, the ionic current signal offers a unique molecular fingerprint, extending beyond the traditional volume exclusion model in nanopore sensing. In 2006, aerolysin was first reported to discriminate conformational differences of single peptides, opening the door for a rapidly growing field of aerolysin nanopore electrochemistry. Over the years, various mutant aerolysin nanopores have emerged, associated with advanced instrumentation and data analysis algorithms, enabling the simultaneous identification of over 30 targets with the number still increasing. Aerolysin nanopore electrochemistry in particular allows time-resolved qualitative and quantitative analysis ranging from DNA sequencing, proteomics, enzyme kinetics, and single-molecule reactions to potential clinical diagnostics. Especially, the feasibility of aerolysin nanopore electrochemistry in dynamic quantitative analysis would revolutionize omics studies at the single-molecule level, paving the way for the promising field of single-molecule temporal omics. Despite the success of this approach so far, it remains challenging to understand how confined interactions correlate to the distinguishable ionic signatures. Recent attempts have added correction terms to the volume exclusion model to account for variations in ion mobility within the nanopore caused by the confined interactions between the aerolysin and the analyte. Therefore, in this Account, we revisit the origin of the current blockade induced by target molecules inside the aerolysin nanopore. We highlight the contributions of the confined noncovalent interactions to the sensing ability of the aerolysin nanopore through the corrected conductance model. This Account then describes the design of interaction networks within the aerolysin nanopore, including electrostatic, hydrophobic, hydrogen-bonding, cation-π, and ion-charged amino acid interactions, for ultrasensitive biomolecular identification and quantification. Finally, we provide an outlook on further understanding the noncovalent interaction network inside the aerolysin nanopore, improving the manipulating and fine-tuning of confined electrochemistry toward a broad range of practical applications.

Large-Area Conductor-Loaded PDMS Flexible Composites for Wireless and Chipless Electromagnetic Multiplexed Temperature Sensors.

Capacitive dielectric temperature sensors based on polydimethylsiloxane (PDMS) loaded with 10vol% of inexpensive, commercially-available conductive fillers including copper, graphite, and milled carbon fiber (PDMS-CF) powders are reported. The sensors are tested in the range of 20-110°C and from 0.5 to 200MHz, with enhanced sensitivity from 20 to 60°C, and a relative response of 85.5% at 200MHz for PDMS-CF capacitors. PDMS-CF capacitors are interrogated as a sensing element in wirelessly coupled chipless resonant coils tuned to 6.78MHz with a response in the resonant frequency (fr) of the sensor, demonstrating an average sensitivity of 0.38%°C-1, a 40x improvement over a pristine PDMS capacitive sensor and outperforms state-of-the-art frequency-domain radio frequency temperature sensors. Exploiting its high sensitivity, the wireless sensing platform is interrogated using a low-cost, portable, and open-source NanoVNA demonstrating a relative response in fr of 48.5%, good agreement with instrumentation-grade vector network analyzers (VNAs) and negligible change in performance at a range of reading distances and humidities. Finally, a wireless tag is demonstrated with rapid, reversible dynamic response to changes in temperature, as well as the in the first scalable, multiplexed array of chipless sensors for spatial temperature detection.

PtAu-Carbon Nanotube/Glassy Carbon Electrode Composite based Electrochemical Sensor for High-Precision Detection of Tetracycline.

With enrichment of tetracycline (TC) in ecosystems, its accurate detection has become a major concern. Noble-metal nano-particles have attracted great interest as potential materials for sensing applications because of their remarkable electrical properties and adaptability. Herein, a novel electro-chemical detection technique based on carbon nano-tubes (CNTs) as the support material is developed to detect TC with high precision. To achieve a characteristic response to TC, a composite electrode for detection electrode is prepared by loading platinum-gold (PtAu) nano-particles on CNTs surface via electro-deposition. The oxidation peak at 0.38 V is used as the signal probe to draw a standard curve by comparing the intensities of the oxidation peaks at various TC concentrations. The PtAu-CNTs/GCE sensor exhibits remarkable sensitivity (LOD = 1.05 × 10-8 mol/L) and anti-interference properties. This study demonstrates the potential for TC detection in complex environments.

Cutting-Edge Sensor Design: MIP Nanoparticle-Functionalized Nanofibers for Gas-Phase Detection of Limonene in Predictive Agriculture

As population growth and climate change intensify pressures on agriculture, innovative strategies are vital for ensuring food security, optimizing resources, and protecting the environment. This study introduces a novel approach to predictive agriculture by utilizing the unique properties of terpenes, specifically S(-)-limonene, emitted by plants under stress. Advanced sensors capable of detecting subtle limonene variations offer the potential for early stress diagnosis and precise crop interventions. This research marks a significant leap in sensor technology, introducing an innovative active sensing material that combines molecularly imprinted polymer (MIP) technology with electrospinning. S(-)-limonene-selective MIP nanoparticles, engineered using methacrylic acid (MAA) and ethylene glycol dimethacrylate (EGDMA), were synthesized with an average diameter of ~160 nm and integrated into polyvinylpyrrolidone (PVP) nanofibers reinforced with multiwall carbon nanotubes (MWCNTs). This design produced a conductive and highly responsive sensing layer. The sensor exhibited rapid stabilization (200 s), a detection limit (LOD) of 190 ppb, and a selectivity index of 73% against similar monoterpenes. Optimal performance was achieved at 55% relative humidity, highlighting environmental conditions’ importance. This pioneering use of polymeric MIP membranes in chemiresistive sensors for limonene detection opens new possibilities for monitoring VOCs, with applications in agricultural stress biomarkers, contaminant detection, and air quality monitoring, advancing precision agriculture and environmental protection.

Near-Infrared Spectral MEMS Gas Sensor for Multi-Component Food Gas Detection

The complex application environments of gas detection, such as in industrial process monitoring and control, atmospheric and environmental monitoring, and food safety, require real-time and online high-sensitivity gas detection, as well as the accurate identification and quantitative analysis of gas samples. Despite the progress in gas analysis and detection methods, high-precision and high-sensitivity detection requirements for target gases of multiple components in mixed gases are still challenging. Here, we demonstrate a micro-electromechanical system (MEMS) with near-infrared (NIR) spectral gas detection technology and spectral model training, which is used to improve the detection and classification of multi-component gases in food. During blind sample testing, the NIR spectral gas sensor demonstrated over 90% accuracy in identifying mixed gases, as well as achieving the classification of ethanol concentration. We envision that our design strategy of an NIR spectral gas sensor could enhance the gas detection and distinguishing ability under the conditions of background gas interference and cross-interference in multi-component detection.

A Novel Machine Learning Technique for Fault Detection of Pressure Sensor

Pressure transmitters are widely used in the process industry for pressure measurement. The sensing line, a core component of the pressure sensor in the pressure transmitter, significantly impacts the accuracy of the pressure transmitter’s output. The reliability of pressure transmitters is critical in the nuclear power industry. Blockage is recognized as a common failure in pressure sensing lines; therefore, a novel detection method based on Trend Features in Time–Frequency domain characteristics (TFTF) is proposed in this paper. The dataset of pressure transmitters comprises both fault and normal data. This method innovatively integrates multi-scale time series decomposition algorithms with time-domain and frequency-domain feature extraction techniques. Initially, this dataset is decomposed into multi-scale time series to mitigate periodic component interference in diagnosis. Subsequently, via the sliding window algorithm, both the time-domain features and frequency-domain features of the trend components are extracted, and finally, the XGBoost algorithm is used to detect faults. The experimental results demonstrate that the proposed TFTF algorithm achieves superior fault detection accuracy for diagnosing sensing line blockage faults compared with traditional machine learning classification algorithms.

Recent advances in designable nanomaterial-based electrochemical sensors for environmental heavy-metal detection.

The detection of heavy metals serves as a defence measure to safeguard the well-being of the human body and the ecological environment. Electrochemical sensors (ECS) offer significant benefits such as exceptional sensitivity, excellent selectivity, affordability, and portability. This review begins by elucidating the ECS principles and delves into recent advancements in the field of heavy metal detection, including the use of metal nanoparticles, carbon-based nanomaterials, and organic framework materials. Advanced materials enhance the sensitivity and selectivity of ECS, allowing it to efficiently and rapidly identify metallic contaminants in food and the environment. Finally, the future development of ECS and challenges encountered in the development process are discussed, and testing materials for the detection of heavy-metal ions for human health and environmental safety are comprehensively considered. This study is likely to attract the interest of environmentalists and those who prioritise human health.

Development of an IoT-based Traffic Signal Violation Detection System

This study tackles the pressing issue of traffic signal stop-line violations, which poses significant threat to road safety, by designing and implementing an intelligent traffic signal violation system (Internet-of-things (IoT) based technology). The system utilizes infrared (IR) sensors for vehicular detection and an ESP32 Camera to capture images of violating vehicles, which are then sent to a server via notification bots for media platforms. Also, the IR sensors are embedded with the ESP32 Camera and programmed microcontroller to detect traffic stop-line signal violations, capture images, and transmit them to designated authorities. The system was implemented using a prototype build and the result showed a 98% accuracy rate in detecting violations. The system reduces response time for violation detection by 60% success rate. In conclusion, this project demonstrates a reliable and efficient traffic signal violation system, leveraging IoT and Artificial Intelligence (AI) technologies to enhance road safety. The real-world significance of this system entails an enabled rapid intervention, identifying and disciplining offenders, enhance resource allocation, repository of data generated and enforcing legal actions thereby contributing to enhanced road traffic operations and user safety.