Detection Zone Research Articles

YOLO-APDM: Improved YOLOv8 for Road Target Detection in Infrared Images

A new algorithm called YOLO-APDM is proposed to address low quality and multi-scale target detection issues in infrared road scenes. The method reconstructs the neck section of the algorithm using the multi-scale attentional feature fusion idea. Based on this reconstruction, the P2 detection layer is established, which optimizes network structure, enhances multi-scale feature fusion performance, and expands the detection network’s capacity for multi-scale complicated targets. Replacing YOLOv8’s C2f module with C2f-DCNv3 increases the network’s ability to focus on the target region while lowering the amount of model parameters. The MSCA mechanism is added after the backbone’s SPPF module to improve the model’s detection performance by directing the network’s detection resources to the major road target detection zone. Experimental results show that on the FLIR_ADAS_v2 dataset retaining eight main categories, using YOLO-APDM compared to YOLOv8n, mAP@0.5 and mAP@0.5:0.95 increased by 6.6% and 5.0%, respectively. On the M3FD dataset, mAP@0.5 and mAP@0.5 increased by 8.1% and 5.9%, respectively. The number of model parameters and model size were reduced by 8.6% and 4.8%, respectively. The design requirements of the high-precision detection of infrared road targets were achieved while considering the requirements of model complexity control.

Distance-based analytical device integrated with carbon nanomaterials for sarcosine quantification in human samples.

Astraightforward distance-based paper analytical device (dPAD) was developedfor monitoring sarcosine levels in human samples for the rapid diagnosis and prognosis of prostate cancer and related symptoms. This assay eliminates the need for the expensive horseradish peroxidase (HRP) enzyme by utilizing carbon nanodots (CDs) as a peroxidase-like nanozyme. The proposed dPAD sensor consists of a sample zone pre-deposited with sarcosine oxidase (SOx) and CDs, and a detection zone containing 3,3',5,5'-tetramethylbenzidine (TMB). When a solution containing sarcosine is added to the sample zone, hydroxyl radicals (•OH) are produced through SOx oxidation and subsequent peroxidase catalysis by the CDs. The formed •OH radicals immediately flow to the detection zone via capillary force, where they oxidize TMB, resulting in a visible colour change from colourless to blue. Sarcosine quantification is effortlessly accomplished by measuring the distance of the blue colour in the detection zone. The developed dPAD offers a linear working range between 12.5 and 35.0nmol L-1 (R2 = 0.9959) and a detection limit (LOD) of 10.0nmol L-1. This covers the clinical range for urinary sarcosine determination, thereby suggesting no additional sample preparation or dilution is needed. The sensor shows high precision with the highest relative standard deviation (RSD) of 4.58% and demonstrates excellent selectivity with no observed interferences. Furthermore, recovery studies in human control urine samples ranged from 98.67 to 101.50%, with the highest RSD of 2.03%. Correspondingly, our dPAD method showed a great match with the performance of a commercial ELISA method for detecting sarcosine in human control serum. The sensor is more cost-effective, user-friendly, and accessible than previous methods. Overall, the proposed method represents a promising analytical tool for sarcosine quantification. The concept is also applicable for broader analytical applications in detecting other biomolecules.

Continuous flow microfluidic system with magnetic nanoparticles for the spectrophotometric quantification of urea in urine and plasma samples.

Urea, synthesized exclusively in the liver, is primarily transported through the bloodstream to the kidneys, where it is excreted in urine, accounting for 80-90% of nitrogen excretion in humans. Elevated blood urea levels, indicative of kidney dysfunction, make it a crucial biomarker for assessing renal function. Previous studies on urea detection using microdevices have largely focused on conductometric methods. In this study, we demonstrated the application of a continuous flow miniaturized system for rapid spectrophotometric urea quantification using polydimethylsiloxane (PDMS) microdevices. The microdevice featured two distinct zones: an enzymatic reaction zone, where urease-conjugated magnetic nanoparticles were immobilized, and a detection zone, where reagents were incorporated to produce a colored reaction product via a modified Berthelot reaction. Integrating magnetic nanoparticles as a solid support for the enzyme enabled the reuse of PDMS microdevices without compromising the analytical signal. Spectrophotometric detection was performed in an additional microdevice acting as a microflow cell coupled with optical fibers. A calibration curve was constructed using urea standards diluted in phosphate buffer solution (PBS), yielding a linear range of 0.12-3.00 mg dL-1. The method demonstrated detection and quantification limits of 0.04 mg dL-1 and 0.12 mg dL-1, respectively. Precision and accuracy assessments yielded a repeatability of 0.90% and intermediate precision of 4.52%, with recovery rates near 100%. The method was applied to plasma and urine samples, showing urea concentrations within normal physiological ranges and an analysis throughput of 36 measurements per hour.

Non-enzymatic Determination of Glucose in Artificial Urine Using 3D-µPADs through Silver Nanoparticles Formation

Patients with diabetes often experience blood glucose fluctuations, making monitoring crucial. Traditional blood sampling methods pose risks of infection and pain. An alternative non-invasive approach using urine tests has been explored. Recent studies highlight microfluidic paper-based analytical devices (µPADs) as convenient, simple, and easily fabricated tools for non-invasive glucose measurement. This study aims to develop a concept of measuring glucose in artificial urine using 3D-µPADs in a non-enzymatic manner by utilizing glucose as a reducing agent for silver nanoparticle (AgNPs) formation. Embedding three-dimensional connectors in µPADs links the sample and detection zones to limit reagent mixing and improve glucose detection resolution. The optimal conditions were NaOH 10 M, starch 1%, and AgNO3 30 mM, with sample and detection zone volumes of 10 and 9 µL, respectively. The fifth reaction sequence involved AgNO3 in the detection zone and a solution of glucose, NaOH, and starch in the sample zone at 1:1:1 volume ratio. The reagent drying time was 15 min, with immobilization once and reaction time of 9 min. The method showed excellent linearity (R2 = 0.9905), precision (%RSD = 4.27%), accuracy (77.32–92.58%), and limit of detection (11.11 mg/dL).

Convolutional neural network approach for fault detection and characterization in medium voltage distribution networks

Power outages significantly impact the power industry by disrupting social welfare and economic stability. Still, existing methods for fault detection face challenges due to load and network topology, conditions, and installed equipment. However, recent advances in artificial intelligence (AI) are enabling researchers to create alternative approaches for fault detection and location strategies. Therefore, this paper introduces a novel method for detecting, classifying, and locating faults in power systems through voltage waveform analysis using a convolutional neural network (CNN) integrated with the Piecewise Function Put Together (PFPT) algorithm for fault detection and fault zone localization in a power distribution network. Utilizing Park's transformation, noise reduction PFPT sine fitting, and CNNs, the proposed method distinguishes between 'healthy' and 'faulty' conditions. Simulation results reveal that while the voltage Park's vector time behavior of a healthy system remains stable, it exhibits circular or mixed patterns under faulty conditions. These patterns enable the identification of four types of short circuit faults—single-line-to-ground (LG), line-to-line (LL), line-to-line-to-ground (LLG), and three-line (3L) faults—by analyzing 3D voltage Park's waveforms at network buses. The study validates fault type identification through the observation of rotating Park vectors from sine fitting of time-based voltage waveforms. By converting 3D voltage waveforms into high-resolution images, the method utilizes a CNN for fault recognition, achieving an accuracy of 93.1%. This innovative approach underscores the robustness and precision of combining traditional electrical engineering techniques with modern AI.

Based on coverage evaluation design method study of factor low-orbit infrared remote sensing constellation configuration

Abstract To satisfy the infrared detection coverage demands of Low Earth Orbit (LEO) satellites in global missile surveillance, we have devised a comprehensive evaluation system for global missile infrared remote sensing coverage performance. By referencing Walker’s extensive and uninterrupted global coverage constellation layout, we conducted a thorough analysis of various constellation configuration parameters. This analysis aimed to assess the coverage efficiency of differently configured LEO networking satellites. Following a detailed comparative analysis, we settled on an LEO infrared remote sensing constellation configuration featuring an orbit height of 1150 km and an orbit inclination of 60°. After an in-depth examination of the capabilities of the infrared remote sensing constellation and the characteristics of the detection zone, we constructed a Blind Spot Compensation Constellation. This was achieved by integrating four additional LEO satellites with a 0° orbit inclination to bolster coverage in low-latitude regions. Using our comprehensive coverage evaluation system, we rigorously evaluated the ground coverage capabilities of multiple constellations. Notably, the Blind Spot Compensation Constellation achieved impressive coverage, reaching 100%, and excelled particularly in the latitude belt of 20° or less. Although the Original Constellation performed better in the 40° latitude belt compared to the Blind Spot Compensation Constellation, it showed some deficiencies in coverage at a 50 km altitude. Specifically, it averaged 18.417 gaps per day, with a coverage rate slightly below 100%. However, it achieved full coverage at other altitudes. To validate our findings, we conducted scenario simulations. For missile targets, the Original Constellation offered a maximum coverage multiplicity of 6, with a total access duration of 4539.1 seconds. On the other hand, the Blind Spot Compensation Constellation boasted a maximum coverage multiplicity of 8, with a total access duration of 5465.9 seconds. Importantly, both constellations fully covered the missile’s entire 1056-second lifecycle, showcasing commendable performance. In conclusion, the LEO infrared remote sensing constellation and its complementary counterpart, developed and rigorously tested in this study, not only meet the demands of continuous global detection but also cater to the priority detection needs of diverse geographical regions.

Vehicle Detection and Counting for Traffic Congestion Estimation Using YOLOv5 and DeepSORT in Smart Traffic Light Application

The escalating number of vehicles on the roads has exacerbated trafficcongestion, presenting a significant and inevitable challenge for roadusers.Trafficcongestionnotonlyincreasesthetraveltimeofroadusers;it also causes air pollution since more vehicles are stuck on theroad. To address this issue, deep learning algorithms, including YOLOv5and DeepSORT, have been leveraged to enable vehicle detection and estimate the number of vehicles through image processing. This study focuses on training a custom YOLOv5 dataset of vehicles and compares its performance with a pretrained YOLOv5 dataset in terms of feasibility for detecting and estimating the number of vehicles. In the proposed approach, YOLOv5 is employed to detect vehicles in video streams, DeepSORT is used for vehicle tracking and counting as they pass through the detection zone, and the number of vehicles in each lane is estimated with the aid of Supervision. The custom dataset's validation demonstrates promising results, with the precision curve indicating convergence for all classes at 97.4% precision and an 87% accuracy in predicting vehicle classes. Additionally, the precision-recall curve assesses the ability to detect individual vehicle categories, such as bus, car, motorcycle, and truck, with accuracies of 68.2%, 95.2%, 79.9%, and 70.1%, respectively. Furthermore, the overall accuracy for detecting all vehicle classes combined is 78.3%. The F1 curve indicates that the system achieves a confidence level of 30.9% F1 score, ensuring 78% confidence in accurately predicting all classes. Both the pretrained and custom datasets exhibit similar accuracy in counting the total number of vehicles passing through the detection zone as well as estimating the number of vehicles in each lane. However, it is evident that the custom dataset's performance can be further improved by incorporating more extensive and diverse datasets during the training process to enhance the accuracy of vehicle detection and estimation. In conclusion, the integration of deep learning algorithms, specificallyYOLOv5 and DeepSORT, offers a promising solution for addressing traffic congestion byefficiently detecting and estimating vehicle numbers, which allow traffic systems to identify which lane is congested and prioritise ensuring the congested lane has a smooth traffic flow. Continued research with larger datasets can lead to further advancements and refined results in the field of traffic management and optimization.

Lab-on-a-chip paper-based electrochemically assisted solid-phase microextraction and ion selective sensor for determination of naproxen in biological fluid samples

BackgroundThe integration of paper-based analytical devices with lab-on-a-chip technology has opened up new possibilities for miniaturized, rapid, low cost and portable diagnostic testing in resource limited settings. This work introduces an integrated lab-on-a-chip platform coupling paper-based analytical devices (LOC-PADs) for dual usage: electrochemically controlled solid-phase microextraction (EC-SPME) and determination of naproxen (NAP) by potentiometric ion-selective electrode (ISE) in biological samples. ResultsConductive papers were successfully fabricated by a chemical procedure. Thereupon, a conducting molecularly imprinted polymer (CMIP) film based on PPy and an anionic model drug (NAP) as a template were applied to the conductive paper substrate via electrochemical methods. A microfluidic chip device was employed to assess the analytical performance of the fabricated paper-based CMIP. The designed chip is divided into two parts with a microfluidic channel between them. The first chamber is an extraction zone for EC-SPME of NAP by using the CMIP paper as a selective sorbent. The second chamber is the detection zone and consists of a paper-based NAP ion selective sensor for potentiometric detection of drug. The analytical process was investigated and optimized for each chamber. Remarkable selectivity towards NAP was achieved in the concentration range from 4.0 × 10−7 to 1.0 × 10−2 mol L−1 with determination coefficient (R2 = 0.99) and the limit of detection (LOD) was 2.0 × 10−7 mol L−1. Anionic Nernstian compliance was gained −58.5 ± 0.5 mV decade−1, and response time in the detection step was 7s for ISE. Satisfactory spiking recovery values within the acceptable range of 90–110 % with RSDs ≤7 % were achieved for the analysis of real samples with fully portable LOC device. SignificanceThe LOC-PADs were favorably used for selective extraction and determination of NAP in serum and saliva samples, respectively. EC-SPME caused sample clean-up, reduced or eliminated different interferences and enhanced selective response of ISE as detection zone of device. Integration of EC-SPME and ISE in a single chip enabled easy and fast determination of trace amounts of NAP in limited real sample volumes, and fully portable advantages.

Unveiling the potential of the capillary-driven microfluidic paper-based device integrated with smartphone-based for simultaneously colorimetric salivary ethanol and △9-tetrahydrocannabinol analysis

Monitoring various biomarkers in saliva samples emerges as a dynamic and non-invasive method. However, the high viscosity of saliva presents a distinct challenge when integrating paper-based platforms for on-site analysis. In addressing this challenge, we introduced the capillary-driven microfluidic paper-based analytical devices (μCD-PAD) designed for user-friendly and simultaneous detection of ethanol and tetrahydrocannabinol (THC) in saliva without a sample preparation step. Employing a colorimetric approach, we quantified both analytes. Synthetic salivas of varying viscosity flowed seamlessly to the detection zone without needing a sample preparation step, and no impact on colorimetric detection due to viscosity was observed (RSD <5 %). Within 10 min after the solution reached the detection zone, the device produced a homogeneous color signal, easily analyzed by a smartphone camera. To extend the application for determination to cover a legal limit concentration of ethanol and concentration of salivary THC even 24 h after marijuana consumption, the detection time of 30 min was optimized. Moreover, a saliva sample containing both analytes was used to demonstrate the capability of the developed device to detect ethanol and THC simultaneously. No cross-talk between ethanol and THC occurred and showed recovery in the 98–102 % for ethanol and 95–105 % for THC with acceptable accuracy. This developed device exhibits excellent potential for forensic applications, providing a user-friendly, cost-effective, and real-time screening tool for detecting ethanol and THC in saliva.

Microfluidic paper-based analytical device for measurement of pH using as sensor red cabbage anthocyanins and gum arabic

The objective of this study was to develop and validate a novel microfluidic paper-based analytical device (μPADpH) for determining the pH levels in foods. Anthocyanins from red cabbage aqueous extract (RCAE) were used as its analytical sensor. Whatman No. 1 filter paper was the most suitable for the device due to its porosity and fiber organization, which allows for maximum color intensity and minimal color heterogeneity of the RCAE in the detection zone of the μPADpH. To ensure the color stability of the RCAE for commercial use of the μPADpH, gum arabic was added. The geometric design of the μPADpH, including the channel length and separation zone diameter, was systematically optimized using colored food. The validation showed that the μPADpH did not differ from the pH meter when analyzing natural foods. However, certain additives in processed foods were found to increase the pH values.

Three-dimensional microfluidic paper-based analytical device fabricated by cutting technique for highly sensitive and specific colorimetric detection of triglycerides in whole blood utilizing a mixed indicator system

In this study, we developed a three-dimensional microfluidic paper-based analytical device (3D-µPAD) for quantifying triglycerides (TGs) in whole blood samples. This method provides an alternative to conventional TGs detection and offers several advantages. The 3D-µPAD was fabricated using an in-house computer-controlled X-Y knife plotter, eliminating the need for hydrophobic reagents. This approach results in a more rapid, simple, highly reproducible, and cost-effective mass production process. To separate plasma from a drop of whole blood, the LF1 membrane was incorporated as a sample zone on the 3D-µPAD. The hydrolysis of TGs was catalyzed by lipase, producing H+ ions detected by a phenol red indicator read against cyan ink to reduce background interference. A spectrophotometer (i1 Pro 3 mini) measured the green color intensity in the 3D-µPAD’s detection zone, which was proportional to the TGs concentration and quantified within 10 min. Results were consistent with clinical methods, with a detection limit of 0.182 mmol L−1 in the 0.1–6 mmol L 0207B1 TGs concentration range. Using mixed indicators successfully reduced background interference and enhanced sensitivity for TGs determination. The 3D-µPAD demonstrated a storage stability of approximately 30 days. Our 3D-µPAD prototype offers a rapid, cost-effective, and user-friendly approach for pre-clinical TGs diagnostics, expanding the potential for point of care whole blood sample analysis.

Flexible/Regenerative Nanosensor with Automatic Sweat Collection for Cytokine Storm Biomarker Detection.

The real-time monitoring of low-concentration cytokines such as TNF-α in sweat can aid clinical physicians in assessing the severity of inflammation. The challenges associated with the collection and the presence of impurities can significantly impede the detection of proteins in sweat. This issue is addressed by incorporating a nanosphere array designed for automatic sweat transportation, coupled with a reusable sensor that employs a Nafion/aptamer-modified MoS2 field-effect transistor. The nanosphere array with stepwise wettability enables automatic collection of sweat and blocks impurities from contaminating the detection zone. This device enables direct detection of TNF-α proteins in undiluted sweat, within a detection range of 10 fM to 1 nM. The use of an ultrathin, ultraflexible substrate ensures stable electrical performance, even after up to 30 extreme deformations. The findings indicate that in clinical scenarios, this device could potentially provide real-time evaluation and management of patients' immune status via sweat testing.

Urine Glucose Detection Via Gold Nanoparticle Formation Using 3D-Connector Microfluidic Paper Based Analytical Devices

A metabolic disorders that have experienced a significant increase in the world are diabetes mellitus. Diabetes is caused by two main factors: the first is damage to pancreatic beta cells, which prevents insulin from being produced, and the second is impaired insulin secretion and function. Chronic diabetes, if not treated properly, can lead to acute complications including eye, kidney, lung, nerve, and even death. Diabetes can be diagnosed through blood and urine. In general, glucose detection is carried out using invasive methods that use blood samples, which can cause pain and discomfort for users. Current research is developing non-invasive glucose detection using urine samples. This research aims to develop non-invasive glucose detection technology using 3D-connector μPADs (Microfluidic Paper Based Analytical Devices) which have the advantages of being safe, easy, and simple. The three-dimensional connector on the device functions as a connector to facilitate the coordination of fluid flow in the sample zone and detection zone. The glucose detection method uses gold (III) chloride as a gold nanoparticle (AuNPs) precursor, an aqueous extract of Acalypha indica Linn as a stabilizing agent, sodium hydroxide (NaOH) as a catalyst, and glucose in artificial urine as a sample. Method validation results using imageJ software indicated linearity with a coefficient of determination value (R2) of 0.9714, precision with a %RSD value (Relative Standard Deviation) of 2.69, and an accuracy level ranging from 92.22-99.23%.

3D Simulation-Driven Design of a Microfluidic Immunosensor for Real-Time Monitoring of Sweat Biomarkers.

This study presents the design and comprehensive 3D multiphysics simulation of a novel microfluidic immunosensor for non-invasive, real-time detection of pro-inflammatory biomarkers in human sweat. The patch-like device integrates magnetofluidic manipulation of antibody-functionalized magnetic nanoparticles (MNPs) with direct-field capacitive sensing (DF-CS). This unique combination enhances sensitivity, reduces parasitic capacitance, and enables a more compact design compared to traditional fringing-field approaches. A comprehensive 3D multiphysics simulation of the device, performed using COMSOL Multiphysics, demonstrates its operating principle by analyzing the sensor's response to changes in the dielectric properties of the medium due to the presence of magnetic nanoparticles. The simulation reveals a sensitivity of 42.48% at 85% MNP occupancy within the detection zone, highlighting the sensor's ability to detect variations in MNP concentration, and thus indirectly infer biomarker levels, with high precision. This innovative integration of magnetofluidic manipulation and DF-CS offers a promising new paradigm for continuous, non-invasive health monitoring, with potential applications in point-of-care diagnostics, personalized medicine, and preventive healthcare.

Deriving Verified Vehicle Trajectories from LiDAR Sensor Data to Evaluate Traffic Signal Performance

Advances and cost reductions in Light Detection and Ranging (LiDAR) sensor technology have allowed for their implementation in detecting vehicles, cyclists, and pedestrians at signalized intersections. Most LiDAR use cases have focused on safety analyses using its high-fidelity tracking capabilities. This study presents a methodology to transform LiDAR data into localized, verified, and linear-referenced trajectories to derive Purdue Probe Diagrams (PPDs). The following four performance measures are then derived from the PPDs: arrivals on green (AOG), split failures (SF), downstream blockage (DSB), and control delay level of service (LOS). Noise is filtered for each detected vehicle by iteratively projecting each sample’s future location and keeping the subsequent sample that is close enough to the estimated destination. Then, a far side is defined for the analyzed intersection’s movement to linear reference sampled trajectories and to remove those that do not cross through that point. The technique is demonstrated by using over one hour of LiDAR data at an intersection in Utah to derive PPDs. Signal performance is then estimated from these PPDs. The results are compared to those obtained from comparable PPDs derived from connected vehicle (CV) trajectory data. The generated PPDs from both data sources are similar, with relatively modest differences of 1% AOG and a 1.39 s/veh control delay. Practitioners can use the presented methodology to estimate trajectory-based traffic signal performance measures from their deployed LiDAR sensors. The paper concludes by recommending that unfiltered LiDAR data are used for deriving PPDs and extending the detection zones to cover the largest observed queues to improve performance estimation reliability.

Acoustofluidics-enhanced biosensing with simultaneously high sensitivity and speed

Simultaneously achieving high sensitivity and detection speed with traditional solid-state biosensors is usually limited since the target molecules must passively diffuse to the sensor surface before they can be detected. Microfluidic techniques have been applied to shorten the diffusion time by continuously moving molecules through the biosensing regions. However, the binding efficiencies of the biomolecules are still limited by the inherent laminar flow inside microscale channels. In this study, focused traveling surface acoustic waves were directed into an acoustic microfluidic chip, which could continuously enrich the target molecules into a constriction zone for immediate detection of the immune reactions, thus significantly improving the detection sensitivity and speed. To demonstrate the enhancement of biosensing, we first developed an acoustic microfluidic chip integrated with a focused interdigital transducer; this transducer had the ability to capture more than 91% of passed microbeads. Subsequently, polystyrene microbeads were pre-captured with human IgG molecules at different concentrations and loaded for detection on the chip. As representative results, ~0.63, 2.62, 11.78, and 19.75 seconds were needed to accumulate significant numbers of microbeads pre-captured with human IgG molecules at concentrations of 100, 10, 1, and 0.1 ng/mL (~0.7 pM), respectively; this process was faster than the other methods at the hour level and more sensitive than the other methods at the nanomolar level. Our results indicated that the proposed method could significantly improve both the sensitivity and speed, revealing the importance of selective enrichment strategies for rapid biosensing of rare molecules.

Computing‐efficient video analytics for nighttime traffic sensing

AbstractThe training workflow of neural networks can be quite complex, potentially time‐consuming, and require specific hardware to accomplish operation needs. This study presents a novel analytical video‐based approach for vehicle tracking and vehicle volume estimation at nighttime using a monocular traffic surveillance camera installed over the road. To build this approach, we employ computer vision‐based algorithms to detect vehicle objects, perform vehicle tracking, and vehicle counting in a predefined detection zone. To address low‐illumination conditions, we adapt and employ image noise reduction techniques, image binary conversion, image projective transformation, and a set of heuristic reasoning rules to extract the headlights of each vehicle, pair them belonging to the same vehicle, and track moving candidate vehicle objects continuously across a sequence of video frames. The robustness of the proposed method was tested in various scenarios and environmental conditions using a publicly available vehicle dataset as well as own labeled video data.

Casein/ferric chloride/polyvinyl alcohol composite for specific in-syringe colorimetric detection of formaldehyde in Hevea brasiliensis latex.

A new, simple, and selective colorimetric method of determining formaldehyde in Hevea brasiliensis latex was developed by using a casein/ferric chloride/polyvinyl alcohol hydrogel composite (casein/FeCl3/PVA) in a modified Leach test. Under heating, formaldehyde reacted with 8% casein in the presence of 0.1% FeCl3 and 4.3% HCl (v/v) entrapped in a 30% PVA hydrogel packed in a syringe. A purple-colored product was formed with a maximum absorbance of 525nm. The color change was evaluated at the color detection zone indicated on thethe syringe. The %magenta values were easily evaluated by using a mobile phone application and employed to determine formaldehyde content. The casein/FeCl3/PVA composite gave a readable response in a formaldehyde detection range from 0.04 to 0.80% with a linear response between %magenta and formaldehyde concentration (R2 = 0.9955). The detection limit was 0.032%, and precisions were in the range 0.67-4.94%. The casein/FeCl3/PVA composite was applied to the analysis of ammonia-preserved latex samples, and recoveries of formaldehyde from samples spiked at 0.1, 0.3, and 0.5% ranged from 81.55 to 99.51% (RSDs ≤ 5.41%). The recoveries and precision of the proposed method were comparable with those of high-performance liquid chromatography (HPLC). The developed method was also selective, showing no interference from other latex preservatives, i.e., phenol, ammonia, or tetramethylthiuram disulfide.

Multifunctional Metal-Organic Frameworks Driven Three-Dimensional Folded Paper-Based Microfluidic Analysis Device for Chlorpyrifos Detection.

Chlorpyrifos (CPF) residues in food pose a serious threat to ecosystems and human health. Herein, we propose a three-dimensional folded paper-based microfluidic analysis device (3D-μPAD) based on multifunctional metal-organic frameworks, which can achieve rapid quantitative detection of CPF by fluorescence-colorimetric dual-mode readout. Upconversion nanomaterials were first coupled with a bimetal organic framework possessing peroxidase activity to create a fluorescence-quenched nanoprobe. After that, the 3D-μPAD was finished by loading the nanoprobe onto the paper-based detection zone and spraying it with a color-developing solution. With CPF present, the fluorescence intensity of the detection zone gradually recovers, the color changes from colorless to blue. This showed a good linear relationship with the concentration of CPF, and the limits of detection were 0.028 (fluorescence) and 0.043 (colorimetric) ng/mL, respectively. Moreover, the 3D-μPAD was well applied in detecting real samples with no significant difference compared with the high-performance liquid chromatography method. We believe it has huge potential for application in the on-site detection of food hazardous substance residues.

Hybrid photo paper-based microfluidic device for colorimetric detection of iodine in salt

Microfluidic paper-based analytical devices (µPADs) have gained widespread use in various analytical applications because they are low-cost and suitable for onsite testing. The development of µPADs, including fabrication methods, new materials, and enhancement functionality is crucial to advance their practical application in analytical chemistry. In this study, we introduce a new hybrid paper-based analytical device, the hybrid photo paper-based microfluidic device (hPPMD), which combines a photo paper-based microfluidic device (PPMD) with a µPAD. We conducted a systematic study that detailed hPPMD’s characteristics, including surface properties and fluidic transportation. The hPPMD showed two fluidic transportation behaviors: continuous flow and discontinuous flow at the device junction, depending on the orientation of the device combination. Our hPPMD could increase fluidic flow approximately four times the speed when six layers of guided channels were added. The customized hPPMD was used for colorimetric detection of iodine in table salt, and then the result was quantitatively analyzed using a computer and smartphone with color analysis software. The detection zones of the hPPMD showed a flawless circular color signal. Under optimum conditions, the hPPMD was sensitive enough to detect iodine in salt solutions at various concentrations ranging from 1 to 100 ppm. The developed hPPMD should be a simple and low-cost analytical device for onsite qualitative analysis of the iodine and other chemical contaminants in food and the environment.