Field Of Environmental Monitoring Research Articles

Full-scale semantic segmentation of hyperspectral imaging based on spatial spatial-spectral joint network

Abstract. Hyperspectral images contain dozens or even hundreds of spectral bands, which contain rich spectral information and help distinguish different ground objects. Hyperspectral images have a wide range of applications in urban planning, environmental monitoring, and other fields. The semantic segmentation of hyperspectral images is one of the current research hotspots. The difficulty lies in the rich spectral information and strong correlation of hyperspectral images. Traditional semantic segmentation methods cannot fully extract information, which affects the accuracy of classification. This article utilizes an encoding decoding structure to simultaneously extract deep and shallow features of images. A REGCS convolution module was constructed using the idea of group convolution to extract spectral and spatial features of images. We compared the Salinas Valley dataset and MUUFL dataset with various classification algorithms. The experimental results show that compared with other classification models, the RESSU model has achieved stable and excellent results in hyperspectral image classification experiments. Among them, in the classification experiment of the Salinas Valley dataset, the accuracy of single class classification reached over 92%. In the effectiveness analysis experiment, we calculated different model parameter quantities to verify the performance of our method, and ultimately achieved good results.

A highly sensitive and selective fluorescent “on–off-on” peptide-based probe for sequential detection of Hg2+ and S2− ions: Applications in living cells and zebrafish imaging

Mercury ions (Hg2+) and sulfur ions (S2−), have caused serious harm to the ecological environment and human health as two kinds of highly toxic pollutants widely used. Therefore, the visual quantitative determination of Hg2+ and S2− is of great significance in the field of environmental monitoring and medical therapy. In this study, a novel fluorescent “on–off-on” peptide-based probe DNC was designed and synthesized using dipeptide (Asn-Cys-NH2) as the raw material via solid phase peptide synthesis (SPPS) technology with Fmoc chemistry. DNC displayed high selectivity in the recognition of Hg2+, and formed non-fluorescence complex (DNC-Hg2+) through 2:1 binding mode. Notably, DNC-Hg2+ complex generated in situ was used as relay response probe for highly selective sequential detection of S2− through reversible formation-separation. DNC achieved highly sensitive detection of Hg2+ and S2− with the detection limits (LODs) of 8.4 nM and 5.5 nM, respectively. Meanwhile, DNC demonstrated feasibility for Hg2+ and S2− detections in two water samples, and the considerable recovery rate was obtained. More importantly, DNC showed excellent water solubility and low toxicity, and was successfully used for consecutive discerning Hg2+ and S2− in test strips, living cells and zebrafish larvae. As an effective visual analysis method in the field, smartphone RGB Color Picker APP realized semi-quantitative detections of Hg2+ and S2− without the need for complicated device.

Innovative rapid liquid concentration measurement based on thermal lens effect and machine learning

This study addresses the critical need for rapid and online measurement of liquid concentrations in industrial applications. Although the thermal lens effect (TLE) is extensively explored in laser systems for determining thermal lens focal lengths, its application in quantifying solution concentrations remains underexplored. This research explores the relationship between various liquid concentrations and the interference fringes induced by the TLE. A novel approach is introduced, utilizing TLE to measure solution concentrations, with integration of image processing and discrete Fourier transform (DFT) techniques for feature extraction from interference rings. Further, machine learning, specifically backpropagation artificial neural network (BP-ANN), is employed to model concentration measurement. The model demonstrates high accuracy, evidenced by low root mean square error (RMSE) values of 3.055 and 5.396 for the training and test sets, respectively. This enables precise, real-time determination of soy sauce concentration, offering significant implications for industrial testing, environmental monitoring, and other related fields.

Biominerals and Bioinspired Materials in Biosensing: Recent Advancements and Applications.

Inspired by nature's remarkable ability to form intricate minerals, researchers have unlocked transformative strategies for creating next-generation biosensors with exceptional sensitivity, selectivity, and biocompatibility. By mimicking how organisms orchestrate mineral growth, biomimetic and bioinspired materials are significantly impacting biosensor design. Engineered bioinspired materials offer distinct advantages over their natural counterparts, boasting superior tunability, precise controllability, and the ability to integrate specific functionalities for enhanced sensing capabilities. This remarkable versatility enables the construction of various biosensing platforms, including optical sensors, electrochemical sensors, magnetic biosensors, and nucleic acid detection platforms, for diverse applications. Additionally, bioinspired materials facilitate the development of smartphone-assisted biosensing platforms, offering user-friendly and portable diagnostic tools for point-of-care applications. This review comprehensively explores the utilization of naturally occurring and engineered biominerals and materials for diverse biosensing applications. We highlight the fabrication and design strategies that tailor their functionalities to address specific biosensing needs. This in-depth exploration underscores the transformative potential of biominerals and materials in revolutionizing biosensing, paving the way for advancements in healthcare, environmental monitoring, and other critical fields.

DEVELOPMENT OF METHODOLOGY FOR MONITORING AND ASSESSING THE NATIONAL GREENING PROGRAM USING OPTICAL AND SAR DATA

Abstract. Launched primarily to address the growing concern of environmental sustainability and to combat climate change, the National Greening Program (NGP) is the most ambitious reforestation program undertaken in the Philippines. Given the enormous number of resources invested in the program, it is crucial to monitor and assess the effectiveness of this initiative. To do this, the success of the selected NGP site in Zambales Province was evaluated using land cover maps, Normalized Difference Vegetation Index (NDVI) from Sentinel-2 and MODIS, and Radar Vegetation Index (RVI) and Radar Forestation Degradation Index (RFDI) from Sentinel-1 Synthetic Aperture Radar (SAR) images. The index time series were processed in Google Earth Engine cloud computing platform. The findings indicate that the rise in vegetation density shown by the generally increasing trend in the NDVI and RVI was consistent with the increase in vegetation cover indicated by the land cover maps. This work contributes towards the field of environmental monitoring and resource management, with potential applications that extend beyond monitoring and assessment of the NGP and towards dealing with global issues regarding land use and environmental protection.

Flexible and stretchable photodetectors constructed by integrating photosensor, substrate, and electrode in one polymer matrix

Flexible photodetector (FPD) is a crucial component in modern wearable/flexible systems and plays a vital role in healthcare, environmental monitoring, flexible display, and other fields. However, achieving satisfactory interfacial adhesion among the electrode, photoactive material, and flexible substrate at the same time to fabricate FPDs with high flexibility, stretchability, stability, and mechanically compliance is still a challenge. In this study, we propose a green and eco-friendly water-system processing strategy to construct all-in-one FPDs, using borate ester polymers (BPs) as both the co-matrix of the photoactive layer substrate and the conductive ink. The flexible substrate exhibits a high adaptation to the conductive ink, and thus, efficient interface charge transfer with the complicated pattern electrode. The resulting FPD exhibits stable photoresponse under different bending angles and large mechanical strains. Even after 2000 cycles of light stimulation test at a whole device elongation of 50%, the FPD exhibits no significant decrease in the photoresponse performance. Such devices may provide new opportunities for multifunctional electronic skin and wearable electronic devices.

An Electroanalytical Enzymeless α-Fe2O3-ZnO Hybrid Nanostructure-Based Sensor for Sensitive Quantification of Nitrite Ions.

Nitrite monitoring serves as a fundamental practice for protecting public health, preserving environmental quality, ensuring food safety, maintaining industrial safety standards, and optimizing agricultural practices. Although many nitrite sensing methods have been recently developed, the quantification of nitrite remains challenging due to sensitivity and selectivity limitations. In this context, we present the fabrication of enzymeless iron oxide nanoparticle-modified zinc oxide nanorod (α-Fe2O3-ZnO NR) hybrid nanostructure-based nitrite sensor fabrication. The α-Fe2O3-ZnO NR hybrid nanostructure was synthesized using a two-step hydrothermal method and characterized in detail utilizing x-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), transmission electron microscopy (TEM), and X-ray photoelectron spectroscopy (XPS). These analyses confirm the successful synthesis of an α-Fe2O3-ZnO NR hybrid nanostructure, highlighting its morphology, purity, crystallinity, and elemental constituents. The α-Fe2O3-ZnO NR hybrid nanostructure was used to modify the SPCE (screen-printed carbon electrode) for enzymeless nitrite sensor fabrication. The voltammetric methods (i.e., cyclic voltammetry (CV) and differential pulse voltammetry (DPV)) were employed to explore the electrochemical characteristics of α-Fe2O3-ZnO NR/SPCE sensors for nitrite. Upon examination of the sensor's electrochemical behavior across a range of nitrite concentrations (0 to 500 µM), it is evident that the α-Fe2O3-ZnO NR hybrid nanostructure shows an increased response with increasing nitrite concentration. The sensor demonstrates a linear response to nitrite concentrations up to 400 µM, a remarkable sensitivity of 18.10 µA µM-1 cm-2, and a notably low detection threshold of 0.16 µM. Furthermore, its exceptional selectivity, stability, and reproducibility make it an ideal tool for accurately measuring nitrite levels in serum, yielding reliable outcomes. This advancement heralds a significant step forward in the field of environmental monitoring, offering a potent solution for the precise assessment of nitrite pollution.

Dual-Channel Mapping–Gas Column Concentration Inversion Method Based on Multispectral Imaging

Infrared multispectral imaging technology can achieve the long-distance, wide-ranging and fast detection of target gas, and has been widely used in the fields of dangerous-gas detection and environmental monitoring. However, due to the difficulty in acquiring background radiation as well as atmospheric disturbance and noise interference in the detection process, the quantitative detection of gas concentration has become a difficult problem to solve. Therefore, this paper proposes an inversion method for gas column concentration based on infrared multispectral imaging technology. Firstly, infrared background radiation images of the non-target gas absorption spectrum band were collected and converted into background radiation images of the target gas absorption spectrum band according to the dual-channel mapping relationship. Then, combined with the gas radiation images of the target gas absorption spectrum band, the column concentration distribution of the gas was obtained by using the measured calibration relationship between absorbance and column concentration. Experiments of gas detection in different environments were carried out, and the column concentration distribution of the target gas was inverted using this method; the results showed that the average relative error of the inversion of the gas column concentration was 4.84%, which enables the quantitative detection of gas column concentration in a complex environment.

Swellable microneedle-coupled light-addressable photoelectrochemical sensor for in-situ tracking of multiple pesticides pollution in vivo

In vivo monitoring of multiple pesticide contamination is of great significance for evaluating the health risks of different pesticides, agricultural production safety, and ecological and environmental assessment. Here, we report a hydrogel microneedle array coupled light-addressable photoelectrochemical sensor for tracking multiple pesticide uptake and elimination in living animals and plants, holding three prominent merits: i) enables in-situ detection of in vivo pesticides, avoiding cumbersome and complex sample transportation and handling processes; ii) allows repeated in vivo sampling of the same organism, improving tracking test controllability and accuracy; iii) avoids lethal sampling, providing a better understanding of the pesticides fate in living organisms. The coupled sensor is mechanically robust for withstanding more than 0.35 N per needle and highly swellable (800 %) for timely extraction of sufficient in vivo solution for analysis. For proof-of-concept, it achieves in-situ detection of atrazine, acetamiprid, and carbendazim efficiently and quantitatively in artificial agarose skin models, mouse skin interstitial fluids, and plant leaves with little inflammatory reaction. This simple, highly integrated, minimally invasive, and high-throughput in vivo monitoring method is ideal for future field environmental monitoring and plant and animal disease diagnosis.

Utilizing unmanned aerial vehicle technology for environmental monitoring: Future trends, methods, and applications

AbstractThis paper provides an overview of the application of Unmanned Aerial Vehicles (UAVs) in environmental monitoring, with a focus on air pollution surveillance. Initially, the paper highlights the significance of UAV technology for real‐time air quality monitoring in developing countries and explores the characteristics of fixed‐wing and multirotor UAVs in monitoring air pollutants. It then delves into methods of measuring air pollutants using UAV platforms, including the three‐dimensional distribution of pollutants around roadsides, green belts, and street‐facing communities. The advantages of UAV measurements, such as lower costs, greater flexibility, and the ability to monitor in three dimensions, are emphasized. Finally, the paper discusses future trends in the field of environmental monitoring using UAVs, including technological advancements, evolving regulatory policies, and integration with other technologies like Artificial Intelligence, big data analytics, and 5G communication. These developments suggest an increasingly significant role for UAVs in environmental monitoring, enhancing efficiency, reducing costs, and contributing to public participation and environmental awareness. However, due to the delays in publication and review processes, the most recent studies may not have been included in the literature review, leading to a scenario where the review might not reflect the latest research trends.

Multi-effect coupling enhanced self-powered heterojunction ultraviolet photodetector with ultra-low detection limit

Convenient, small-sized, and self-powered sensor units are easier to integrate, making them the future development direction of photodetectors. GaN materials with wide band gap are powerful candidates for UV detection, but most GaN based self-powered UV photodetectors have poor detection ability for the weak UV light, which limits their applications. This study reports a self-powered UV photodetector based on BaTiO3/GaN heterojunction, which can detect ultra-weak UV light at a minimum of 0.68 nW/cm2 by coupling the ferroelectric polarization, anti-reflection effect, and pyro-phototronic effect. The polarized BaTiO3 thin film on the surface of GaN has an enhanced residual polarization field and generated anti-reflection effect, which can regulate the heterojunction band structure, suppress the dark current and minimize surface reflection of GaN. Simultaneously enhanced pyro-phototronic effect can significantly improve the response speed and responsivity of the device. The response time and recovery time of the device are as low as 8.794 ms and 5.018 ms. The maximum responsivity and detectivity are 88.206 mA/W and 7.392 × 1013 Jones, respectively, increased by 359% and 6065% compared to the device before polarization. Thanks to excellent material stability, the device has a sensitive and uniform response to continuously incident UV light. In addition, by simulating flame generation, it has been proven that the device has brilliant performance in flame detection and can achieve remote monitoring and fire warning functions. These results can promote the potential application of self-powered UV photodetectors based on BaTiO3/GaN heterojunction in environmental monitoring and space communication fields.

Segmentation analysis of UAV images based on Unet deep learning algorithm

The continuous development of UAV technology provides us with more and higher quality data, in which the application of UAV image segmentation technology can help us better understand and process these data. Traditional image segmentation methods can no longer meet the needs of UAV image segmentation, so researchers have begun to explore the application of deep learning methods in UAV image segmentation.U-Net, as a classical deep learning model, is also widely used in UAV image segmentation.U-Net is characterized by two parts: encoder and decoder, which are used to extract the image features, and decoder is used to map the features back to the original image size. features to map back to the original image size. UAV image segmentation technology can be applied in agriculture, urban planning, environmental monitoring and other fields. In the field of agriculture, UAV image segmentation can help farmers better manage and monitor farmland to improve crop yield and quality. In the field of urban planning, UAV image segmentation can help urban planners better understand the development status of the city and provide a more scientific basis for urban planning. In the field of environmental monitoring, UAV image segmentation can help us better monitor the changes in the natural environment and provide more effective means for environmental protection. By setting the ratio of training set, validation set and test set as 6:2:2 and performing 100 rounds of training, the U-Net model shows good results in UAV image segmentation. The loss of the model gradually stabilizes at 0.2979 and the accuracy gradually converges to 90.34%. The test results show that the prediction results are very close to the true mask, indicating that the U-Net model can segment UAV images well. The application of UAV image segmentation technology can help us better understand and process the data acquired by UAV and provide more information and basis. In the future, with the continuous development of UAV technology, UAV image segmentation technology will also be more widely used. Through the application of UAV image segmentation technology, we will better understand and protect our environment, manage our farmland more efficiently, and plan our cities more scientifically.

Nanozyme sensor array based on Fe, Se co-doped carbon material for the discrimination of Sulfur-containing compounds

Developing methods for the accurate identification and analysis of sulfur-containing compounds (SCCs) is of great significance because of their essential roles in living organisms and the diagnosis of diseases. Herein, Se-doping improved oxidase-like activity of iron-based carbon material (Fe-Se/NC) was prepared and applied to construct a four-channel colorimetric sensor array for the detection and identification of SCCs (including biothiols and sulfur-containing metal salts). Fe-Se/NC can realize the chromogenic oxidation of 3,3′,5,5′-tetramethylbenzidine (TMB) by activating O2 without relying on H2O2, which can be inhibited by different SCCs to diverse degrees to produce different colorimetric response changes as “fingerprints” on the sensor array. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that nine kinds of SCCs could be well discriminated. The sensor array was also applied for the detection of SCCs with a linear range of 1–50 μM and a limit of detection of 0.07–0.2 μM. Moreover, colorimetric sensor array inspired by the different levels of SCCs in real samples were used to discriminate cancer cells and food samples, demonstrating its potential application in the field of disease diagnosis and food monitoring. Environmental ImplicationsIn this work, a four-channel colorimetric sensor array for accurate SCCs identification and detection was successfully constructed. The colorimetric sensor array inspired by the different levels of SCCs in real samples were also used to discriminate cancer cells and food samples. Therefore, this Fe-Se/NC based sensor array is expected to be applied in the field of environmental monitoring and environment related disease diagnosis.

Research on Data Fusion and Analysis Based on Artificial Intelligence Algorithm in Sensor Networks

This paper discusses the data fusion and analysis technology based on artificial intelligence algorithm in sensor networks. Firstly, the basic concept, composition and structure of sensor network are summarized, and its characteristics and challenges are analyzed. Then, the basic principle of data fusion technology and its application in sensor networks are introduced, and the basic concept, classification and application of artificial intelligence algorithm in data fusion are described in detail. On this basis, this paper constructs a data fusion framework based on artificial intelligence, and proposes a new data fusion method combining deep learning and machine learning algorithm. This method can effectively improve the accuracy and reliability of sensor data and reduce data redundancy and noise interference. In addition, the thesis also conducts in-depth research on the application scenarios of data fusion and analysis in sensor networks, including Environmental monitoring, intelligent transportation, medical health and other fields. The effectiveness and practicability of the proposed method are verified by experiments and case studies. Experimental results show that the data fusion technology based on artificial intelligence algorithm can significantly improve the data processing ability and analysis accuracy of sensor networks, and provide strong support for related applications. Finally, this paper summarizes the main conclusions of the study, and points out the shortcomings of current research and future prospects. In the future, the data fusion method based on artificial intelligence will be further optimized and improved, and its application in more fields will be expanded, which will provide more powerful support for the development and application of sensor network technology.

Hybrid cross-linking for the synthesis of highly tough fluorescent alginate fibers

The limited mechanical performance and functional constraints of alginate fibers significantly impede their development in the context of professional research. To address the aforementioned issues, this study initially utilized polyethyleneimine (PEI) and citric acid to synthesize carbon quantum dots (CD) with surface amino functionalization. Subsequently, in a sodium alginate (SA) solution, CD and vinyl silicon-based nanoparticles (VSNP) were co-introduced. Utilizing the abundant hydroxyl and carboxyl groups in polysaccharides, stable interactions were achieved with CD and VSNP. Incorporating a dynamic hydrogen-bonded crosslinking network among CD, VSNP and SA, the establishment of a calcium alginate (CA) ionically crosslinked network is facilitated via calcium-sodium ion exchange. Additionally, the interwoven and interlocked structure between the aforementioned dual networks was established. Employing a wet spinning process, the synergistic multiple dynamic energy dissipation mechanisms significantly enhance the mechanical performance. Fluorescent alginate fibers (CA/VSNP/CD) were prepared. The tensile strength was 3.81 cN/dtex, and the elongation at break was 13.16%. Compared to pure CA fibers, there were respective increases of 95.38% and 82.02%. Simultaneously, the fibers exhibit excitation-dependent emission characteristics. This work lays the foundation for potential applications of fluorescent alginate fibers in environmental monitoring, chemical sensors, dynamic anti-counterfeiting, and biomedical fields.

New azo dyes for detection of metallic impurities

In the world of molecular sensing, the ability to detect and differentiate between various analyte is a crucial aspect of scientific exploration. In this study, two novel azo dyes Dye-1 and Dye-2 that exhibit distinct sensing capabilities were synthesized and characterized. Dye-1 and Dye-2, each possess unique sensing capabilities. Dye-1 demonstrates an impressive affinity for the metal ion sensing and reveals differential colorimetric detection of metal ions based on the solvent system. Dye-2 on the other hand do not reveal significant color changes on addition of different metal ions, however it does reveal a selective and specific detection for dichromate (Cr2O72−) ions exclusively. The limit of detection (LOD) values obtained highlights the considerable sensitivity of Dye-1 and Dye-2 as a chemosensor. Finally, the utility of both the dyes for the detection of metallic impurities in real water samples has been demonstrated. The mechanism by which Dye-1 and Dye-2 binds to different metallic ions was also explored and studied through Density Functional Theory (DFT) computational modelling. This research contributes to the understanding of molecular sensing and offers potential applications in the field of analytical chemistry and environmental monitoring.

Quasicrystal Nanosheet/α-Fe2O3 Heterostructure-Based Low Power NO2 Sensors: Experimental and DFT Studies.

Industrial emissions, environmental monitoring, and medical fields have put forward huge demands for high-performance and low power consumption sensors. Two-dimensional quasicrystal (2D QC) nanosheets of metallic multicomponent Al70Co10Fe5Ni10Cu5 have emerged as a promising material for gas sensors due to their excellent catalytic and electronic properties. Herein, we demonstrate highly sensitive and selective NO2 sensors developed by low-cost and scalable fabrication techniques using 2D QC nanosheets and α-Fe2O3 nanoparticles. The sensitivity (ΔR/R%) of the optimal amount of 2D QC nanosheet-loaded α-Fe2O3 sensor was 32%, which is significantly larger about 3.5 times than bare α-Fe2O3 sensors for 1 ppm of NO2 at 150 °C operating temperature. The sensors exhibited p-type conduction, and resistance was reduced when exposed to NO2, an oxidizing gas. The enhanced sensing characteristics are a result of the formation of nanoheterojunctions between 2D QC and α-Fe2O3, which improved the charge transport and provided a large sensing signal. In addition, the heterojunction sensor demonstrated excellent NO2 selectivity over other oxidizing and reducing gases. Furthermore, density functional theory calculation examines the adsorption energy and charge transfer between NO2 molecules on the α-Fe2O3(110) and QC/α-Fe2O3(110) heterostructure surfaces, which coincides well with the experimental results.

Er3+/Yb3+/Ho3+ tri-doped no-core fiber temperature sensor based on fluorescence intensity ratio technology: towards high stability and accurate measurements.

In this paper, the green upconversion (UC) fluorescence emission from E r 3+/Y b 3+/H o 3+ tri-doped tellurite glass is investigated for temperature sensing. The doping of H o 3+ ions not only enhances the chance of energy level transition but also avoids the influence of the thermal effect caused by the proximity of 2 H 11/2 and 4 S 3/2 energy levels. The luminescence characteristics at different Y b 3+ and H o 3+ ion concentration doping molar ratios were investigated, and the strongest luminescence characteristics were exhibited when the Y b 3+ ion concentration was at 5mol% and H o 3+ at 0.2mol%. Based on this, a tri-doped T e O 2-Z n O-B i 2 O 3 (TZB) no-core fiber was fabricated and connected with multimode fibers (MMFs) to form a temperature sensor. The temperature sensing performance of the tri-doped TZB temperature sensor was evaluated in detail over the temperature range of 255-365K. The repeatability and stability of the temperature sensor was experimentally verified. The E r 3+/Y b 3+/H o 3+ tri-doped sensor can be used for noninvasive optical temperature sensing in the fields of environmental monitoring, biological sensing, and industrial process temperature control, etc.

Bifunctional MOF-Encapsulated Cobalt-Doped Carbon Dots Nanozyme-Powered Chemiluminescence/Fluorescence Dual-Mode Detection of Aflatoxin B1.

A novel bifunctional MOF-encapsulated cobalt-doped carbon dots nanozyme (Co-CD/PMOF) with excellent peroxidase-mimic catalytic activity and fluorescence property was synthesized and employed to fabricate a chemiluminescence/fluorescence (CL/FL) dual-mode immunosensor for AFB1 detection. Co-CD/PMOF could catalyze the luminol/H2O2 system to generate robust and long-lasting CL signals due to the slow diffusion effect and continuous generation of •OH, O2•-, and 1O2 species. Differing from traditional flash-type CL emissions, this glow-type CL emission is helpful to fabricate a sensitive and accurate CL sensing platform. Then the CL/FL dual-mode detection of AFB1 was developed using antibody-functionalized Co-CD/PMOF as the signal-amplifying nanoprobe. The CL mode assay based on indirect competitive immune principle was carried out on a chemiluminescence optical fiber platform, where the AFB1-OVA-functionalized optical fiber probe was employed for biorecognition, separation, and signal conducting. The AFB1 detection range and LOD were 0.63-69.36 ng/mL and 0.217 ng/mL, respectively. Using AFB1 antibody-functionalized immunomagnetic beads for capturing and separation, the FL mode detection of AFB1 was established based on the sandwich immune principle. A linear range of 0.54-51.91 ng/mL and a LOD of 0.027 ng/mL were obtained. This work designed a sensitive, rapid, and reliable nanozyme-powered dual-mode assay strategy and provided technical support in the field of environmental monitoring and food safety.

Simultaneous measurement of temperature, liquid level and axial strain based on torsional taper Mach-Zehnder interferometer with Vernier effect

A novel and simple highly-sensitive temperature, liquid level and axial strain sensor based on a torsional taper Mach-Zehnder interferometer (MZI) with Vernier effect is proposed. An interesting finding, which is the linear relationships between the wavelength shifts of resonance peaks (envelopes) and temperature, liquid level and axial strain surrounding the sensing fiber, is demonstrated experimentally. Another interesting feature is that the sensor with Vernier effect magnifies the temperature, liquid level and axial strain sensitivities by 11.3, 11.2 and 12.6 times, respectively. In the proposed method, the sensor is fabricated by cascading one polarization mode interferometer (PMI) as reference interferometer and one torsional taper MZI as sensing interferometer. The PMI is composed of polarization maintaining fiber (PMF), and cascaded MZI is composed of thin-core photosensitive fiber (TPSF). Combined with the characteristic of high sensitivity, the sensor can be widely used in the field of environmental and structural safety monitoring.