Low-cost Detection Research Articles

Near-Infrared Light and OpenCV as Components for Low-Cost Airborne Microplastic Detection Machine

Abstract. Microplastic, resulting from the breakdown of larger plastic objects, poses substantial threats to ecosystems and human health. This research addresses the escalating environmental concern of airborne microplastics by developing a low-cost detection machine employing infrared sensors. The near-infrared light is attached to the machine for detection using the sensors. Different types of microplastic, specifically less than 5mm, are used. There are 3 types of plastics used for detection, 30 pieces each of polyethylene, polystyrene, and polyester microplastics. Each type of microplastic is tested for detection in different time frames, 1, 2, 3, 4, and 5 minutes each, and the three types of plastics are combined randomly for the last detection with the same number of minutes. Because of the final results, it can determine the effectiveness of the sensor in detecting airborne microplastics in terms of the number of correctly detected microplastics. In conclusion, this research advances our capacity to detect airborne microplastics, contributing to environmental conservation and public health protection. The detection machine's development and the insights gained from the study have broader implications for standardizing methodologies, advancing technology, and fostering collaborative efforts in addressing the challenges posed by airborne microplastics.

A novel sensing probe based on AuNPs-Apt for the detection of enrofloxacin

Enrofloxacin (ENR), as a synthetic broad-spectrum antibiotic is widely utilized in veterinary medicine to treat animal diseases and promote livestock growth, it can inhibit bacterial DNA gyrase subunit A, thereby preventing bacterial DNA replication and exerting its antibacterial effect. However, excessive use of enrofloxacin poses significant risks to ecological balance and human health due to residual contamination. We have developed a novel ENR aptamer sensor based on the gold nanoparticles/aptamer (AuNPs-Apt) complexes, in which AuNPs were synthesized via the seed method and functionalized with aptamers. The optical properties, particle size, functional groups and morphology of the AuNPs-Apt probe were characterized by transmission electron microscope, Fourier transform infrared spectrometer and UV-vis spectrophotometer, respectively. The aptamer biosensor can specifically identify enrofloxacin, with a wide detection range (0.05-100μg ml-1) and a good linear relationship (R2=0.99) within the detection range. In addition, the biosensor also has the advantages of short detection time, low biological toxicity, good stability, and low detection cost. Therefore, it shows a great prospect for practical application in the field of detecting enrofloxacin residues.

Dual-signal ratiometric electrochemical aptasensor for Zearalenone detection based on magnetic-assisted enrichment and hybridization chain reaction

In this work, a dual-signal ratiometric electrochemical aptasensor based on the hybrid chain reaction (HCR) and streptavidin-modified magnetic beads (SA-MBs) was developed to rapidly detect zearalenone (ZEN). The HCR, as a powerful signal amplification technique to imporve the signal of sonser. When the target is present, they specifically bind with ZEN-Apt and release ZEN-cDNA to trigger HCR. Simultaneously, more double-stranded DNA causes the signal of Thi to be blocked. As a result, the two signals tend to change in the opposite direction as the ZEN concentration changes. Additionally, the peak current ratio of IThi/IFc showed a positive correlation with the ZEN concentration. Under optimal conditions, the constructed biosensor showed an excellent linear detection range (1.0 × 10−10 mol/L to 1.0 × 10−6 mol/L), a low detection limit (4.4 × 10−11 mol/L) and high specificity for ZEN. In addition, the detection method retains the characteristics of low cost and rapid detection of electrochemical detection, while improving the detection limit and detection accuracy via SA-MBs and internal reference signal. This provides a new idea for the practical detection of ZEN.

Low-cost portable sensor for rapid and sensitive detection of Pb2+ ions using capacitance sensing integrated with microfluidic enrichment.

Lead ion (Pb2+) pollution is a critical global issue due to its ability to accumulate in the human body, resulting in severe health problems. Despite extensive research efforts devoted to the detection of Pb2+ contamination, practical, rapid, and field-deployable sensors for Pb2+ is yet to be developed to effectively safeguard the environment and public health. Herein, a label-free affinity-based sensing device is developed based on printed circuit board (PCB) for low-cost, easy-to-use, and real-time on-site detection of Pb2+ ions. The sensors are prepared by forming a self-assembled monolayer of glutathione (GSH) on the surface of gold-plated PCB electrodes, which serves as a molecular probe to recognize Pb2+. Rapid and sensitive detection is achieved by using capacitance sensing integrated with microfluidic enrichment. The sensor's interfacial capacitance is used to indicate specific binding, while the capacitance reading process simultaneously induces alternating current electrothermal (ACET) acceleration of analyte's travel towards the probes. Thus, the enrichment and detection are integrated into a single step, making pre-concentration unnecessary and shortening the assay time to 30s. This Pb2+ sensor has demonstrated one of the lowest limits of detection reported so far (1.85 fM) with a linear range of 0.01-10pM. To evaluate the sensor's specificity, non-target metal ions are tested, all showing negligible responses. Testing of tap water sample also yields reasonable results, validating the sensor's robustness. The above-mentioned features, together with a commercial portable readout, make this sensor well-suited for point-of-use Pb2+ detection at low cost.

Recombinant MS087-based indirect ELISA for the diagnosis of Mycoplasma synoviae.

Accurate detection is a prerequisite for effective prevention and control of Mycoplasma synoviae infection. ELISA is the most popular method for the clinical detection of M. synoviae because of its convenience, low cost, and high detection rate. However, the cross-reactivity of commercially available ELISA kits with other avian pathogen-positive sera needs to be addressed. The aim of this study was to establish an ELISA method with high specificity for the detection of anti-M. synoviae antibodies in chicken serum to evaluate the M. synoviae infection status on poultry farms. The recombinant MS087 (rMS087) protein was expressed in Escherichia coli BL21 (DE3) and purified by Ni2+ affinity chromatography. An antibody against rMS087 was generated by immunizing BALB/c mice. Bioinformatic analysis revealed that MS087 was conserved among M. synoviae strains. Western blotting and indirect immunofluorescence results indicated that MS087 was not only localized in the cytoplasm and on the membrane but also secreted by the organism. For the established ELISA method based on rMS087, the optimal antigen concentration, blocking buffer, blocking duration, serum dilution, serum incubation duration, secondary antibody dilution, secondary antibody incubation duration and colorimetric reaction duration were 2 μg/mL, 1% BSA, 3 h, 1:500, 1.5 h, 1:20,000, 2 h and 5 min, respectively. Validation of the rMS087-based ELISA revealed a cut-off value of 0.5. The coefficients of variation of both the intra-batch and inter-batch methods were less than 9%. The assay was able to differentiate positive serum against M. synoviae from antisera against nine other avian pathogens and was able to recognize M. synoviae-positive sera at a dilution of 1:1,000. Compared with the commercial ELISA method, the rMS087-based ELISA has the potential to recognize more positive sera against M. synoviae. Collectively, the rMS087-based ELISA is a reproducible, specific, and sensitive serological method for detecting antibodies against M. synoviae in chicken serum and has robust potential for large-scale serological epidemiology of M. synoviae infection on poultry farms.

The enhancement detection method based on the Fabry–Pérot cavity using terahertz frequency-domain spectroscopy

This article demonstrates a simple, efficient, and low-cost gas detection method for gases with absorption peaks in the terahertz range. A modes-adjustable Fabry–Pérot cavity is designed. By adjusting the length of the cavity, the center resonant frequency of the cavity can be coupled to the gas absorption peak. This kind of coupling can greatly enhance gas detection. To detect gas absorption peaks, we choose terahertz frequency domain spectroscopy (THz-FDS), whose frequency resolution can be up to the MHz level. Vapor is selected to verify the coupling phenomenon. The resonant frequency of the cavity is modified to couple to 0.56 THz, the absorption peak of vapor. Experiments are conducted at different humidity levels, the humidity is controlled by the supersaturated salt solution. Results indicate that at a humidity level of 15 %, the coupling effect can enhance the detectability of vapor by approximately 167 %, and this enhancement effect diminishes as humidity increases. We analyze the effect of different modes on the coupling and find that the high modes can make the coupling easier, but have little effect on the enhancement. Furthermore, the method is used to detect biomolecule-α-tyrosine to ensure it has wide applicability. This method can be used to detect substances with absorption peaks in the THz regime, especially for low-concentration gas.

Detection of urea in milk by urease-inorganic hybrid nanoflowers combined with portable colorimetric microliter tube.

Asimple one-pot green synthesis method was used to prepare urease-inorganic hybrid nanoflowers (UE-HNFs), which had a high surface-to-volume ratio to improve enzyme catalytic efficiency and make urease reusable. A portable colorimetric microliter tube based on urease-inorganic hybrid nanoflowers (UE-HNFs-PCMT), as an urea colorimetric biosensor, was developed for determining urea concentration in milk. The combination of urea colorimetric biosensor and asmartphone is usedfor capturingthe colour change of milk after reaction. There was a good linear relationship between colour intensity of the image (Δ intensity) and urea concentration (43-600mg L-1), with a detection limit of 12.81mg L-1. UE-HNFs-PCMT has the advantages of no need for complex equipment, easy operation, reusability, low detection cost, good portability, and environmental friendliness and can achieve urea detection in milk.

Progressions in Modified Graphite Electrodes with Green Nanostructured Materials for Low Cost and Sustainable Electrochemical Detection of Environmental Contaminants

Progressions in Modified Graphite Electrodes with Green Nanostructured Materials for Low Cost and Sustainable Electrochemical Detection of Environmental Contaminants

Acoustic Streaming Tunnel Enables Particle Velocity Stretching in Multiplex Flow Cytometry.

Multiplexed flow cytometry, known for its powerful high-throughput identification capability, is widely applied across various biomedical and clinical fields. However, classical flow cytometry relies on multichannel lasers and detectors, which are significant in cost and size, limiting their application in miniaturized assays. Herein, we developed an acoustic streaming-based flow cytometry technique that focuses on multisized microbeads flowing sheathlessly. This method enables the discrimination of particle types and the quantification of target protein concentrations using only a single detector. Microbeads of different sizes exhibit distinct behaviors in the continuous acoustic streaming tunnel, leading to an increased velocity difference during their transition under the laser spot. Consequently, a size detection method based on "velocity stretching" has been established. A multiplex assay of three proteins: cardiac troponin I, creatine kinase-MB and myoglobin, in acute myocardial infarction is performed to validate the feasibility and evaluate the performance of the system. This new multiplexed flow cytometry strategy is expected to enable low-cost and onsite detection of multiple biomarkers.

Biological Ce/Fe-metal organic framework multifunctional nanozyme-based colorimetric sensor array for multi-analyte detection in human sweat

Diabetes, a widespread metabolic disorder, is a major public health concern. To address the ongoing need for effective diagnosis, a novel approach has been developed that allows the rapid, low-cost, and ultra-sensitive detection of disease-specific biomarkers in various body fluids using a single platform. Notably, this method focuses on biomarkers found in human sweat—glucose, L-Tryptophan, uric acid, and acetone—that are indicative of diabetes. The new strategy involves a nanozyme-based colorimetric sensor array made from a Ce/Fe fumarate biological metal–organic framework (Ce/Fe-Fum Bio-MOF) doped with Cu and Ni, and further enhanced with Ag nanoparticles. This nanozyme displays multiple enzyme-like activities, which result in distinct color changes when tested with specific chromogenic substrates. When these color changes are reduced by the introduction of biomarkers, the results can be easily captured using a smartphone. This method offers a wide linear detection range (20–1000 µM) and low detection limits for each biomarker, showing strong selectivity, reproducibility, and practical performance in detecting biomarkers in human sweat samples. The integration of smartphone signaling with this sensor array creates potential for a portable, handheld device for point-of-care applications, and could also be adapted for other sensing and biosensing needs. This colorimetric assay marks a significant advancement in the detection of diabetes biomarkers and the development of point-of-care diagnostic systems.

Comparison of a hepatitis C core antigen assay to nucleic acid amplification testing for detection of hepatitis C viremia in a US population.

A research-use HCV Core Antigen Assay showed high concordance with nucleic acid amplification testing for the detection of current hepatitis C infection. The assay may enable more rapid and lower-cost detection and/or confirmation of hepatitis C infection.

SISTEMA PARA DETECÇÃO DE VAZAMENTO DE GÁS

Gas poisoning, such as Liquefied Petroleum Gas (LPG), is responsible for around 19,000 deaths annually, due to its high flammability and toxic effects when inhaled in large quantities(Science Advances, 2024). In response to this, a prototype low-cost LPGgas detection system was developed, based on Arduino. The system uses sensors to identify leaks and trigger alerts via LEDs, buzzer, fan and relay, an LCD board and an android app that receives the data sent viaBluetooth to the owner. Tests have confirmed the system’s efficiency in detecting dangerous levels of gas, with positive results for residential and commercial safety.

Real-time, specific, and label-free transistor-based sensing of organophosphates in liquid

Organophosphates (OP), commonly used in agriculture and as chemical warfare agents, pose significant environmental risks, necessitating real-time, low-cost OP detection methods. In particular, liquid-phase OP sensing with minimal sample volumes is crucial. While several methods allow rapid detection of low concentrations of OP vapors, they are effective only in the short term, while vapors are still being produced. Many OP compounds are semi-volatile, leading to the contamination of water, soil, and surfaces, posing a risk of secondary, long-term exposure. Detecting this contamination requires methods that can be directly applied to droplets of the affected medium. Currently, no method provides the desired combination of ultra-sensitivity, quantitative detection, rapid response, and low-cost for detecting OPs in liquid samples. This study aims to demonstrate quantitative, low-cost, real-time, specific, and label-free OP sensing in ultra-small samples using a transistor-based approach. The current work employs the 2-(4-Aminophenyl)-1,1,1,3,3,3-hexafluoro-2-propanol (aminophenyl-HFIP) functionalized meta-nano-channel field-effect chemical sensor (MNChem sensor) to monitor the organophosphate, diethyl cyanophosphonate (DCNP), in liquid samples. The silicon component of the MNChem is fabricated using a complementary metal-oxide semiconductor (CMOS) process, and the amine-based chemical functionalization of the sensing area is performed post-fabrication. The MNChem sensor provides electrostatic control over the source-drain current (IDS), allowing an optimized channel configuration that efficiently transduces localized OP recognition events into significant IDS variations. Sensing is performed using 0.5 μL buffer solution to simulate a miniature field-deployable sensor for on-site liquid analysis. We report the sensing of DCNP with a limit-of-detection of 100 fg/mL, a dynamic range of 9 orders of magnitude, and excellent linearity (≥0.97) and sensitivity. Control measurements confirm the specificity and reliability of the sensor's response, validating its applicability. This study introduces a novel method for OP detection in contaminated droplets using a low-cost disposable transistor technology.

An Image-Based Sensor System for Low-Cost Airborne Particle Detection in Citizen Science Air Quality Monitoring.

Air pollution poses significant public health risks, necessitating accurate and efficient monitoring of particulate matter (PM). These organic compounds may be released from natural sources like trees and vegetation, as well as from anthropogenic, or human-made sources including industrial activities and motor vehicle emissions. Therefore, measuring PM concentrations is paramount to understanding people's exposure levels to pollutants. This paper introduces a novel image processing technique utilizing photographs/pictures of Do-it-Yourself (DiY) sensors for the detection and quantification of PM10 particles, enhancing community involvement and data collection accuracy in Citizen Science (CS) projects. A synthetic data generation algorithm was developed to overcome the challenge of data scarcity commonly associated with citizen-based data collection to validate the image processing technique. This algorithm generates images by precisely defining parameters such as image resolution, image dimension, and PM airborne particle density. To ensure these synthetic images mimic real-world conditions, variations like Gaussian noise, focus blur, and white balance adjustments and combinations were introduced, simulating the environmental and technical factors affecting image quality in typical smartphone digital cameras. The detection algorithm for PM10 particles demonstrates robust performance across varying levels of noise, maintaining effectiveness in realistic mobile imaging conditions. Therefore, the methodology retains sufficient accuracy, suggesting its practical applicability for environmental monitoring in diverse real-world conditions using mobile devices.

Design and Comparison of Low-Cost Urine Level Detection Systems for Critical Patients

Design and Comparison of Low-Cost Urine Level Detection Systems for Critical Patients

Standoff Identification of Plastic Waste Using a Low-Cost Compact Laser-Induced Breakdown Spectroscopy (LIBS) Detection System.

We report the standoff/remote identification of post-consumer plastic waste by utilizing a low-cost and compact standoff laser-induced breakdown spectroscopy (ST-LIBS) detection system. A single plano-convex lens is used for collecting the optical emissions from the plasma at a standoff distance of 6.5 m. A compact non-gated Czerny-Turner charge-coupled device (CCD) spectrometer (CT-CCD) is utilized to analyze the optical response. The single lens and CT-CCD combination not only reduces the cost of the detection system by tenfold, but also decreases the collection system size and weight compared to heavy telescopic-based intensified CCD systems. All the samples investigated in this study were collected from a local recycling plant. All the measurements were performed with only a single laser shot which enables rapid identification while probing a large number of samples in real time. Furthermore, principal component analysis has shown excellent separation among the samples and an artificial neural network analysis has revealed that plastic waste can be identified within ∼10 ms only (testing time) with accuracies up to ∼99%. Finally, these results have the potential to build a compact and low-cost ST-LIBS detection system for the rapid identification of plastic waste for real-time waste management applications.

A Low-Cost Polarization Detection Approach Enabled by Energy Harvesting Metasurface: Concept, Design, and Experiment

A Low-Cost Polarization Detection Approach Enabled by Energy Harvesting Metasurface: Concept, Design, and Experiment

Self-supervised learning-based re-parameterization instance segmentation for hazardous object in terahertz image

Recently, terahertz imaging technology has shown widespread potential applications in the public security screening field. Previous terahertz hazardous object detection techniques primarily relied on manual recognition and image processing methods. Some solutions incorporating deep learning technologies depend on large quantities of high-quality data, making it challenging to achieve low-cost and high-performance detection. To solve the issue of dependency on large amounts of high-quality data, we propose a diversified dynamic structure You Only Look Once (DDS-YOLO) model based on masked image modeling and structural reparameterization for the instance segmentation of hazardous objects in terahertz security inspection images. To address the scarcity and annotating difficulty problems of terahertz security inspection image samples, we combine automatic data strategies with masked image modeling for self-supervised learning. We propose a multilevel feature refinement fusion mechanism to enhance the quality of learned feature representations. The backbone parameter transfer and fine-tuning training strategies are employed to achieve hazardous object instance segmentation on the terahertz dataset. To address the low detection accuracy issue caused by the poor terahertz image quality, we develop a reparameterizable hierarchical structure for the backbone, improve a multi-scale feature-integrating neck, and design a dynamically decoupled head with lower computational requirements to enhance the performance of the instance segmentation model. Experimental results demonstrate that the proposed model accurately outputs detection boxes, categories, and segmentation masks for hazardous objects with minimal training samples. The comparative experimental results indicate that the proposed model outperforms existing state-of-the-art methods in terms of detection performance. The proposed DDS-YOLO model achieves 59.3 % in mask mean Average Precision (mAP) and 61.3 % in box mAP, and the model parameters and computational requirements also meet practical application scenarios.

Ultrafast detection of bisulfite by a unique quinolinium-based fluorescent probe and its applications in smartphone-assisted food detection and bioimaging

Sulfur dioxide (SO2) is one of the major pollutants in the atmosphere, which is highly susceptible to inhalation by the human body and is converted into its derivatives (HSO3−/SO32−), which is hazardous to both human health and the ecological environment. Therefore the detection of SO2 derivatives (HSO3−/SO32−) is very important. In this work, we have prepared ID-QL, a water-soluble fluorescent probe based on the intramolecular charge transfer (ICT) mechanism, it exhibits colorimetric and fluorescent dual-channel response to HSO3− with ultrafast, highly selective and sensitive detection. In particular, ID-QL can be used for quantitative detection of HSO3− in real food samples. We developed a portable test strip for ID-QL and successfully combined it with smartphone to achieve convenient, low-cost and portable detection of HSO3− in real samples. The probe displays good mitochondrial targeting ability and can be used for visual monitoring and imaging of sulfites in live cells and zebrafish.

A Low-Cost and stable DAL arrhythmia detection algorithm based on the weak stratification query strategy of morphological statistical features

The annotation cost of electrocardiogram (ECG) data is extremely high, resulting in a lack of labeled data. However, most existing models are based on supervised learning and highly dependent on labeled data. Therefore, this study proposes a low-cost and stable arrhythmia detection algorithm based on deep active learning to reduce annotation costs and develop a model with a low dependence on labeled data. The algorithm first proposes a Skew series query strategy based on the weak stratification of morphological statistical features, especially for ECG data, including Skew, Skewierste, Skewier-C, and Skewier-C/2, and develops a CNN-based classifier. Finally, experiments verified that the query strategy proposed in this sstudy has higher stability and adaptability than other classical ECG strategies, and that the performance of the proposed CNN is also higher than that of other classical classifiers. The heartbeat detection algorithm based on deep active learning (DAL) proposed in this study can significantly reduce the dependence on labeled data, significantly reducing annotation costs.