A high-sensitivity surface plasmon resonance (SPR) sensor based on the coupling of Au grating and Au film is investigated through simulations and experiments. The SPR sensor is designed by using a hybrid method composed of genetic algorithm (GA) and rigorous coupled wave analysis (RCWA). The numerical results indicate the sensor has an angular sensitivity of 397.3°/RIU (refractive index unit), which is approximately 2.81 times higher than the conventional Au-based sensor and it is verified by experiments. Theoretical analysis, by finite-difference time-domain (FDTD) method, demonstrates the co-coupling between surface plasmon polaritons (SPPs) propagating on the surface of Au film and localized surface plasmons (LSPs) in the Au grating nanostructure, improving the sensitivity of the SPR sensor. According to the optimized structural parameters, the proposed sensor is fabricated using e-beam lithography and magnetron sputtering. In addition, the proposed sensor is very sensitive to the detection of small molecules. The limit of detection (LOD) for okadaic acid (OA) is 0.72 ng/mL based on an indirect competitive inhibition method, which is approximately 38 times lower than the conventional Au sensor. Such a high-sensitivity SPR biosensor has potential in the applications of immunoassays and clinical diagnosis.
In this paper, an algal identification and concentration determination method based on discrete excitation fluorescence spectra is proposed for online algae identification and concentration prediction. The discrete excitation fluorescence spectra of eight species of harmful algae from four algal categories were assessed. After determining typical excitation wavelengths according to the distribution of photosynthetic pigments and eliminating strongly correlated wavelengths by applying the hierarchical clustering, seven characteristic excitation wavelengths (405, 435, 470, 490, 535, 555, and 590 nm) were selected. By adding the ratios between feature points (435 and 470 nm, 470 and 490 nm, as well as 535 and 555 nm), standard feature spectra were established for classification. The classification accuracy in pure samples exceeded 95%, and that of dominant algae species in a mixed sample was 77.4%. Prediction of algae concentration was achieved by establishing linear regression models between fluorescence intensity at seven characteristic excitation wavelengths and concentrations. All models performed better at low concentrations, not exceeding the threshold concentration of red tide algae outbreak, which provides a proximate cell density of dominant algal species.
Detecting marine plankton by means of digital holographic microscopy (DHM) has been successfully deployed in recent decades; however, in most previous studies, the identification of the position, shape, and size of plankton has been neglected, which may negate some of the advantages of DHM. Therefore, the procedure of image fusion has been added between the reconstruction of initial holograms and the final identification, which could help present all the images of plankton clearly in a volume of seawater. A new image fusion method called digital holographic microscopy-fully convolutional networks (DHM-FCN) is proposed, which is based on the improved fully convolutional networks (FCN). The DHM-FCN model runs 20 times faster than traditional image fusion methods and suppresses the noise in the holograms. All plankton in a 2 mm thick water body could be clearly represented in the fusion image. The edges of the plankton in the DHM-FCN fusion image are continuous and clear without speckle noise inside. The neural network model, YOLOv4, for plankton identification and localization, was established. A mean average precision (mAP) of 97.69% was obtained for five species, Alexandrium tamarense, Chattonella marina, Mesodinium rubrum, Scrippsiella trochoidea, and Prorocentrum lima. The results of this study could provide a fast image fusion method and a visual method to detect organisms in water.
A new inspection and analysis platform based on two-electrode system was constructed.The two-electrode system based on minimizing polarizing current theory was realized by maximizing the ratio of the area of working electrode and auxiliary electrode,and hardware software were designed.Therefore the reference electrode can be abandoned,which overcomes the shortcomings of traditional three-electrode system while reproducing the function of electrochemical workstation.Research on Electric Conductivity(EC),pH and the concentrations of heavy metal ions of water had been carried out on this platform,and effective results had received which demonstrated the ability of multiple water quality parameters inspection.This platform can be used on solution properties analysis in laboratory or industry locale.
In this paper, we design a smart pH sensor using untreated platinum sheet based on chronopotentiometry. This novel pH sensor is very suitable for applications in the deep sea, highly polluted water, and other harsh environments, where maintenance is difficult. In order to verify the long-term monitoring stability of the pH sensor, 17-day monitoring experiments are conducted in river water. We draw some conclusions for the properties of the pH sensor. First, the pH values obtained from the positive current agreed well with the pH glass electrode, indicating that it is suitable for pH monitoring. Moreover, the deviation derived from hysteresis is small. Second, the pH values obtained from the negative current could not reflect the actual pH of river water in long-term measurement. There may be two reasons for this: the changing conductivity in the river water and the unstable composition of the platinum sheet. Third, the conductivity may have an obvious impact on the potential obtained from the negative current; the electrochemical reaction where Pt is oxidized to PtO may be influenced by the ionic strength of the solution. Therefore, the pH values obtained from the positive current is more suitable for long-term pH monitoring.
Abstract New methods for extracting vein features from finger vein image and generating templates for matching are proposed. In the algorithm for generating templates, we proposed a parameter-templates quality factor (TQF) - to measure the quality of generated templates. So that we can use fewer finger vein samples to generate templates that meet the quality requirement of identification. The recognition accuracy of using proposed methods of finger vein feature extraction and template generation strategy for identification is 97.14%.
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