Cost-effective Sensor Research Articles

Development And Testing of Arduino Based Autonomous Environmental Monitoring System

This paper presents the development of an Autonomous Environmental Monitoring System (AEMS) designed to track various environmental parameters. With the growing demand for autonomous monitoring systems in today’s modern era, we propose an Arduino-based real-time hardware solution to measure parameters such as flame, temperature, humidity, and distance. The system leverages advanced and cost-effective electronic sensors and hardware to monitor these environmental factors. We developed individual sensor modules for each parameter and integrated them into a unified multi-sensor system through sensor fusion. The collected data is then displayed on a 16 × 2 LCD module. The result is a fully functional autonomous monitoring system, tested under laboratory environmental conditions using a compact, handheld device.

Low-Cost Sensors for the Measurement of Soil Water Content for Rainfall-Induced Shallow Landslide Early Warning Systems

Monitoring soil water content (SWC) can improve the effectiveness of early warning systems (EWSs) designed to mitigate rainfall-induced shallow landslide risk. In extensive areas, like along linear infrastructures, the adoption of cost-effective sensors is critical for the EWS implementation. The present study aims to evaluate the reliability of different low-cost SWC sensors (frequency domain reflectometry and capacitance-based) in capturing soil moisture conditions critical for EWS, without performing soil-specific calibration. The reliability of the low-cost sensors is assessed through a comparative analysis of their measurements against those from high-cost and well-established sensors (time domain reflectometry) over a two-year period in a shallow landslide-prone area of Oltrepò Pavese, Italy. Although no landslides are observed during the monitoring period, meteorological conditions are reconstructed and statistical analysis of sensor’s responses to different rainfall events is conducted. Results indicate that, despite differences in absolute readings, low-cost sensors effectively capture relative SWC variations and demonstrate sensitivity to rainfall events across both cold and warm periods. The presented low-cost sensors can serve as reliable indicators of soil infiltration and saturation levels, highlighting their potential for real-time monitoring within extensive networks for EWS.

Improved hydrogen detection in SnO2-based materials using CuxPd bimetallic catalyst

Hydrogen (H2) holds promise as a replacement for conventional fossil fuels in future energy systems. However, developing a rapid, highly sensitive, and cost-effective H₂ sensor remains a significant challenge. This study first uses a low-cost-oriented CuxPd binary alloy for SnO2 for H2 sensing, and the corresponding properties are evaluated comprehensively. The results of H2 sensitivity demonstrate that our bimetallic CuxPd significantly enhances the sensitivity of SnO2, which is attributed to the synergistic effect of CuxPd bimetallic catalysts Our CuxPd/SnO2 nanocomposite materials not only exhibit high response (401 @ 1000 ppm at 100 °C), rapid response/recovery time (1/12 s @ 1000 ppm at 100 °C) and still maintains good response (1.42) to 1 ppm H2 gas. The introduction of Cu effectively stabilizes Pd and creates more oxygen vacancies, which provides a more catalytic effect than monometallic Pd. Therefore, this work provides a scenario for low-cost catalysis of H2 detection.

A Sensitive and Quick Fluorescent Sensor for the "Turn-On" Detection and Imaging of Glutathione Based on Sulfur Quantum Dots and MnO2 Nanosheets.

Glutathione (GSH) is one of the most abundant bioethanol antioxidants in living cells. Here, a fluorescent probe based on MnO2 nanosheets and sulfur quantum dots (SQDs) was fabricated. Because of the synergistic effect of IFE and FRET, the fluorescence from SQDs was quenched by MnO2 nanosheets. In the presence of GSH, the fluorescence of SQDs could be recovered because of the reduction of MnO2 nanosheets by GSH. The method can detect GSH in the concentration range of 5 ~ 1000 μM with the detection limit as low as 1.26 μM. This quick, easy, and cost-effective sensor could be used for the quantification of GSH in serum samples and the imaging of GSH in Escherichia coli O157:H7.

Low-Cost IoT-Based Sensors Dashboard for Monitoring the State of Health of Mobile Harbor Cranes: Hardware and Soft-ware Description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Label-Free Electrochemical Sensor Sensitivity Enhancement Using Field Effect Transistors: Fluoride Ions for Fuel Cell Degradation Analysis

In the context of proton exchange membrane fuel cells, precise monitoring of fluoride ions in the effluent water is crucial. Associated with the fluoride ion accumulation, membrane degradation and reduction of fuel cell efficiency arise. This study focuses on developing extended gate field-effect transistors (EGFET) sensors for real-time monitoring of fluoride ion concentrations. High sensitivity and specificity in detecting the concentration of fluoride ions using both N-type (TN0702) and P-type (LP0701) metal-oxide-semiconductor field-effect transistors (MOSFET) were achieved. The transfer characteristics (Id vs Vgs) owere thoroughly examined across a range of fluoride concentrations from parts per million (ppm) to parts per billion (ppb). N-type MOSFET-based sensors showed a distinct decrease in drain current (Id) with increasing fluoride concentration. With fluoride concentrations changed, the P-type MOSFET exhibited a notable shift in the threshold voltage (Vgs). A robust linear relationship was observed on calibration curves with R2 values exceeding 0.90 between the natural logarithm of fluoride concentration (lnC) and gate-source voltage (Vgs). This work demonstrates that MOSFET can significantly enhance the sensitivity of electrochemical sensors without complex labeling processes. The improvement has a future in the miniaturization of cost-effective sensor systems for fuel cell applications, facilitating real-time performance and degradation monitoring.

Laser-induced graphene gas sensors for environmental monitoring.

Artemesia tridentata is a foundational plant taxon in western North America and an important medicinal plant threatened by climate change. Low-cost fabrication of sensors is critical for developing large-area sensor networks for understanding and monitoring a range of environmental conditions. However, the availability of materials and manufacturing processes is still in the early stages, limiting the capacity to develop cost-effective sensors at a large scale. In this study, we demonstrate the fabrication of low-cost flexible sensors using laser-induced graphene (LIG); a graphitic material synthesized using a 450-nm wavelength bench top laser patterned onto polyimide substrates. We demonstrate the effect of the intensity and focus of the incident beam on the morphology and electrical properties of the synthesized material. Raman analyses of the synthesized LIG show a defect-rich graphene with a crystallite size in the tens of nanometers. This shows that the high level of disorder within the LIG structure, along with the porous nature of the material provide a good surface for gas adsorption. The initial characterization of the material has shown an analyte response represented by a change in resistance of up to 5% in the presence of volatile organic compounds (VOCs) that are emitted and detected by Artemisia species. Bend testing up to 100 cycles provides evidence that these sensors will remain resilient when deployed across the landscapes to assess VOC signaling in plant communities. The versatile low-cost laser writing technique highlights the promise of low-cost and scalable fabrication of LIG sensors for gas sensor monitoring.

Ruthenium(II) N-heterocyclic carbene polymer based sensors for detection of predatory drugs like ketamine and scopolamine.

The current demand in the field of abusive drug research is to have a highly sensitive and rapid method for detecting ketamine and scopolamine, which are frequently used in drug-facilitated sexual assaults administered via beverages and food. We report here the first electrochemical sensors of N-heterocyclic carbene (NHC) coordinated ruthenium organometallic 1-dimensional polymers and their multi-walled carbon nano tube (MWCNT)-based composite for detecting ketamine and scopolamine. The preparation of ruthenium NHC organometallics and the MWCNT composite as sensors offers significant advantages for electrochemical applications, with enhanced sensitivities of 121.979 and 3.273 μA μM-1 cm-2 for ketamine and scopolamine, respectively, and selectivity in sensing applications. The complex-carbon composite sensor has a low limit of detection i.e., 0.194 and 3.18 nM for ketamine and scopolamine identification, respectively. Alongside, the selectivity of the composite sensor was evaluated in the presence of other blood constituents, and the study evidenced remarkable discernment towards the title drugs. Furthermore, real-time analyses using the composite sensor demonstrated quantitative identification of ketamine and scopolamine. Therefore, this innovation has the potential to provide valuable tools for forensic investigations and address the urgent need for real-time detection of date rape drugs. This contribution demonstrates a robust proof-of-concept that emphasizes the importance of creating non-enzymatic, environmentally friendly, and cost-effective sensors for on-site sensing applications as point-of-care devices.

Cost-Effective Multispectral Sensor and Artificial Neural Networks for the Detection of Starch Adulteration in Raw Milk

This study aims to detect starch adulteration in dairy products utilizing an artificial neural network (ANN) model. Globally, milk fraud represents a significant challenge to food safety, posing substantial health risks to consumers. In this context, spectral data derived from milk samples with varying starch concentrations were processed using feature scaling and normalization techniques. The ANN model was rigorously trained and validated employing the stratified k-fold cross-validation method, demonstrating exceptional proficiency in detecting starch-adulterated milk samples and effectively differentiating among various starch concentrations. The principal findings indicate that the model achieved 100% accuracy, coupled with high levels of precision, sensitivity, and F1-scores. Future research should explore the application of this model to different types of adulteration and extend its validation on larger datasets. Furthermore, the potential adaptability of this method for other food products and field applications warrants investigation. This study offers valuable insights for milk producers, food safety professionals, and consumers, particularly highlighting the implications for small-scale rural farms, thereby enriching the discourse on food safety within short food supply chains.

A Metal-Organic Framework-Based Colorimetric Sensor Array for Transcutaneous CO2 Monitoring via Lensless Imaging

Transcutaneous carbon dioxide (TcPCO2) monitoring provides a non-invasive alternative to measuring arterial carbon dioxide (PaCO2), making it valuable for various applications, such as sleep diagnostics and neonatal care. However, traditional transcutaneous monitors are bulky, expensive, and pose risks such as skin burns. To address these limitations, we have introduced a compact, cost-effective CMOS imager-based sensor for TcPCO2 detection by utilizing colorimetric reactions with metal–organic framework (MOF)-based nano-hybrid materials. The sensor, with a colorimetric sensing array fabricated on an ultrathin PDMS membrane and then adhered to the CMOS imager surface, can record real-time sensing data through image processing without the need for additional optical components, which significantly reduces the sensor’s size. Our system shows impressive sensitivity and selectivity, with a low detection limit of 26 ppm, a broad detection range of 0–2% CO2, and strong resistance to interference from common skin gases. Feasibility tests on human subjects demonstrate the potential of this MOF-CMOS imager-based colorimetric sensor for clinical applications. Additionally, its compact design and responsiveness make it suitable for sports and exercise settings, offering valuable insights into respiratory function and performance. The sensing system’s compact size, low cost, and reversible and highly sensitive TcPCO2 monitoring capability make it ideal for integration into wearable devices for remote health tracking.

Calibration of NO, SO2, and PM using Airify: A low-cost sensor cluster for air quality monitoring

During the past decade, substantial efforts have been dedicated to advancing cost-effective sensor platforms for air quality monitoring. Calibration is a crucial step in ensuring that the data quality of low-cost air monitoring systems meets established standards. Recent research has extensively evaluated low-cost air monitoring platforms against the data quality objectives set by the European Directive. This paper introduces a novel calibration model for low-cost air quality sensors, significantly improving the accuracy of the measurement of nitric oxide (NO), sulfur dioxide (SO2), and particulate matter (PM1, PM2.5, and PM10), while promoting accessibility and adaptability in environmental monitoring technologies. This study extends the evaluation of a developed platform capable of integrating a diverse array of sensors to measure up to 12 parameters. Our proposed models demonstrate a significant improvement, achieving a 60% better accuracy for SO2. Additionally, these models deliver similar results for PMx and NO or exceed those of state of the art research. The calibration methodology meets the requirements of the Data Quality Objectives (DQO) for all monitored parameters and also achieves indicative levels for PM parameters.

A highly sensitive and selective ZnO-based ammonia sensor: Fe- doping effect

Abstract This study investigates the gas sensing capabilities of ZnO and Fe-doped ZnO films prepared via a cost-effective spin coating method. The films were characterized optically, structurally, and morphologically. X-ray diffraction revealed good crystal quality with a polycrystalline nature, and wurzite structure, though crystal quality and crystallite size decreased with Fe doping. Optical measurements indicated an increased optical band gap from 3.205 ± 0.002 to 3.220 ± 0.002 eV after Fe doping. Scanning electron microscope images confirmed spherical grainy structures with reduced grain sizes after Fe doping. Gas sensing measurements at room temperature (RT) at an exposure of vapors of various toxic chemicals (acetone, ethanol, propanol, methanol, and ammonia), demonstrated a highly selective nature of ZnO and Fe-ZnO towards the ammonia. The Fe-doping into ZnO improved the ammonia sensing capability of ZnO film. The Fe-ZnO film exhibited a gas response of 388.8 ± 5.5 at 400 ppm ammonia exposure, which was nearly 10 times larger than that of ZnO film with a response/recovery time of 22/51 s, good stability, and good repeatability. The Fe-ZnO film’s higher response is attributed to its smaller grain sizes and surplus of charge carriers after Fe doping which promote the adsorption of extra oxygen ions onto the film’s surface and the subsequent interaction between the adsorbed oxygen ions ammonia molecules. It could detect up to the lower limit of 1 ppm ammonia with a response of 24.2 ± 0.9, which is better than the previous reports. These results reveal the Fe-ZnO film as a viable material for developing a cost-effective and efficient ammonia sensor at room temperature.

Integrated One-Step Fabrication of Protonic Sensing Devices for Respiratory Monitoring.

The development of direct fabrication routes with seamless integration of both the macro/micropatterning process and nanostructure synthesis is crucial for the commercial realization of cost-effective nanoscale sensor devices. This, if realized, can liberate us from the conventional limitations inherent in nanoscale device manufacturing. Specifically, such fabrication routes can, in principle, address the challenges such as the complexity, multistep nature, and substantial costs associated with existing technologies, which are not suitable for widespread market adoption in everyday-use devices. Herein, we propose a novel yet facile one-step fabrication approach that simultaneously accomplishes both patterning and nanostructure synthesis by employing low-power, cost-effective laser technology with a minimal environmental footprint. Versatile in nature, this approach can enable the incorporation of diverse functionalities spanning a broad spectrum of technologies, encompassing fields such as sensors, catalysts, photonics, energy storage, and biomedical monitoring devices. As a proof of concept, using our approach, we fabricated an ultra responsive, high-speed protonic sensing device for real-time respiratory monitoring. The enhanced temporal characteristics of our as-fabricated device, particularly in the relative humidity levels of interest in breath monitoring (typically over 55%), exhibited a superior response/recovery time in rapid humidity fluctuations. We envisage that the advantages brought by the presented fabrication approach can pave the way to establish a new method for inexpensive large-scale functional-material-based device fabrication.

A novel, portable, and cost-effective turbidimetric sensor for sensitive alkaline phosphatase activity assay

The development of enzyme activity analysis methods is critical for precise and rapid assessments of enzyme activity levels within biological systems, facilitating a more profound comprehension of physiological functions and disease mechanisms. Alkaline phosphatase (ALP) participates in various physiological processes involving phosphate ester hydrolysis. Altered ALP activity levels are often indicative of different diseases, underscoring the necessity for accurate ALP activity determination in medical diagnostics. This study innovatively applies turbidity as a physical variable, proposing a turbidimetric sensor based on an enhanced ammonium molybdate reagent for phosphate analysis. By integrating this with the ALP substrate p-nitrophenyl phosphate, a turbidimetric sensor was devised and employed for ALP activity analysis. The proposed turbidimetric sensor demonstrated high sensitivity both for phosphate (0.18 μmol/L) and ALP activity (0.03 mU/mL) assay. In practical applications, this turbidimetric sensor has been effectively employed to detect ALP activity in mouse feces, showcasing its potential for auxiliary diagnosis of inflammatory bowel disease. Significantly, this novel turbidity-based approach offers not only swift and straightforward procedures but also remarkable portability and cost-efficiency. Requiring solely a handheld turbidimeter and eliminating the need for bulky instruments, this approach holds significant potential for point-of-care testing applications.

Innovative Use of Carbon Nanofibers/Praseodymium Cobaltite for Targeted Detection of Hematologic Sulfamethazine.

Antibiotics are essential for treating illnesses, but abuse has resulted in serious consequences. Rapid and precise detection of antibiotic residues, such as sulfamethazine (SFZ), in water and biological samples is critical for public health and environmental safety. To address this challenge, we have introduced a pioneering electrochemical sensor incorporating a nanocomposite of perovskite-structured praseodymium cobaltite (PrCoO3) integrated with carbon nanofibers (CNFs) on a glassy carbon electrode (GCE|CNF/PrCoO3). We synthesized the CNF/PrCoO3 nanocomposite using ultrasonic fabrication and confirmed its formation with advanced techniques. GCE|CNF/PrCoO3 offer superior SFZ detection with a 2.889 nM/L limit and high selectivity, due to PrCoO3's electrocatalytic properties and CNF's enhanced conductivity. We validated the sensor's effectiveness in detecting SFZ in various real-water samples, demonstrating its repeatability, reproducibility, and stability. This confirms its reliability for environmental monitoring. The study highlights the potential of perovskite-carbon composites and paves the way for developing cost-effective sensors for pharmaceutical contaminants.

3D alignment of distant patterns with deep-subwavelength precision using metasurfaces

Measurement of the relative positions of two objects in three dimensions with sub-nanometer precision is essential to fundamental physics experiments and applications such as aligning multi-layer patterns of semiconductor chips. Existing methods, which rely on microscopic imaging and registration of distant patterns, lack the required accuracy and precision for the next generation of three-dimensional (3D) chips. Here we show that 3D misalignment between two distant objects can be measured using metasurface alignment marks, a laser, and a camera with sub-nanometer precision. Through simulations, we demonstrate that the shot noise-limited precisions of the lateral and axial misalignments between the marks are λ0/50, 000 and λ0/6, 300 (λ0: laser’s wavelength), respectively. With its high precision and simplicity, the technique enables the next generation of 3D-integrated optical and electronic chips and paves the way for developing cost-effective and compact sensors relying on sub-nanometer displacement measurements.

Highly sensitive colorimetric detection of Cd(ii) based on silica sol modified with dithizone and cationic surfactant.

The cadmium ion (Cd2+) is highly poisonous and nondegradable and easily bioaccumulates through the food chain. Therefore, it is crucial to develop cost-effective chemical sensors for Cd2+ with fast response time, high selectivity, and very low detection limits. In this study, a colorimetric sensor for the determination of Cd2+ was fabricated by modifying silica sol with dodecyltrimethylammonium bromide (DTAB) and dithizone (DZ). Cd2+ formed a complex with DZ, changing the solution color immediately from purple to orange prior to detection using ultraviolet-visible spectrophotometry and a customized Cd analyzer for the precipitate. Under the optimum conditions, the developed Cd2+ sensor had a linear range of 0.01-0.25 mg L-1, a low limit of detection of 5.0 μg L-1, and outstanding repeatability. This sensor also showed good precision, with the relative standard deviations of less than 2.59% and 3.24% for the intra- and inter-day data, respectively. The proposed colorimetric method was successfully applied to determine Cd2+ in environmental water samples, and the results were comparable to those obtained using standard atomic absorption spectrometry. Moreover, quantitative analysis was conducted using the customized Cd analyzer to estimate the color intensity change, without requiring sophisticated scientific instruments. This colorimetric sensor can be used for the portable, cost-effective, and rapid on-site detection of Cd2+ in environmental water samples.

Temperature-insensitive and cost-effective distributed NP-Doped optical fiber sensors

This paper presents the development of a cost-effective distributed optical fiber sensor for temperature-insensitive assessment of mechanical disturbances along an optical fiber cable. The proposed sensor system uses a nanoparticle (NP)-doped optical fiber with enhanced Rayleigh backscattering to provide higher sensitivity and spatial resolution using the transmission and reflection analysis (TRA) approach, where the transmitted and backscattered optical powers are analyzed as a function of the mechanical disturbance. In addition, Fiber Bragg Gratings (FBGs) are used as wavelength filters to provide the wavelength division multiplexing of the proposed device, which enable the use of 3 different NP-doped optical fiber sections for simultaneous detection of multiple curvature conditions in a cost-effective distributed sensing approach. The sensor characterization tests are performed by means of applying curvature angles from 360° to 1080° at different positions along NP-doped fibers (namely 25 mm, 100 mm and 175 mm) at 4 different temperatures of 25 °C, 30 °C, 40 °C and 50 °C. The results indicate the feasibility of the proposed approach, where the temperature variations lead only to a wavelength shift of the Bragg wavelength, whereas the mechanical disturbances (the curvatures) lead only to variations in the transmitted and reflected optical powers. Thus, by analyzing the transmitted and reflected optical powers in conjunction with the Bragg wavelength shift, it is possible to estimate both the mechanical disturbance amplitude (i.e., curvature angle) and the position along each NP-doped optical fiber section. Results indicate a relative error of around 3 % and 4 % for the mechanical disturbance location and absolute value, respectively. Moreover, the temperature cross-sensitivity in this case is below 2 % considering both amplitude and location of the mechanical disturbance. The proposed approach can be applied in structural health monitoring of different types of structures by integrating the fibers in the structures themselves with the possibility of measuring the strain distribution along the fibers (instead of in different points along the fiber) using a lower cost hardware when compared with similar distributed optical fiber sensing approaches.

Development and deployment of a mid-cost CO2 sensor monitoring network to support atmospheric inverse modeling for quantifying urban CO2 emissions in Paris

Abstract. To effectively monitor highly heterogeneous urban CO2 emissions using atmospheric observations, there is a need to deploy cost-effective CO2 sensors at multiple locations within the city with sufficient accuracy to capture the concentration gradients in urban environments. These dense measurements could be used as input of an atmospheric inversion system for the quantification of emissions at the sub-city scale or to separate specific sectors. Such quantification would offer valuable insights into the efficacy of local initiatives and could also identify unknown emission hotspots that require attention. Here we present the development and evaluation of a mid-cost CO2 instrument designed for continuous monitoring of atmospheric CO2 concentrations with a target accuracy of 1 ppm for hourly mean measurements. We assess the sensor sensitivity in relation to environmental factors such as humidity, pressure, temperature and CO2 signal, which leads to the development of an effective calibration algorithm. Since July 2020, eight mid-cost instruments have been installed within the city of Paris and its vicinity to provide continuous CO2 measurements, complementing the seven high-precision cavity ring-down spectroscopy (CRDS) stations that have been in operation since 2016. A data processing system, called CO2calqual, has been implemented to automatically handle data quality control, calibration and storage, which enables the management of extensive real-time CO2 measurements from the monitoring network. Colocation assessments with the high-precision instrument show that the accuracies of the eight mid-cost instruments are within the range of 1.0 to 2.4 ppm for hourly afternoon (12:00–17:00 UTC) measurements. The long-term stability issues require manual data checks and instrument maintenance. The analyses show that CO2 measurements can provide evidence for underestimations of CO2 emissions in the Paris region and a lack of several emission point sources in the emission inventory. Our study demonstrates promising prospects for integrating mid-cost measurements along with high-precision data into the subsequent atmospheric inverse modeling to improve the accuracy of quantifying the fine-scale CO2 emissions in the Paris metropolitan area.

Silver amplified immunosensor via effective fluorogenic Ag+-imidazole aggregation for detection of AFB1

BackgroundCereals are susceptible to aflatoxin contamination during storage and transportation, which is highly carcinogenic and teratogenic, and seriously threaten human health. The accurate and rapid detection of total aflatoxin (including aflatoxin B1, B2, G1, and G2) is of great importance for food safety. Conventional fluorescence immunoassays have the advantage of being sensitive and fast; however, these methods can be affected by strong background and matrix interference. Therefore, the development of ultrasensitive, cost-effective, and interference rejection sensors for detecting aflatoxins in moldy grains is vital for food safety and human health. ResultsIn this paper, a broad-spectrum aflatoxin monoclonal antibody was prepared by using hybridoma cell fusion technology. An aggregation-induced emission (AIE) based immunosensor via silver amplification coupled with a fluorogenic Ag+ probe was established for AFB1 analysis. Silver nanoparticles are decomposed into numerous Ag+ by H2O2, and then Ag+ further specifically binds with imidazole-modified AIE molecules, improving the sensitivity and anti-interference ability of the method. The IC50 and IC15 of AIE-based immunosensor for AFB1 were 0.019 and 0.0014 μg/L, respectively, 2.3-fold and 5.8-fold higher than those of icELISA. The AIE-based immunosensor was also used to analyze AFB1 from actual cereal samples, with spiked recoveries ranging from 72.91 to 115.92 %. In addition, the method was used to detect total aflatoxins in moldy grains. SignificanceBased on the advantages of broad-spectrum aflatoxin monoclonal antibody, high-efficiency metal signal amplification, and functional AIE molecule, a sensitive, accurate, cost-effective, and time-saving method was developed for the analysis of total aflatoxins in cereals. Moreover, the proposed signal amplification strategy shows great potential for analyzing other trace-level small molecular pollutants.