Mengwei Wang

In natural aquatic environments, the existence of colored dissolved organic matter (CDOM), suspended particles, and colloids can cause scattering and reflection of light and even emit fluorescence itself. Such interference negatively impacts algal fluorescence, further making it unreliable to measure the algal concentration using three-dimensional excitation–emission matrix (3D-EEM) fluorescence spectroscopy. In this study, we proposed a novel algal fluorescence anti-interference network (AFAI-Net) based on a convolutional neural network. The main procedure of this model can be divided into two parts: (1) to quickly determine if there is an interference of CDOM or turbidity in the detected algal samples; (2) to correct the interfered samples and output the fluorescent components of the algae. We trained the model using the 3D-EEMs of pure algal samples (non-interfered) and mixed samples of algae and CDOM or turbidity (interfered); as a result, the well-trained model achieved a total classification accuracy of 96.82%, and the RMSE of CDOM and turbidity removal fitting effects were 0.2274 and 0.3423, respectively. Compared with the non-negative weighted least squares (NNLS) regression analysis method, using the CNN model for CDOM correction resulted in 13.11%, 0.65%, and 5.69% reductions in the average deviation rate for PD, PG, and CM, respectively. Furthermore, the spectra corrected by the model predicted algal densities that were closer to the true algal densities. This study provides a new way to remove non-algal factors that affect algal fluorescence spectra in water bodies, which is beneficial to monitoring eutrophication and red tide in aquatic systems.

We investigate a high-sensitivity surface plasmon resonance (SPR) biosensor consisting of a Au layer, four-layer MoS₂, and monolayer graphene. The numerical simulations, by the transfer matrix method (TMM), demonstrate the sensor has a maximum sensitivity of 282°/RIU, which is approximately 2 times greater than the conventional Au-based SPR sensor. The finite difference time domain (FDTD) indicates that the presence of MoS₂ film generates a strong surface electric field and enhances the sensitivity of the proposed SPR sensor. In addition, the influence of the number of MoS₂ layers on the sensitivity of the proposed sensor is investigated by simulations and experiments. In the experiment, MoS₂ and graphene films are transferred on the Au-based substrate by the PMMA-based wet transfer method, and the fabricated samples are characterized by Raman spectroscopy. Furthermore, the fabricated sensors with the Kretschmann configuration are used to detect okadaic acid (OA). The okadaic acid–bovine serum albumin bioconjugate (OA-BSA) is immobilized on the graphene layer of the sensors to develop a competitive inhibition immunoassay. The results show that the sensor has a very low limit of detection (LOD) of 1.18 ng/mL for OA, which is about 22.6 times lower than that of a conventional Au biosensor. We believe that such a high-sensitivity SPR biosensor has potential applications for clinical diagnosis and immunoassays.