Cost-effective Sensor Research Articles

Comparative Study of Natural Fibres to Improve Insulation in Wooden Beehives Using Sensor Networks

The beekeeping sector is increasingly focused on creating optimal and natural environments for honeybees to reduce dependence on external factors, especially given progressively hotter summers. Improving hive thermal conditions can enhance bee wellbeing and production. While pinewood hives are predominant, some have started using insulating materials like polystyrene. However, many synthetic materials, despite their excellent insulation properties, are incompatible with organic food production, requiring alternative solutions. This study compares the thermal insulation properties of various natural materials, including white and black agglomerated cork, wood fibres, and rock mineral wool. These materials are potentially compatible with organic food production. Additionally, the research evaluates cost-effective sensor networks to monitor bioclimatic variables in real time. Lab tests using a Langstroth-type hive with a controlled heat source were conducted, monitoring temperature and humidity inside and outside the hive. The results revealed that all selected materials provided similar thermal insulation, superior to a hive without insulation. This finding suggests that using natural materials can enhance hive thermal comfort (i.e., the material’s ability to maintain a stable internal temperature), thereby improving honeybee wellbeing and productivity in a manner compatible with organic food production.

A Framework for Condition Survey and Structural Health Monitoring of Masonry Arch Bridges

Masonry arch bridges are essential elements of the road and railway networks of Europe, as they pertain to cultural heritage and play an important role in transportation. The integrity and safety of these centuries-old structures must be ensured. Therefore, there is great interest in understanding the behavior of masonry arch bridges and monitoring their health. Efficient methods for assessing the condition of structures, including inspections and monitoring, constitute a fundamental element of structural health monitoring (SHM) applications. Given the multitude of surveying techniques available, it becomes crucial to establish cost-effective sensor placement strategies capable of detecting structural failures. These strategies can involve identifying particularly sensitive areas, optimizing the number of sensors used, as well as refining the number and duration of monitoring setups, among other considerations. As part of the PONT3 project, which aims to develop a comprehensive and cost-effective approach to anticipate failure propagation in ageing bridges, this research focusses on developing interoperability-driven inspection and monitoring strategies to provide data that will allow anticipating the evolution of possible failure scenarios. In this regard, an extensive summary of the key characteristics of inspection and monitoring technologies, and typical system configurations in existing literature for masonry arch bridges will be provided. Then, the measurable physical parameters of interest for a specific failure propagation scenario will be identified using numerical simulations and applicable monitoring techniques for each of the physical parameters will be determined. Monitoring requirements in terms of measurement locations, accuracy, range, and temporal resolution will also be identified. The recommendations for interoperability-driven data collection to exchange and employ acquired information for the purpose of structural diagnosis will conclude the framework. The study will finally discuss a future outlook on the development of integrated SHM-oriented digital twins.

Dynamic object detection using sparse LiDAR data for autonomous machine driving and road safety applications

Light Detection And Ranging (LiDAR) sensor offers solutions to extract real-time information of vehicle’s surroundings and can provide a new opportunity to improve the road safety related issues in mixed traffic conditions. This paper explores fundamental principles and combination of algorithms, to accurately detect, classify and track surrounding vehicles on expressways using a vehicle mounted cost-effective LiDAR sensor in dynamic conditions. The proposed approach employs algorithms such as Density-Based Spatial Clustering of Applications with Noise, Random sample consensus, and Kalman filter on 3D LiDAR data for ground and non-ground points segmentation, object clustering, road boundary identification, vehicle classification and tracking. Trajectory data of the tracked vehicles is used to estimate their relative positions and speeds. The surrounding vehicles were classified into three categories: two-wheelers, four-wheelers and heavy-vehicles. The proposed method achieves an accuracy, precision, recall, and F1-score above 94 %, demonstrating its robustness. The resilience and versatility of the proposed algorithm were validated through comprehensive evaluations on a significant dataset spanning over 8000 km (around 3.6 million frames) of driving data collected under varied environmental scenarios on expressway. Further, various applications of proposed methodology in road safety studies are discussed. The approach can extract real-time micro-level trajectory data for surrounding vehicles on expressways in any lighting condition, which is useful for researchers and can be used in connected and autonomous vehicles. It also provides valuable information for assessing surrogate safety measures and identifying road safety issues and suggesting countermeasures, including advanced and cost-effective driver assistance system applications.

Development and Initial Evaluation of a Cost-Effective Force Sensor for Ureteroscopic Application.

Purpose: Retrograde intrarenal surgery is the gold-standard treatment for most kidney stones. During ureteroscopy, ureteral access sheath insertion at forces greater than 8.0 Newtons (N) risks high-grade ureteral injury. To monitor force, our institution utilizes a unique, Bluetooth-equipped device (i.e., the University of California-Irvine Force Sensor). Given the unique nature of the force sensor, we sought to develop an inexpensive and accessible force sensor based on Boyle's law and the specific amount of force required to compress an occluded 1.0 mL syringe. Materials and Methods: We evaluated three brands of 1.0 mL syringes. After setting the plunger at 1.0 mL, the syringe was occluded, and the syringe plunger was compressed. The syringe volume was recorded when the applied force on the plunger reached 4.0 N, 6.0 N, and 8.0 N. Multiple trials were performed to assess reliability and reproducibility. A method for applying this clinically was also developed. Results: The precise force thresholds identified for a 1.0 mL Luer-Lok™ Syringe (Becton Dickinson, Franklin Lakes, NJ) were 0.30 mL for 4.00 N, 0.20 mL for 6.00 N, and 0.15 mL for 8.00 N. The 1.0 mL Tuberculin Syringe and 1.0 mL Luer Slip Syringe were less precise, but compression from 1.0 to 0.40 mL, 0.25 mL, and 0.20 mL corresponded to force sensor readings that did not exceed 4.00 N, 6.00 N, and 8.00 N, respectively. Conclusions: Based on volume changes, 4.00 N, 6.00 N, and 8.00 N of force can be reliably and reproducibly achieved using an occluded 1.0 mL syringe.

A data-driven method for estimating sewer inflow and infiltration based on temperature and conductivity monitoring

Quantitation of sewer inflow and infiltration (I/I) is important for maintaining efficient wastewater transport and treatment. I/I flows can be quantified based on flow rate and water quality measurements. Flow rate-based methods require continuous monitoring of flow rates using flow meters that are costly and prone to fouling. In comparison, conductivity and temperature, as simple water quality parameters, are more easily measurable with more cost-effective and reliable sensors. In this study, a data-driven methodology is developed for estimating I/I flows based on online conductivity and temperature measurements. A Prophet-model-based analytic algorithm is first developed to reconstruct the temperature and conductivity profiles of the base wastewater flow (BWF) from the measured temperature and conductivity time series. The algorithm is shown to be able to reconstruct the BWF temperature and conductivity profiles in two monitored catchments. The reconstructed BWF data are then incorporated into mass/energy balance equations for estimating I/I flows from the measured temperature and conductivity data. The overall I/I quantification method is finally demonstrated using simulation studies of a real-life sewer network and validated against the known I/I flows. This work provides a reliable method for I/I quantification based on simple measurements.

Advanced nutrient monitoring: Cost-effective cerium ion detection with smart device integration

This research is critically important for large-scale poultry farming, where vigilant monitoring is necessary to assess nutrient levels in poultry feed. Detecting cerium in poultry feed through colorimetric techniques is crucial for evaluating nutrient intake, feed quality, animal health, and broader environmental impacts. This study significantly contributes to ongoing research and monitoring efforts, aiding in maintaining an optimal mineral balance and enhancing chicken quality and overall poultry performance. The study utilized Alizarin Red dye (AR), both individually and in combination with Eriochrome Black T (EBT), to identify the presence of cerium ions. Various experimental conditions such as pH, concentration, temperature, and specificity were thoroughly examined. The UV-Vis absorption spectra indicated that the average minimum detectable limit for cerium ions is approximately 5 ppm, with a detection range of 0.2–3 mM. Additionally, a cost-effective paper-based sensor and a portable colorimetric method for detecting cerium ions were innovatively designed. This paper-based sensor ensures precise detection at room temperature, demonstrating high sensitivity and selectivity. The detection system was integrated with smart devices, enabling the swift capture of Red, Green, and Blue (RGB) values for practical real-time applications. This integration allows for rapid on-site identification of cerium ions. The introduction of this affordable, accurate, and portable colorimetric method for monitoring cerium ions represents a promising advancement in developing accessible tools within this field.

Self-Powered Acceleration Sensor for Distance Prediction via Triboelectrification.

Accurately predicting the distance an object will travel to its destination is very important in various sports. Acceleration sensors as a means of real-time monitoring are gaining increasing attention in sports. Due to the low energy output and power density of Triboelectric Nanogenerators (TENGs), recent efforts have focused on developing various acceleration sensors. However, these sensors suffer from significant drawbacks, including large size, high complexity, high power input requirements, and high cost. Here, we described a portable and cost-effective real-time refreshable strategy design comprising a series of individually addressable and controllable units based on TENGs embedded in a flexible substrate. This results in a highly sensitive, low-cost, and self-powered acceleration sensor. Putting, which accounts for nearly half of all strokes played, is obviously an important component of the golf game. The developed acceleration sensor has an accuracy controlled within 5%. The initial velocity and acceleration of the forward movement of a rolling golf ball after it is hit by a putter can be displayed, and the stopping distance is quickly calculated and predicted in about 7 s. This research demonstrates the application of the portable TENG-based acceleration sensor while paving the way for designing portable, cost-effective, scalable, and harmless ubiquitous self-powered acceleration sensors.

Exploring the efficacy of subwavelength gratings as short-wavelength infrared filters

Advancements in nanofabrication technology have greatly facilitated research on nanostructures and their associated properties. Among these structures, subwavelength components have emerged as promising candidates for ultra-compact optical elements, can potentially supplant conventional optical components and enable the realization of compact and efficient optical devices. Spectral analysis within the infrared spectrum offers a wealth of information for monitoring crop health, industrial processes, and target identification. However, conventional spectrometers are typically bulky and expensive, driving an increasing demand for cost-effective spectral sensors. Here we investigate three distinct subwavelength grating structures designed to function as narrowband filters within the short-wavelength infrared (SWIR) range. Through simple adjustments to the period of grating strips, these filters selectively transmit light across a wide wavelength range from 1100 to 1700 nm with transmission exceeding 70% and full width at half maximum (FWHM) down to 6 nm. Based on a simple design, the results present great potential of subwavelength grating filters for multiband integration and developing ultra-compact spectral sensors.

Colorimetric sensor to monitor the temperature with in situ synthesized silver nanoparticles embedded in carboxymethyl cellulose metallogel

A colorimetric sensing platform based on carboxymethyl cellulose-silver nanoparticles (CMC-AgNPs) metallogel was proposed to monitor the temperature history and quality of perishable products stored at low temperature. The carboxymethyl cellulose (CMC) metallogel, fabricated in the presence of Ag+, incorporates ionic crosslinking of biopolymers, complexation between reactants, and subsequent in situ synthesis of AgNPs, which are designed to lead the color transition of metallogel from colorless to dark brown depending on temperature and time. The mechanical and structural properties of metallogels and AgNPs were characterized by rheology, XRD, and FT-IR. Color changes associated with temperature, time, and metal ion precursors were examined by UV–visible spectroscopy and colorimetry. The longer the exposure time to thermal stress condition such as room temperature (25 °C) or high temperature (60 °C), the deeper the color of metallogel. The CMC-AgNPs metallogel-based sensor provides an efficient, safe way to track temperature history and assess perishable products’ quality, crucial for the safe distribution of food, vaccines, and medicines. This cost-effective and reliable visual sensor minimizes spoilage and health risks by enabling accurate temperature monitoring without the need for complicated equipment.

Bactericidal action of cost-effective colorimetric acid-base sensor of dyed cotton-based fabrics as a promising wound dressing mat

Acid-base sensitive fabrics is considered as one of interesting features for smart textile applications; therefore, this work presents a simple treatment of cotton fabric with 3-Chloro-2-hydroxypropyltrimethylammonium chloride (CHPTAC) followed by dyeing with curcumin dye. On the contrary, the dyed cationized cotton fabric exhibited a rough surface owing to the curcumin dye adsorption onto the surface of cationized cotton fabric. The interesting acid-base sensitivity of the dyed (dyed cationized cotton) fabric was observed and the color of dyed cationized cotton was changed from brownish yellow color to bright yellow color in acidic pH up to 6 while by increase the pH than 8 the color changed from brownish yellow to reddish brown color. The color change can easily be observed by naked eye. It was found that, dyed cationized cotton fabric exhibit the highest bactericidal activity compared with the undyed cationized cotton fabric, and untreated cotton fabric, which possessed the lowest antibacterial effect. The results show that a more serious case for dyeing cotton fabric leads to bacterial cell membrane permeability than untreated cotton fabric or a control group. Furthermore, the research showed that untreated, cationized and dyed/cationized cotton fabrics are unaffected by negative reactions. The dyeing process increased the functional performance of cotton fabric, as they have better anti-adhering capability. This technique can be tailored for biomedical especially with wound dressing, which designed to prevent the biofilm formation. Results obtained in this work recommend that dyed cationized cotton fabric with curcumin is an excellent pH sensitive for wound dressing material that can prevent the bacterial infection and control of biofilm adhesion.

Strain-Modulated Flexible Bio-Organic/Graphene/PET Sensors Based on DNA-Curcumin Biopolymer.

In recent years, there has been growing interest in the development of metal-free, environmentally friendly, and cost-effective biopolymer-based piezoelectric strain sensors (bio-PSSs) for flexible applications. In this study, we have developed a bio-PSS based on pure deoxyribonucleic acid (DNA) and curcumin materials in a thin-film form and studied its strain-induced current-voltage characteristics based on piezoelectric phenomena. The bio-PSS exhibited flexibility under varying compressive and tensile loads. Notably, the sensor achieved a strain gauge factor of 407 at an applied compressive strain of -0.027%, which is 8.67 times greater than that of traditional metal strain gauges. Furthermore, the flexible bio-PSS demonstrated a rapid response under a compressive strain of -0.08%. Our findings suggest that the proposed flexible bio-PSS holds significant promise as a motion sensor, addressing the demand for environmentally safe, wearable, and flexible strain sensor applications.

Monitoring of CO2 using MWCNTs functionalized clay porous composite for clean room facility

In this current work, a vastly sensitive, selective, and cost-effective CO2 gas sensor was developed by utilizing MWCNTs doped clay ceramics, for the developments in clean room facilities or pharmaceutical purposes. Herein, MWCNTs functionalized clay composites (CTsX; X = 0, 5, 10, and 15) were prepared by a bottom up approach i.e. solid-state reaction method and characterized by SEM with EDAX, XPS, XRD, FTIR, UV–visible spectroscopy, BET, and sensing. SEM micrographs revealed the created pores and grown nanotubes within the base material. XPS demonstrated some sites of Oads deficiencies, attributed to the expediting of sensing phenomena. Further, MWCNTs are well functionalized with clay as confirmed via XRD phases such as SiO2 (trigonal), AlKSi3O8 (monoclinic), Al2CaSi2O8 (triclinic) and C2Ca5Si2O13 (monoclinic). Furthermore, BET established the mesoporous nature of CTs15 composite having mean pore size ∼ 3.82 nm, also CO2 gas (200–1000 ppm) was tested at ambient temperature. Moreover, CTs15 composite showed maximum sensor response (S.R.) 5.31 at 1000 ppm of CO2 gas, whereas minimum response/recovery time (Tres./Trec.) 10.87/8.93 s were noticed at 200 ppm while operated at 28℃ (ambient condition). Additionally, LOD assured 217 ppm, which suggests that fabricated sensor is highly sensitive and selective for CO2, and may be used for clean room services. Therefore, this cost effective and eco-friendly composite is suitable for monitoring of CO2 gas for indoor and outdoor environments within a few seconds of Tres. and Trec..

Development of An Eco-Friendly and Cost-Effective Electrochemical Sensor for the Simultaneous Detection of Dopamine and Serotonin

An electrochemical sensor based on eco-friendly green synthesized silver nanoparticles decorated reduced graphene oxide (AgNPs-rGO) modified screen-printed carbon electrode (SPCE) for the simultaneous detection of serotonin (5-HT) and dopamine (DA) is reported for the first time. The experimental parameters affecting the sensor performance were optimized in terms of AgNPs-rGO coating amount, scan rate and electrolyte pH (6–8). Under optimal conditions, the AgNPs-rGO/SPCE was employed to individually determine both analytes using DPV technique. The sensor was also efficient in the simultaneous detection of these species and reported well-resolved oxidation peaks with a linear range of 10–100 μM and detection limits of 7 μM and 7.41 μM, respectively. The developed device showed good selectivity, reproducibility, and repeatability. Furthermore, it was successfully applied to the determination of both biomolecules in artificial urine samples with good recovery. The main advantages of the designed sensor are its simplicity, portability, and low cost.

Low-cost sensor-based damage localization for large-area monitoring of FRP composites

In recent years, there has been growing interest in self-sensing structural materials across research and industry sectors. Detecting and locating structural damage typically requires numerous sensors wired to a data acquisition (DAQ) circuit, rendering implementation impractical in real structures. This paper proposes an innovative, cost-effective sensor network for damage detection and localization in fiber-reinforced polymer composites. The innovation encompasses three key elements: (1) utilizing carbon fiber tows within the composite as piezoresistive sensors, eliminating the need for additional foreign sensor devices; (2) introducing a novel sensor layout wherein sensor tow branches with varied resistance values are connected in parallel, reducing the number of connections to the DAQ circuit and cutting manufacturing costs significantly; (3) developing a practical sensor terminal fabrication technique to minimize manufacturing expenses. The proposed design methodology for the branch resistance values is first validated using a demonstration panel. Subsequently, the overall strategy is assessed by conducting impact tests on carbon and glass fiber-reinforced composite specimens. Results validate the sensor’s ability to accurately detect and locate structural damage.

Scalable-produced micro-elastic triboelectric sensing ground for all-weather large-scale applications

Large-scale smart floor will be one potential form for a human–machine interface, but is also facing many difficulties and challenges in the materials science and information engineering. Here, we present a straightforward method for fabricating large-scale micro-elastic triboelectric nanogenerator (ME-TENG) arrays, which can be assembled as triboelectric sensing ground for real-time interface interaction and all-weather monitoring. ME-TENG unit of 6 by 6 arrays offers a maximum power density of 5.84 mW·m−2 under a load resistance of 70 MΩ. It displays a sensitivity of 132.48 mV/kPa in the pressure range of 0 to 180 kPa. The electrical output signals have remained stable in the durability tests over 10 days with various weather conditions. Furthermore, successfully demonstrated a regional comprehensive security monitoring (RCSM) system for enabling all-weather monitoring of the features, position, and movement trajectories of an object. Even more gratifying is that the ME-TENG unit of 30 cm by 30 cm prepared by a simple and fast “all-in-one” method, takes costs of 14.84 dollars/m2 and the production period of 2 h, compared with the most cost-effective piezoelectric sensors, the cost and duration were reduced by 98.67 % and 42.86 %, respectively. This work will greatly contribute to the development and implementation of large-scale sensing and the multidimensional security system.

Aluminum-Based Plasmonic Photodetector for Sensing Applications

Plasmonic sensors have great potential for widespread usage. However, the prohibitive cost of noble metals restrains the wider adoption of these devices. The aim of our study is to develop a cost-effective Al-based alternative to common noble metal-based plasmonic detectors. We considered a structure consisting of an n-type doped Si wafer with a shallow p-n junction and an overlying Al grating with a trapezoidal groove profile. The RCWA (rigorous coupled-wave analysis) method was used to numerically calculate the distribution of absorbed light energy in the plasmonic detector layers and to optimize the grating parameters. Based on the simulation results, experimental samples of plasmonic photodetectors with optimal grating parameters (period—633 nm, relief depth—50 nm, groove filling factor—0.36, and thickness of the intermediate Al layer—14 nm) were manufactured, and their properties were studied. For these samples, we obtained a polarization sensitivity value of Ip/Is = 8, an FWHM of the resonance in the photocurrent spectrum ranging from 50 to 100 nm, a sensitivity at the resonance maximum of Iph = 0.04–0.06 A/W, and an angular half-width of photocurrent resonance of Δθ = 5°, which are comparable to noble metal-based analogs. Our results may be used for creating cost-effective high-sensitivity plasmonic sensors.

Cost-Effective Temperature Sensor for Monitoring the Setting Time of Concrete

Concrete and Portland cement-based products are the most widely used materials in the construction industry. According to the Global Cement and Concrete Association (GCCA), 14 billion cubic meters of concrete are consumed worldwide every year. Knowledge of their properties is essential to ensure the quality of concrete products and structures. Knowing the evolution of certain parameters related to their durability makes it possible to prevent situations that affect compliance with quality requirements. Thanks to advances in IoT (Internet of Things) technologies, it is possible to know the evolution of these parameters in real time. The following work pursues the development and application of a prototype to monitor the setting time of concrete. This equipment provides real-time measurements, taking advantage of the Internet of Things (IoT) technology, allowing effective monitoring of the thermal behavior of concrete during its setting process. By measuring the temperature of the process and evaluating the resistance acquired during the setting time, we can correlate these two parameters, thus ensuring their correct evolution and allowing quick action to avoid future problems. For the development of this work, temperature measurements were made during the setting of 12 concrete specimens corresponding to four different mixtures (two types of cement with and without additives), assessed at three setting ages (28, 90, and 180 days). Through detailed experimental tests, the sensor was accurately and reliably validated, showing its ability to detect temperature changes, indicating the initial and final setting time. In addition, it was observed that the integration of the DS18B20 sensor does not compromise the structural properties of the concrete. The prototype’s cost-effectiveness, efficiency, and easy installation make it a valuable tool for construction professionals, offering an innovative solution to ensure the quality and durability of the concrete. This breakthrough could represent a significant step towards the digitalization and improvement of construction processes, with direct implications for the efficiency and sustainability of modern infrastructures.

Flexible dual-mode ammonia-impedance sensing patch (FDP) based on Ti3C2Tx MXene/graphene material and symmetric structure

The pursuit of simple, cost-effective, and efficient flexible ammonia sensors is an area of keen interest. This study aimed to create a highly sensitive, affordable flexible sensing patch (FDP) for immediate ammonia and impedance monitoring. Using a laser direct writing technique, symmetrical graphene patterns were crafted onto commercial kapton tape. These patterns were folded in half, allowing the lower layer to serve as an impedance sensor and the upper layer as an electrode for ammonia detection. The modified Ti3C2Tx MXene solution was introduced as the sensitive layer in response to ammonia gas, and we also explored how varying thicknesses of this sensitive layer affected the response results. Notably, the results highlighted that FDPs with two thicknesses (50 μL solution) offered optimal ammonia detection. With a detection limit of 150 ppb, impressive sensitivity (18.08%/ppm), exceptional linearity (0.996), and stability within the 0.5 ppm–20 ppm range, the overall performance of the FDP surpassed that of most reported ammonia sensor devices based on graphene materials. FDP was able to better characterize the shelf life of lamb at a cost of no more than $1.5. This study provides solutions for rapid ammonia detection and food quality assessment.

Cost-Effective Optical Wireless Sensor Networks: Enhancing Detection of Sub-Pixel Transmitters in Camera-Based Communications.

In the domain of the Internet of Things (IoT), Optical Camera Communication (OCC) has garnered significant attention. This wireless technology employs solid-state lamps as transmitters and image sensors as receivers, offering a promising avenue for reducing energy costs and simplifying electronics. Moreover, image sensors are prevalent in various applications today, enabling dual functionality: recording and communication. However, a challenge arises when optical transmitters are not in close proximity to the camera, leading to sub-pixel projections on the image sensor and introducing strong channel dependence. Previous approaches, such as modifying camera optics or adjusting image sensor parameters, not only limited the camera's utility for purposes beyond communication but also made it challenging to accommodate multiple transmitters. In this paper, a novel sub-pixel optical transmitter discovery algorithm that overcomes these limitations is presented. This algorithm enables the use of OCC in scenarios with static transmitters and receivers without the need for camera modifications. This allows increasing the number of transmitters in a given scenario and alleviates the proximity and size limitations of the transmitters. Implemented in Python with multiprocessing programming schemes for efficiency, the algorithm achieved a 100% detection rate in nighttime scenarios, while there was a 89% detection rate indoors and a 72% rate outdoors during daylight. Detection rates were strongly influenced by varying transmitter types and lighting conditions. False positives remained minimal, and processing times were consistently under 1 s. With these results, the algorithm is considered suitable for export as a web service or as an intermediary component for data conversion into other network technologies.

Versatile Metal-Organic Framework Incorporating Ag2S for Constructing a Photoelectrochemical Immunosensor for Two Breast Cancer Markers.

Breast cancer poses the significance of early diagnosis and treatment. Here, we developed an innovative photoelectrochemical (PEC) immunosensor characterized by high-level dual photocurrent signals and exceptional sensitivity. The PEC sensor, denoted as MIL&Ag2S, was constructed by incorporating Ag2S into a metal-organic framework of MIL-101(Cr). This composite not only enhanced electron-hole separation and conductivity but also yielded robust and stable dual photocurrent signals. Through the implementation of signal switching, we achieved the combined detection of cancer antigen 15-3 (CA15-3) and carcinoembryonic antigen (CEA) with outstanding stability, reproducibility, and specificity. The results revealed a linear range for CEA detection spanning 0.01-32 ng/mL, with a remarkably low detection limit of 0.0023 ng/mL. Similarly, for CA15-3 detection, the linear range extended from 0.1 to 320 U/mL, with a low detection limit of 0.014 U/mL. The proposed strategy introduces new avenues for the development of highly efficient, cost-effective, and user-friendly PEC sensors. Furthermore, it holds promising prospects for early clinical diagnosis, contributing to potential breakthroughs in medical detection and ultimately improving patient outcomes.