Rapid Detection Research Articles

Multi-walled carbon nanotube wrapped rare earth tungstate nanosheet composites for selective and rapid detection of sulfadiazine

Multi-walled carbon nanotube wrapped rare earth tungstate nanosheet composites are developed for the ultrasensitive and selective electrochemical detection of sulfa drug, sulfadiazine (SAN) as they exhibit high levels of toxicity towards public health and ecosystem. Here, we synthesized Gadolinium tungstate decorated with multi-walled carbon nanotubes (Gd2WO6/MWCNT) though hydrothermal processing and characterized by XRD, SEM, EDX, TEM, and EIS measurement to investigate the phase purity, morphology, and interfacial property. Gd2WO6 is a double perovskite that has high oxygen surface exchange kinetics and the MWCNT possesses high catalytic capability and a larger surface area contributes to the efficient sensing ability. The Gd2WO6/MWCNT possessed a great electrical conductivity owing to the synergism between the Gd2WO6 and MWCNT. The synergistic effect increases the surface area, ionic and electrical conductivity which increases the current response of SAN. Under optimum conditions, the Gd2WO6/MWCNT/GCE displays a broad linear of 0.01 – 671 µM and a LOD level of 2 nM (S/N ratio: 3) with a sensitivity of 0.371 µA µM cm−1. The analytical applicability of the proposed electrode modifier leads to a promising sensor for SAN detection in human urine and water samples with appreciable results.

Ultrasensitive and Rapid Circulating Tumor DNA Liquid Biopsy Using Surface-Confined Gene Amplification on Dispersible Magnetic Nano-Electrodes.

Circulating tumor DNA (ctDNA) detection has been acknowledged as a promising liquid biopsy approach for cancer diagnosis, with various ctDNA assays used for early detection and treatment monitoring. Dispersible magnetic nanoparticle-based electrochemical detection methods have been proposed as promising candidates for ctDNA detection based on the detection performance and features of the platform material. This study proposes a nanoparticle surface-localized genetic amplification approach by integrating Fe3O4-Au core-shell nanoparticles into polymerase chain reactions (PCR). These highly dispersible and magnetically responsive superparamagnetic nanoparticles act as nano-electrodes that amplify and accumulate target ctDNA in situ on the nanoparticle surface upon PCR amplification. These nanoparticles are subsequently captured and subjected to repetitive electrochemical measurements to induce reconfiguration-mediated signal amplification for ultrasensitive (∼3 aM) and rapid (∼7 min) metastatic breast cancer ctDNA detection in vitro. The detection platform can also detect metastatic biomarkers from in vivo samples, highlighting the potential for clinical applications and further expansion to rapid and ultrasensitive multiplex detection of various cancers.

Spectroscopy Detection and Imaging System Based on Line Array Single Photon Detectors

Abstract. Single-photon detectors, with their exceptional sensitivity, provide a reliable means for single-photon-level detection, demonstrating significant advantages in detecting weak signals in complex environments compared to traditional detectors. With the continuous advancement in semiconductor manufacturing technology, single-photon detectors based on linear array configurations have emerged and rapidly developed. This study utilizes a linear array single-photon detector in free-running mode combined with a scanning mechanism to design and implement a spectral detection and imaging system. Through the spectral scanning unit, this system successfully achieves precise spectral detection in the 890 nm to 1710 nm range, with a spectral resolution better than 2 nm. Utilizing the imaging scanning unit, the system effectively performs target spectral imaging under single and multiple wavelength conditions at 1064 nm, 1310 nm, and 1520 nm. By optimizing algorithms for data processing, the system can achieve rapid and accurate spectral detection and imaging even under low-light conditions where the average photon count per pixel is less than 3. The results of this study are expected to provide strong technical support for the application of spectral imaging technology in the field of high-speed detection and imaging.

A novel in-house built printed circuit board-ceria based electrochemical device for rapid detection of Epithelial Sodium Channel (ENaC), a hypertension biomarker

The Epithelial Sodium Channel (ENaC) is a protein that plays a role in the cellular intake of salt and is known as one of the biomarkers for hypertension in the human body. Higher concentrations of sodium lead to increased activity of the ENaC protein in sodium absorption. In this study, a self-made immunosensor was developed using a homemade Printed Circuit Board-Screen Printed Carbon Electrode (PCB-SPCE) modified with cerium oxide. Anti-ENaC was immobilized on the surface of PCB-SPCE/ceria at pH 7.4, relying on the interaction between carboxyl groups of anti-ENaC and cerium oxide to detect the ENaC protein. Additionally, a portable in-house potentiostat named “UnpadStat” was developed for integration with the immunosensor. UnpadStat features a 5-in. touch screen display to showcase measurement data. Cyclic voltammetry measurements using the redox system of K3[Fe(CN)6] were applied to bare PCB-SPCE, PCB-SPCE/Ceria, PCB-SPCE/Ceria/Anti-ENaC, and PCB-SPCE/Ceria/Anti-ENaC/ENaC. Measurement results indicated an increase in current after modification with cerium, reaching 73.805 µA compared to unmodified (63.256 µA). After anti-ENaC immobilization, there was a decrease in current to 64.456 µA, and upon binding with the ENaC protein, a further decrease in current occurred, corresponding to the concentration of the ENaC protein. The detection limit obtained in this study was 0.133 ng/mL, and the quantification limit was 0.444 ng/mL for the concentration range of ENaC protein from 0.375 ng/mL to 6.0 ng/mL. The integration of the PCB-based immunosensor and UnpadStat can be utilized to determine the levels of ENaC protein in non-hypertensive urine samples and hypertensive patients.

Rapid detection of endogenous impurities in walnuts using near-infrared hyperspectral imaging technology

Impurities in walnut-based products pose a significant risk to human health, necessitating the development of rapid and efficient methods for detecting endogenous impurities in walnut shell-breaking materials. To address this issue, this study aims to investigate the feasibility of near-infrared hyperspectral imaging (NIR-HSI) in conjunction with conventional chemometric methods and deep learning models. First, biochemical experiments demonstrated differences in the total phenolic and flavonoid contents of different walnut kernels. An improved support vector machine model based on the butterfly optimization algorithm was constructed for classification using an NIR dataset of endogenous impurities in walnuts, achieving an accuracy of 96.12 %. In addition, this study proposed the WT-NIRSNet model, a deep neural network with an additional attention mechanism, and compared it to a traditional chemometric approach. The study demonstrated an accuracy of 99.03 % on the test set using WT-NIRSNet to solve the fast classification problem of endogenous impurities in walnuts. It can achieve desirable classification results using only raw spectral data without complex preprocessing operations and is superior to traditional chemometric models. Therefore, the NIR-HSI technique combined with deep neural networks has a significant potential for detecting endogenous impurities in walnuts.

Research progress on the detection of foodborne pathogens based on aptamer recognition.

Foodborne diseases caused by bacterial contamination are a serious threat to food safety and human health. The classical plate culture method has the problems of long detection cycle, low sensitivity and specificity, and complicated operation, which cannot meet the growing demand for rapid quantitative detection of pathogenic bacteria. The frequent outbreak of foodborne diseases has put forward higher requirements for rapid and simple detection technology of foodborne pathogens. Aptamer is a kind of oligonucleotide fragment that can recognize targets with the advantages of high affinity and good specificity. The target can be range from proteins, small molecules, cells bacteria, and even viruses. Herein, the latest advances in sensitive and rapid detection of foodborne pathogens based on aptamer recognition was reviewed. Special attention has been paid to the obtained sequences of aptamers to various foodborne pathogens, the optimization of sequences, and the mechanism of aptamer recognition. Then, the research progress of biosensors for the detection of pathogenic bacteria based on aptamer recognition were summarized. Some challenges and prospects for the detection of foodborne pathogens based on aptamer recognition were prospected. In summary, with the further deepening of aptamer research and improvement of detection technology, aptamer-based recognition can meet the needs of rapid, sensitive, and accurate detection in practical applications.

A novel signal amplified sandwich aptamer-based visual lateral flow assay based on self-assembly bifunctional nucleic acid for rapid and sensitive detection of CKMB

At present, rapid and sensitive detection of disease biomarkers is still the focus of early clinical diagnosis. As a rapid response to point-of-care testing (POCT), lateral flow assay (LFA) has been given serious attention. Unfortunately, due to the sensitivity limitations, LFA, especially AuNPs-based LFA, was difficult to play an important role in the detection of trace biomarkers. The combination of different signal amplification methods increases the complexity of operation and use, or prolongs the detection time, which sacrifices the advantages of fast and convenient of LFA. Hence, a novel sandwich aptamer-based visual LFA based on bifunctional nucleic acid (bFNA-LFA) was proposed for rapid and sensitive detection of creatine kinase MB (CKMB) in this study. Based on hybridization chain reaction (HCR) and nucleic acid hybridization, a bifunctional nucleic acid with target recognition and signal amplification was prepared, which effectively improved the sensitivity of CKMB detection without increasing the reaction time and depending on the detecting equipment. Under optimal reaction conditions, the limit of detection of 7.92 ng/mL was observed, which was 20 times lower than that of sandwich aptamer-based AuNPs LFA in the previous study. The proposed bFNA-LFA assay expanded a new idea in improving the sensitivity of LFA, and provided a reference for the subsequent application of nucleic acid nanomaterials in the detection of protein biomarkers.

Soybean Saponin Content Detection Based on Spectral and Image Information Combination

Soybean saponin is a natural antioxidant and is anti-inflammatory. Hyperspectral analysis technology was applied to detect soybean saponin content rapidly and nondestructively in this paper. Firstly, spectral preprocessing methods were studied, and standard normal variable (SNV) was used to remove noise information. Secondly, a two-step hybrid variable selection approach based on synergy interval partial least squares (SiPLS) and iteratively retains informative variables (IRIV) was proposed to extract characteristic variables. Then, the ensemble learning model was constructed by back propagation neural network (BPNN), deep forest (DF), partial least squares regression (PLSR), and extreme gradient boosting (EXG). Finally, image information was combined into spectral data to improve model accuracy. The prediction coefficient of determination (R2) of the final model reached 0.9216. It can provide rapid, nondestructive, and accurate detection technology of soybean saponin content. A combination of spectral and image information will provide a new idea for application of hyperspectral.

UAV and Deep Learning: Detection of selected riparian species along the Ganga River

Abstract. Environmental protection and sustainable natural resource management are being recognised worldwide as essential goals to safe guard human health and well-being. Riparian zones, that face the highest decline in freshwater biodiversity, are of prime conservation priority because they are essential for regulating climate, preserving aquatic-terrestrial biodiversity, maintaining ground water recharge and restoring rivers. In today's fast-paced data-driven environment, artificial intelligence (AI) is the precise answer to a wide range of problems including biodiversity conservation and wildlife management. Leveraging advancements like Uncrewed/Unmanned Aerial Vehicles (UAVs) and AI has resulted innovative strides in wildlife conservation. This study utilised UAV imagery to record high-resolution data of aquatic habitat and species along the Ganga River and employed deep learning algorithms to analyse the data. Through extensive field surveys in the Hastinapur Wildlife Sanctuary, 7,025 photos representing a variety of environments, including 20,000 annotated samples of aquatic animals such as turtles and gharials were generated. Vision based computing capabilities such as pattern recognition model were developed to identify these species. To enrich and enhance the dataset and model, we used different image pre-processing techniques. Slight rotation (±5 degrees), minor cropping (up to 10%), and adjustments in brightness, saturation, and shear (±15%) were applied. Controlled blur (up to 0.5%) and exposure modifications (±5%) were also implemented on the image dataset to improve accuracy. Three Convolutional Neural Network (CNN) architectures, single-stage detectors named YOLO v7, YOLO v8, and Roboflow 3.0, were used for detecting the select species. Results show YOLO v8 excels, achieving mean average precision (mAP) of 98.8% for gharial and 92.2% for turtle detection, with a rapid average detection time of 0.308 seconds per frame at 3200 × 3200 resolution. Additionally, our model demonstrates real-time species detection capability through innovative frame sampling techniques with UAVs. This methodology provides promising technique to collect scientific data on IUCN red listed turtles and critically endangered gharials, allowing detection, monitoring, and real time counting with minimal intrusion. In conclusion, the fusion of UAVs and deep learning promises to revolutionize habitat monitoring, aiding conservation decision-making.

AgroFarmIQ: Advanced Crop Monitoring and Yield Prediction

In response to the imperatives of modern agriculture, our project presents a comprehensive precision agriculture solution centered around a multifunctional robocar. This autonomous vehicle integrates IoT sensors for data-driven crop management, employing machine learning for monitoring and predicting yields while optimizing resource utilization. Automation features include automated obstacle avoidance, periodic image capture, and automatic water spraying for real-time data collection and management, empowering farmers with actionable insights for informed decision-making. Additionally, a web-based platform utilizing Streamlit framework facilitates rapid and accurate crop disease detection using a meticulously trained VGG19 model from uploaded images. By embracing advanced technology, our solution aims to enhance agricultural productivity and pave the way for a sustainable future. Keyword: Precision agriculture, Robocar, IoT sensors, Machine learning, Crop management, Automated farming, Data-driven agriculture, Resource optimization, Real-time monitoring, Sustainable farming

Validation of a Loop-Mediated Isothermal Amplification-Based Kit for the Detection of Legionella pneumophila in Environmental Samples According to ISO/TS 12869:2012.

Legionella pneumophila is a freshwater opportunistic pathogen and the leading cause of severe pneumonia known as Legionnaires' disease. It can be found in all water systems and survives in biofilms, free-living amoebae, and a wide variety of facilities, such as air conditioning and showers in hospitals, hotels and spas. The reference cultural method allows for the isolation and identification in many days, and in addition, it does not detect viable but rather non-culturable bacteria, increasing the risk of infection. In this context, a new LAMP-based (loop-mediated isothermal amplification) kit was developed, allowing for the rapid, sensitive, and labor-saving detection of L. pneumophila. The kit, "Legionella pneumophila Glow", was validated according to ISO/TS 12869:2012, testing sensitivity, inclusivity and exclusivity, and kit robustness. Sensitivity showed that the "Legionella pneumophila Glow" kit can detect up to 28 plasmid copies/µL. Robustness tests showed consistent results, with both contamination levels and the matrices used giving reproducible results. Furthermore, real samples were evaluated to compare the performance of the two methods. The LAMP kit "Legionella pneumophila Glow" proved a useful option for the rapid, efficient, and labor-saving screening of different typologies of water samples, offering significant advantages over the traditional method, as it is characterized by a high sensitivity, ease of use for laboratory testing, and a large reduction in analysis time, making it an asset to official controls.

Accurate analysis of coal calorific value using NIRS-XRF: Utilizing RF classification and PLSR subtype modeling

Rapid and precise measurement of the calorific value of coal is crucial for coal chemical enterprises. However, due to the wide variety of coal sources and the diverse types of coal, the matrix effect significantly reduces the accuracy of spectroscopic rapid detection techniques for coal quality. To address this issue, this study conducts research on a coal calorific value detection method based on the combined use of near-infrared spectroscopy (NIRS) and X-ray fluorescence spectroscopy (XRF), exploring the feasibility of identifying coal types first and then predicting calorific value based on corresponding subtype models. In the aspect of spectral preprocessing, fusion of the dual spectra is achieved using techniques such as truncation, smoothing, Standard Normal Variate (SNV), and normalization. For coal type identification, the identification performance of five machine learning algorithms is compared, and the random forest (RF) algorithm is used to improve the identification accuracy to 98.43 %. In terms of prediction modeling, a comparison is made between traditional holistic models based on Partial Least Squares Regression (PLSR) and subtype models built according to coal types, revealing that the latter demonstrating stronger generalization ability and robustness. Accordingly, a precise prediction strategy for coal calorific value based on the combined use of NIRS and XRF in complex coal types is proposed: first, the RF algorithm is used to automatically identify coal types based on the fusion spectrum of NIRS and XRF, and then the calorific value of the corresponding coal type is accurately predicted using the PLSR subtype model selected according to the identification results. Industrial test results indicate that the average absolute error (AAE), root mean square error of prediction (RMSEP), average relative error (ARE), and standard deviation (SD) for coal calorific value prediction are 0.20 MJ/kg, 0.24 MJ/kg, 0.79 %, and 0.04 MJ/kg, respectively, meeting the requirements of enterprises. The research findings provide a practical technical method for timely and accurate acquisition of coal calorific value data, addressing the interference problem of matrix effects in complex coal type scenarios and holding broad application prospects in the coal-based energy industries.

Multiple-component covalent organic frameworks for simultaneous extraction and determination of multitarget pollutants in sea foods

Persistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have been raising global concerns due to their toxic effects on environment and human health. The monitoring of residues of POPs in seafood is crucial for assessing the accumulation of these contaminants in the study area and mitigating potential risks to human health. However, the diversity and complexity of POPs in seafood present significant challenges for their simultaneous detection. Here, a novel multi-component fluoro-functionalized covalent organic framework (OH-F-COF) was designed as SPE adsorbent for simultaneous extraction POPs. On this basis, the recognition and adsorption mechanisms were investigated by molecular simulation. Due to multiple interactions and large specific surface area, OH-F-COF displayed satisfactory coextraction performance for PFASs, PCBs, and BPs. Under optimized conditions, the OH-F-COF sorbent was employed in a strategy of simultaneous extraction and stepwise elution (SESE), in combination with HPLC−MS/MS and GC−MS method, to effectively determined POPs in seafood collected from coastal areas of China. The method obtained low detection limits for BPs (0.0037 −0.0089 ng/g), PFASs (0.0038 −0.0207 ng/g), and PCBs (0.2308 −0.2499 ng/g), respectively. This approach provided new research ideas for analyzing and controlling multitarget POPs in seafood. Environmental implicationsPersistent organic pollutants (POPs), such as perfluoroalkyl and polyfluoroalkyl substances (PFASs), polychlorinated biphenyls (PCBs), and bisphenols (BPs), have caused serious hazards to human health and ecosystems. Hence, there is a need to develop a quantitative method that can rapidly detect POPs in environmental and food samples. Herein, a novel multi-component fluorine-functionalized covalent organic skeletons (OH-F-COF) were prepared at room temperature, and served as adsorbent for POPs. The SESE-SPE strategy combined with chromatographic techniques was used to achieve a rapid detection of POPs in sea foods from the coastal provinces of China. This method provides a valuable tool for analyzing POPs in environmental and food samples.

Metal-organic frameworks constructed with base-acid mixed ligands and its potential as dual-functional fluorescent probe toward Fe3+/Al3+ ions

By virtue of mixed ligand strategy (2,5-Bis(1H-pyrazole-4-ylmethylene)-cyclopentanone and 2,5-dihydroxyterephthalic acid / terephthalic acid), four new metal–organic frameworks (MOFs) were fabricated and characterized. When 2,5-dihydroxyterephthalic acid acted as auxiliary ligand, MOFs 1–3 show 1D chain structure. 4 containing auxiliary ligand terephthalic acid (without − OH group) displays a 2D layer structure. Notably, 1 exhibits the significant selectivity toward Fe3+ and Al3+ ions, whereas the fluorescence intensity of 4 changed slightly in the presence of various metal ions, indicating that uncoordinated –OH in 1 plays an important role in sensing process. Moreover, it is revealed that 1 could act as dual-functional fluorescent probe toward Fe3+ and Al3+ with selectivity, sensitivity and reusability, and it could be prepared as fluorescent test papers for rapid on-site detection.

Customized carbon dots as a fluorescent sensor for detecting paraquat through enhanced excited state binding energy

A novel fluorescent sensor utilizing customized carbon dots (CDs) has been developed for the rapid and efficient detection of the herbicide paraquat. The 4,4′-diamino-2,2′-stilbenedisulfonic acid (DSD acid) functionalized CDs (DSD-CDs) were synthesized by a hydrothermal method using citric acid and L-cysteine as precursors, followed by amidation with DSD acid. The resultant DSD-CDs exhibited bright blue fluorescence upon UV excitation. The fluorescence of the functionalized carbon dots was effectively quenched by paraquat, following a static quenching mechanism as confirmed by fluorescence lifetime measurements. Interestingly, the mechanism study revealed that the excited-state of the DSD-CDs could interact effectively with paraquat, resulting in stronger fluorescence quenching compared to the ground-state. The sensor showed high sensitivity, selectivity, and stability, with a wide linear range of 0.01 ∼ 2 mg/L (R2 = 0.997) and a low detection limit of 0.003 mg/L. The developed fluorescent sensor demonstrated exceptional potential for paraquat detection in actual samples, encompassing blood, urine, soybean, and river water, with recovery rates exceeding 83 % and relative standard deviations (RSD) below 10 %. This underscores its practical utility in environmental protection and clinical testing applications.

Rapid and accurate detection of Dendrobium officinale adulterated with lower-price species using NMR characteristic markers integrated with artificial neural network

Rapid and accurate detection of Dendrobium officinale adulterated with lower-price species using NMR characteristic markers integrated with artificial neural network

A novel laser-EMAT ultrasonic longitudinal wave resonance method for wall thickness measurement at high temperatures

In this paper we propose a novel ultrasonic longitudinal wave resonance method for measuring the thickness of metal walls using a laser-electromagnetic acoustic transducer (Laser-EMAT). The method is based on the surface constraint mechanism (SCM) of the material and is expected to be capable of accurately detecting local thinning of metal walls in a non-contact manner and at high temperatures. Based on finite element analysis of laser-EMAT ultrasonic resonance measurement of aluminum alloy thickness, we investigated the effects of such key factors as SCM, irradiation parameters of laser source, and the size of EMAT receiving coil on the accuracy of thickness measurement (resonance frequency position) and on the amplitude of the resonance wave. Both numerical simulations and experiments are conducted to demonstrate that the measurement accuracy of the proposed method is not affected by SCM, irradiation laser source parameters, and EMAT receiving coil size, and that accurate detection of stepped aluminum plates with thickness thinning from 3.0 mm to 0.5 mm is achieved. Furthermore, we were able to perform rapid detection of aluminum thin plate thickness at 500 °C temperature with an EMAT lift-off of 5.0 mm and achieved a relative experimental error as small as 3.40 %. The results obtained in this study showed that the proposed method performed well in non-contact measurement of metal thinning in harsh environment of high temperature.

A Novel Near-Infrared Fluorescent Probe for the Detection of Endogenous Peroxynitrite (ONOO-) in Living Cells.

In this study, we present a novel near-infrared (NIR) fluorescent probe Nile-ONO designed for the selective and sensitive detection of ONOO-. The probe Nile-ONO employed Nile red as the fluorophore, with diphenylphosphinate serving as the reaction site. In the presence of ONOO-, the probe Nile-ONO exhibits remarkable fluorescence enhancement at 659nm, with a response time of less than 20min and a low detection limit of 0.32 µM. Importantly, MTT assays demonstrate low cytotoxicity in living cells. Furthermore, Nile-ONO has excellent imaging capabilities for endogenous ONOO-. Overall, this work introduces a valuable new method for the rapid detection of ONOO- in biological systems.

A rapid solid-phase extraction purification combined with UPLC[sbnd]MS/MS used for simultaneous determination of eight tetracyclines and three metabolite in chicken and pork

A method for rapid detection and identification of 8 tetracyclines (TCs) and 3 metabolite residues in chicken and pork was established and verified by combining rapid solid-phase extraction (SPE) technology with ultrahigh-performance liquid chromatography-tandem mass spectrometry (UPLCMS/MS). After a sample was extracted, it was purified with a PRiME HLB cartridge, and the cartridge did not need to be activated, balanced, or eluted. The filtrate was dried under nitrogen, redissolved, and subsequently analyzed via UPLCMS/MS. The limits of detection for chicken and pork were 0.05–0.48 μg/kg and 0.05–0.52 μg/kg, respectively. The limits of quantification were 0.16–1.61 μg/kg and 0.16–1.75 μg/kg, respectively. The recoveries were 85.15%–107.50% and 84.21%–106.37%, respectively. The established method was suitable for qualitative and quantitative analyses of TCs and their metabolite residues in chicken and pork and has been used for the detection of TC in chicken and pork samples from local markets.

An enzymatic recombinase amplification assay combined with CRISPR-Cas12a for the rapid detection of acute hepatopancreatic necrosis disease in shrimp Penaeus vannamei

An enzymatic recombinase amplification assay combined with CRISPR-Cas12a for the rapid detection of acute hepatopancreatic necrosis disease in shrimp Penaeus vannamei