Detection Applications Research Articles

Enhancing gamma-ray detection: Processing grooved microstructures on LYSO crystal with femtosecond laser.

Gamma-ray detection plays a crucial role in the fields of biomedicine, space exploration, national defense, and security. High-precision gamma photon detection relies on scintillation crystals, which attenuate gamma rays through mechanisms such as photoelectric effect and Compton scattering. These interactions generate light signals within the scintillation crystal, which are subsequently converted into electronic signals using photodetectors, enabling accurate readout, and analysis. Improving the readout efficiency of visible photons in crystal detectors can significantly improve the efficiency of gamma-ray detection. Scintillator crystals are usually hard and brittle materials, which are difficult to process. In this paper, we innovatively propose the method of using a femtosecond laser to process grooved microstructures on the light output surface of scintillator crystals to improve the detection efficiency, and thus enhance the comprehensive performance of gamma-ray detection. Optical simulation software is first used to explore the enhancement of the light output performance by the grooved microstructures. Subsequently, a 5-dimension system for femtosecond laser processing of scintillator crystals was constructed, which can achieve accurate processing of grooved structures. Finally, the feasibility of the study was verified by applying grooved microstructure on crystal bars and crystal arrays. TracePro simulation results showed an average efficiency improvement in light output of 33.56% within the groove parameters: spacing from 20 to 140µm, depth from 8 to 28µm, and width from 10 to 30µm. A custom-designed readout electronic system for gamma detection and a laser processing platform was then constructed to evaluate the feasibility of applying grooved structures to the lutetium-yttrium oxyorthosilicate (LYSO) crystal surface. According to the simulation results, 12 groups of crystal bars were fabricated with spacings from 60 to 140µm, depths from 7 to 16µm, and widths from 11 to 14µm. Experimental results showed an average improvement of 20.4% in light output for the crystal bars, and that of the crystal arrays can be improved by 6.85% on average. This study introduces a method of using femtosecond lasers to fabricate grooved microstructures on LYSO crystal surfaces, which has demonstrated a significant improvement in light output in both simulation and experimentation. This method can be applied to the production of crystal arrays at a low cost and on a large scale, showing promising potential for common gamma detection applications, such as medical imaging, industry, and astrophysics.

LoRaWAN-based smart water management IoT applications: a review

ABSTRACT The Internet of Things (IoT) is one area of the technology field that has grown significantly in recent years. Users can control and monitor IoT systems through smart devices remotely. Many other technologies, such as Zigbee, Wi-Fi, and Bluetooth, are used in IoT systems, however, they consume a lot of energy. Also, these networks are incompatible with battery-powered devices. The Low Power Wide Area Network (LPWAN) was developed to direct this concern and to be a suitable option for longer ranges. A Long Range Wide Area Network (LoRaWAN) is a type of Low Power Wide Area Network (LPWAN) includes battery-powered devices and can communicate in both directions. This study provides comprehensive review of LoRaWAN-based water-management applications. In this survey, we present an overview of LPWAN technologies and compare them. We concentrate on LoRaWAN technology, its architecture, and its main characteristics. Furthermore, we review LoRaWAN-based water applications and classify them into three categories which include: water-quality monitoring applications, water-metering applications, and water-leakage detection applications. We also discuss scalability, simulation, security, and applications of LoRaWAN in water-management systems. Finally, we evaluate the usefulness of LoRaWAN and provide recommendations for researchers to utilize the benefits of LoRaWAN in IoT water-management applications.

Fano resonances of coupled magnetic-dipole modes in a dielectric triplet for precise discrimination of deep-subwavelength microspheres

Mie resonances in dielectric particles play a crucial role in light–matter interaction, characterized by strong multipolar optical responses and low dissipative losses. This study presents an innovative methodology for precise discrimination of the size and quantity of deep-subwavelength microspheres by leveraging Fano resonances in coupled magnetic-dipole (MD) modes of a CaTiO3-ceramic-particle triplet. The multifaceted characteristics of coupled MD resonances exhibit high sensitivity and specificity in detecting minute changes in the ambient environment. The alignment of theoretical and experimental results demonstrates the reliability and accuracy of our approach, highlighting its versatility and adaptability. This principle of deep-subwavelength measurement can also be appreciated at the nanoscale, indicating great potential for its applications in biological detection, chemical sensing, and environmental monitoring. The multifaceted Fano resonances of coupled Mie dipole modes establish a robust framework for future research in real-time monitoring and precision detection.

Effect of atomic configuration and biaxial strain on the electronic properties of CdxZn1−xTe and Hg1−xCdxTe alloys: a first principles study

Abstract The electronic properties of CdZnTe and HgCdTe alloys intended for ionizing radiation and infrared detection applications are studied using first-principles calculations based on density functional theory (DFT). The Heyd, Scuseria, and Ernzerhof (HSE) functional and the modified Becke-Johnson (mBJ) potential are employed to predict the band gap and bowing parameters of the ternary alloys over the full composition range. We demonstrate the impact of the atomic disorder on the band gap energy and thermodynamic stability of the alloys by analyzing all distinct atomic arrangements of supercells up to 16 atoms. Subsequently, we investigate the impact of biaxial strain in the (001), (111), and (112) crystal planes on the electronic properties of binary ZnTe, CdTe, and HgTe compounds, as well as the ternary alloys with composition x = 25%, 50%, and 75%. We observe distinct behavior of the compounds under tetragonal and trigonal deformations, particularly for the semimetal HgTe and its alloys, where we find an unexpected non-linear band gap dependence of HgCdTe on the Cd composition, strain orientation, and strain magnitude. These results can be used to aid in the design of optoelectronic devices with targeted functionality.

Development of recombinase polymerase amplification combined with a lateral flow dipstick assay for rapid and simple detection of Tylenchulus semipenetrans in soil.

Tylenchulus semipenetrans, the causal agent of citrus slow decline disease, is one of the most destructive plant-parasitic nematodes in all citrus-growing regions of the world, causing significant reductions in citrus growth and yield. Accurate and rapid detection of T. semipenetrans is critical for the diagnosis and effective control of the disease. We developed a rapid, visual, isothermal detection method using recombinase polymerase amplification combined with a lateral flow dipstick (RPA-LFD) assay to detect T. semipenetrans in soil. The primers and a probe were designed based on sequence differences in the internal transcribed spacer region 1 (ITS1) of ribosomal DNA (rDNA) among T. semipenetrans and four other Tylenchulus species. The RPA reaction can be performed in 10-25 min at a constant temperature ranging from 30 to 45 °C, and the result can be read directly on the LFD within 3 min. Under the optimized conditions, the RPA-LFD assay could specifically detect T. semipenetrans with a sensitivity as low as 10-2 second-stage juveniles/0.5 g soil, which was 10-fold more sensitive than that of the conventional PCR assay. Furthermore, we combined a soil DNA extraction method with the RPA-LFD assay to achieve simple and rapid detection of T. semipenetrans in natural field soil samples within 1 h. The developed RPA-LFD assay is a simple, rapid, specific, sensitive and visual method for detecting T. semipenetrans. It shows great potential for on-site rapid detection applications, especially in resource-limited settings. © 2025 Society of Chemical Industry.

Detecting and Quantifying Glaucomatous Visual Function Loss With Continuous Visual Stimulus Tracking: A Case-Control Study.

Continuous visual stimulus tracking could be used as an easy alternative to standard automated perimetry (SAP) for visual function screening. With continuous visual stimulus tracking, we simplified the perimetric task to following a moving dot on a screen with the eyes. Here, we determined whether tracking performance (the agreement between gaze and stimulus position) enables the detection and quantification of glaucomatous visual function loss (in terms of SAP), and whether it shows a learning effect. We evaluated the tracking performance of 36 cases with early, moderate, or severe glaucoma (median with interquartile range [IQR] age = 70 [67-74] years) and 36 controls (median = 70, IQR = 67-72 years). All participants monocularly tracked a moving stimulus (Goldmann size III) at 3 Weber contrast levels: 40, 160, and 640%, while their eye movements were recorded. Glaucoma decreased the tracking performance, with the most severe reduction in the severe glaucoma cases. A distinction between groups was possible, but depended on the contrast level: tracking performance of early glaucoma cases was significantly different from controls only at 40% contrast. Within the cases, glaucomatous visual function loss (SAP Mean Sensitivity [MS]) was best correlated with tracking performance when using 160% contrast. There was no significant learning effect. Overall, the data indicate that it is possible to detect and quantify glaucomatous visual function loss with continuous visual stimulus tracking. Continuous visual stimulus tracking is an easy, fast, and intuitive technique that has the potential for diagnostic applications in detection of new glaucoma cases and monitoring of previously diagnosed cases.

A Novel Ensemble Belief Rule-Based Model for Online Payment Fraud Detection

In recent years, with the rapid development of technology and the economy, online transaction fraud has become more and more frequent. In the face of massive records of online transaction data, manual detection methods are long outdated, and machine learning methods have become mainstream. However, although traditional machine learning methods perform well in fraud detection tasks, the lack of interpretability and class imbalance issues have always been pain points that are difficult to resolve for such methods. Unlike traditional methods, the belief rule base, as a rule-based expert system model, can integrate expert knowledge and has excellent interpretability. In this paper, we propose an innovative ensemble BRB (belief rule base) model to solve the credit card fraud detection problem by combining an ensemble learning framework with the BRB model. Compared with traditional machine learning methods, the proposed model has the advantage of high interpretability. And compared with traditional BRB models, the ensemble framework enables better performance in dealing with highly imbalanced classification tasks. In an experimental study, two datasets of credit card fraud detection from Kaggle are used to validate the effectiveness of this work. The results show that this new method can achieve excellent performance in the application of fraud detection and is capable of effectively mitigating the impact of an imbalanced dataset.

Real-Time 0.89 THz Terahertz Imaging with High-Electron-Mobility Transistor Detector and Hydrogen Cyanide Laser for Non-Destructive Nut Detection

We present a method for real-time terahertz imaging that employs a hydrogen cyanide (HCN) laser as a terahertz source at 0.89 THz and an AlGaN/GaN high-electron-mobility transistor (HEMT) terahertz detector as a camera. We developed an HCN laser and constructed a transmission imaging system based on it. This combination utilizes a high-power HCN laser with a highly sensitive terahertz detector, enabling practical applications of real-time terahertz imaging. A resolution test plane was produced to determine that the system could achieve a lateral resolution of 2 mm, and real-time terahertz imaging was carried out on Siemens star, pistachios, and sunflower seeds. The results demonstrate that the hidden structures inside nuts can be observed by terahertz imaging. Through our analysis of terahertz images of both sunflower seeds and pine nuts, we successfully assessed their fullness and demonstrated the capability to distinguish between full and unfilled nuts. These findings validate the potential of this technique for future applications in nut detection. We discuss the limitations of the current setup, potential improvements, and possible applications, and we outline the introduction of aspherical lenses and terahertz transmission tomography.

YOLOv7scb: A Small-Target Object Detection Method for Fire Smoke Inspection

Fire detection presents considerable challenges due to the destructive and unpredictable characteristics of fires. These difficulties are amplified by the small size and low-resolution nature of fire and smoke targets in images captured from a distance, making it hard for models to extract relevant features. To address this, we introduce a novel method for small-target fire and smoke detection named YOLOv7scb. This approach incorporates two key improvements to the YOLOv7 framework: the use of space-to-depth convolution (SPD-Conv) and C3 modules, enhancing the model’s ability to extract features from small targets effectively. Additionally, a weighted bidirectional feature pyramid network (BiFPN) is integrated into the feature-extraction network to merge features across scales efficiently without increasing the model’s complexity. We also replace the conventional complete intersection over union (CIoU) loss function with Focal-CIoU, which reduces the degrees of freedom in the loss function and improves the model’s robustness. Given the limited size of the initial fire and smoke dataset, a transfer-learning strategy is applied during training. Experimental results demonstrate that our proposed model surpasses others in metrics such as precision and recall. Notably, it achieves a precision of 98.8% for small-target flame detection and 90.6% for small-target smoke detection. These findings underscore the model’s effectiveness and its broad potential for fire detection and mitigation applications.

Detecting the droplet and bubble in the oil-gas-water three-phase medium via ultrasonic A-scan signal-assisted B-mode imaging method

Abstract Accurately detecting the parameters of bubbles and droplets in the oil-gas-water three-phase medium is essential to critical decision-making about industrial production. An ultrasonic A-scan signal-assisted B-mode imaging (AS-assisted-BI) method that combines a B-mode imaging (BI) method and an A-scan signal (AS) method is developed to simultaneously visualize the oil-gas-water three-phase medium, distinguish the gas bubble and oil droplet, and measure the parameters about their sizes and locations. The applicability of the plane wave imaging (PWI) method for visualizing the oil-gas-water three-phase medium with less computational cost was confirmed. Then, the influence of the bubble and droplet characteristics, such as acoustic impedance difference, size, and location, upon the ultrasonic propagation mechanism are studied to understand the B-mode image and A-scan signal as the design basis of the BI and AS method. The BI method locates the least key points from the B-mode image based on the PWI method compared with the conventional industrial B-mode image method. The AS method automatically locates the key point from the A-scan signal rather than manually marking it in the previous study. Two options for the AS-assisted-BI method, suitable for different detection requirements and applications, have been designed. The numerical simulation and experimental results demonstrated that the proposed method received excellent precision, robustness, and efficiency for detecting the acoustic impedance difference between the bubble and droplet, the longitudinal location of the bubble front surface, and the droplet’s size and center location.

Passive matrix Schottky barrier 2D photodiode array on graphene/SOI platform

Abstract We fabricated 4 × 4 pixel two-dimensional (2D) photodiode array (PDA) out of monolayer graphene and n-type silicon (n-Si) electrodes on a silicon-on-insulator (SOI) substrate. Our device design is based on passive matrix sensor array architecture consisting of individual graphene and silicon electrodes aligned perpendicular to each other. I-V measurements conducted at room temperature to reveal the electronic characteristics of graphene and Si junction in the device structure. The spectral responsivity, respond speed and the optical crosstalk of each G/Si pixels in the array have been determined by wavelength resolved and time dependent photocurrent spectroscopy measurements. Micro-Raman mapping measurements were conducted to examine the surface coverage of graphene electrode on each pixel. The results of Micro-Raman mapping measurements were correlated with the corresponding photocurrent data acquired under light illumination. We believe that this work constitutes a significant potential in integrating variety of 2D materials and SOI technology into next generation image sensing and multiple pixel light detection applications.

The Present State and Potential Applications of Artificial Intelligence in Cancer Diagnosis and Treatment.

An aberrant increase in cancer incidences has demanded extreme attention globally despite advancements in diagnostic and management strategies. The high mortality rate is concerning, and tumour heterogeneity at the genetic, phenotypic, and pathological levels exacerbates the problem. In this context, lack of early diagnostic techniques and therapeutic resistance to drugs, sole awareness among the public, coupled with the unavailability of these modern technologies in developing and low-income countries, negatively impact cancer management. One of the prime necessities of the world today is the enhancement of early detection of cancers. Several independent studies have shown that screening individuals for cancer can improve patient survival but are bogged down by risk classification and major problems in patient selection. Artificial intelligence (AI) has significantly advanced the field of oncology, addressing various medical challenges, particularly in cancer management. Leveraging extensive medical datasets and innovative computational technologies, AI, especially through deep learning (DL), has found applications across multiple facets of oncology research. These applications range from early cancer detection, diagnosis, classification, and grading, molecular characterization of tumours, prediction of patient outcomes and treatment responses, personalized treatment, and novel anti-cancer drug discovery. Over the past decade, AI/ML has emerged as a valuable tool in cancer prognosis, risk assessment, and treatment selection for cancer patients. Several patents have been and are being filed and granted. Some of those inventions were explored and are being explored in clinical settings as well. In this review, we will discuss the current status, recent advancements, clinical trials, challenges, and opportunities associated with AI/ML applications in cancer detection and management. We are optimistic about the potential of AI/ML in improving outcomes for cancer and the need for further research and development in this field.

Correlation Properties of Color Histograms in the Case of Image Quality Decreasing

In image recognition tasks, objects are identified by examining various features such as texture, color, contour detection, and statistical or semantic descriptions. One widely used approach for extracting image attributes is the analysis of intensity histograms. While the traditional RGB color model is commonly used in digital image processing, it is often more effective to analyze color properties in HS* systems (such as HSL, HSV, and HSI) since these systems more closely resemble the spectral representation of color. A key characteristic shared by these three systems is the use of the H (Hue) coordinate, which is represented as an angular value within a cylindrical coordinate system. The paper investigates the possibility of using color histograms generated in HS* spaces for identifying images that have undergone various types of distortions. The CQ100: A High-Quality Image Dataset for Color Quantiza-tion Research was chosen for the research. The non-quantized section of the CQ100 dataset consists of 100 RGB images in PNG format, each with a resolution of 768×512 pixels and a color depth of 24 bits. The study examines how different distortions, which can occur during real-time photo and video capture, affect the color properties of images. Specifically, the re-search focuses on distortions caused by rotation, noise, blurring, and optical aberration. His-tograms were compared using the Pearson cross-correlation coefficient, and the findings re-veal that the correlation remains high for the same image despite the applied distortions. Conversely, the correlation coefficient between different images is low for most of the studied objects. These results suggest that color histograms could be effectively used for image identi-fication tasks, even when images are significantly distorted, as is common in image registra-tion processes. The applicability of correlation detection as a method for histograms com-parison is considered regardless of the relative simplicity of its calculation. This approach could contribute to the development of faster image recognition systems.

Sagnac Interference-Based Contact-Type Fiber-Optic Vibration Sensor

The observation and evaluation of vibration signals is crucial for enhancing engineering quality and ensuring the safe operation of equipment. This paper proposes a fiber-optic vibration sensor based on the Sagnac interference principle. The polarization-maintaining fiber (PMF) is spliced between two single mode fibers (SMFs) to form the SMF-PMF-SMF (SPS) fiber structure. The Sagnac interferometer consists of an SPS fiber structure connected to a 3 dB coupler. Due to the principle of the elastic-optical effect, the interferometric spectrum of the PMF-based Sagnac interferometric structure changes when the PMF is subjected to stress, enabling vibration to be measured. The experimental results show that the relative measurement error of the fiber-optic vibration sensor for healthy and faulty bearings is less than 1.8%, which verifies the effectiveness and accuracy of the sensor. The sensor offers benefits of excellent anti-vibration fatigue characteristics, simple production, small size, light weight, and has a wide range of applications in mechanical engineering, fault detection, safety and security, and other fields.

Tunable Emissions in Zero‐Dimensional (C6H5CH2NH3)3InBr6 Enabled by Controlled Structural Amorphization

ABSTRACTZero‐dimensional (0D) hybrid metal halides (HMHs) hold great promise as multifunctional emitters. However, precise functionalization of organic moieties and controlled modulation of self‐trapped exciton (STE) emission from inorganic polyhedra remain challenging. This study introduces 0D (PMA)3InBr6 (PMA+ = C6H5CH2NH3+) as a multifunctional emitter, leveraging pressure‐induced structural regulation to control photoluminescence properties. Increasing pressure leads to simultaneous contraction and distortion of InBr63− octahedra, shifting the STE emission color from orange to green. At high compression, structural amorphization quenches STE emission, but upon pressure release, a bright cyan emission from the PMA+ cation emerges, with intensity approximately 21 times stronger than that of the initial STE emission. The enhanced emission is attributed to altered molecular configurations, disrupted intermolecular contacts, and reduced lattice vibrations, collectively suppressing excimeric coupling and minimizing nonradiative losses in the recovered amorphous phase. Furthermore, emission conversion is also achieved via laser‐induced structural amorphization, expanding the potential of (PMA)3InBr6 for direct laser writing and sensitive laser detection applications.

A magnetic bead-based dual-aptamer sandwich assay for quantitative detection of ciprofloxacin using CRISPR/Cas12a.

Ciprofloxacin (CIP) is a broad-spectrum fluoroquinolone antibiotic, and its excessive residues in food and water sources pose potential risks to human health. Therefore, there is a need for a rapid and convenient method for its accurate quantification. The clustered regularly interspaced short palindromic repeat (CRISPR)/Cas12a system has gained extensive application in signal detection and amplification due to the trans-cleavage activity of Cas12a. In this study, we devised a novel magnetic bead-based dual sandwich aptamer coupled with a CRISPR/Cas12a system for the precise quantification of CIP in milk, river water, and honey. Through the incorporation of a magnetic bead-based dual aptamer sandwich approach, the concentration of CIP in the samples was pre-enriched. Additionally, by optimizing the Fluorescence-Quencher (F-Q) probe concentration, detection aptamer (APTd) concentration, and assay duration, the limit of blank (LOB) of the system was determined as 362nM, while the limit of detection (LOD) was determined as 403nM. This enabled the accurate quantification of CIP within the linear range of 0.5μM to 0.2mM with high specificity. Moreover, the performance of this detection method was comparable to that of high-performance liquid chromatography (HPLC) in river water, milk, and honey samples.

Yerba mate tea mediated synthesis of nanoscale zero valent iron particles and their application in detection of Pb ions in water

Yerba mate tea mediated synthesis of nanoscale zero valent iron particles and their application in detection of Pb ions in water

Gold nanostars: From synthesis, properties to their application in food hazard detection

Gold nanostars: From synthesis, properties to their application in food hazard detection

Abstract TP166: Refining Deep Learning Application Diagnostic Accuracy in Intracerebral Hemorrhage (ICH): Focus on Subtle Hemorrhage Detection

Introduction: Deep-learning (DL)-based applications for Intracerebral Hemorrhage (ICH) detection on non-contrast computed tomography (NCCT) scans have demonstrated the potential to enhance diagnostic accuracy and efficiency amid the increasing radiologist workload. However, the sensitivity and specificity of these applications remain suboptimal, particularly in cases with subtle ICH (ICH volume < 5ml) and when confounding factors are present. This study aimed to enhance DL-based application, reduce false positives and improve the detection of subtle ICH. Methods: This study compared two versions of the DL-based application for ICH detection (CINA-ICH, Avicenna.AI, La Ciotat, France), both using a hybrid 2D/3D architecture. The first version, CINA-ICH, was trained on 8,994 representative CT scans (1,034 ICH+) from a cohort diverse in patient characteristics and acquisition parameters. The improved version, CINA-ICH(i), was trained on the same dataset enriched with 600 challenging cases (several confounding factors present) and included a specialised 3D network for subtle ICH detection, that underwent independent training on 2,238 CT-scans including 399 subtle ICH. The evaluation dataset included 479 NCCT scans (131 ICH+ including 24 subtle ICH) from over 200 U.S. hospitals, 4 scanner makers and 35 scanner models. Ground truth was determined by consensus among three board-certified radiologists. Sensitivity, Specificity, Accuracy and Matthews Correlation Coefficient (MCC) of CINA-ICH and CINA-ICH(i) were evaluated with a detailed analysis of false positive cases and subtle ICHs. Results: CINA-ICH(i) demonstrated a statistically significant (p<0.05) improvement in diagnostic performance compared to CINA-ICH: 88.5% vs. 83.2% for sensitivity, 94% vs. 85.6% for specificity, and 92.5% vs. 85% for accuracy. MCC for CINA-ICH(i) was improved from 0.65 to 0.81. CINA-ICH(i) reduced false positives from 50 to 21, effectively minimizing the detection of spurious findings and anatomical structures such as falx cerebri and sinuses. Additionally, CINA-ICH(i) enhanced the recognition of subtle ICH, detecting 37.5% of cases compared to only 8.3% detected by CINA-ICH. Conclusions: The implemented strategy to enhance the DL-based algorithm successfully increased ICH detection accuracy. This advancement demonstrates the potential for optimized algorithms to better support clinical decision-making by reducing false positives and improving the detection of subtle ICH cases.

A sensitive PnpR-based biosensor for p-nitrophenol detection.

A common aromatic and phenolic pollutant, p-nitrophenol (PNP), is widely used in various industry and has serious risk to the environmental health. Biosensors have been extensively employed as an alternative technology for pollutants monitoring. By mining the new sensing elements, more specific biosensors could be characterized for highly sensitive detection. Herein, the PnpR transcription factor was identified to activate the transcription of pnpC1 by binding to PpnpC1 promoter region in P. putida DLL-E4, and PNP was recognized as its specific inducer. The PnpR-based biosensor for detection of PNP was developed, demonstrating adequate sensitivity in a liquid solution with satisfactory specificity. The biosensor was optimized by adopting a transcriptional amplifier, which increased the maximum output by 149-fold, and improved the detection limit by 100-fold, from 1mg/L to 10μg/L. These biosensors had a linear range of 5-80mg/L and 0.01-1.0mg/L for PNP determination, respectively. Then, the agarose gel entrapment-based biosensor was constructed and allowed a good of PNP detection in the range of 5-60mg/L in M9 solid agar within 70min, and a detection sensitive of 16.8mg/kg in soil. The good performance of the biosensor suggested its potential application of high-efficient and on-site detection in environmental matrices.