Development of a PCB-based passive capacitive sensor for fluidic flow detection

A passive wireless sensor system integrated with capacitive fluidic flow detection is proposed and developed based on the printed circuit board (PCB) technique. The capacitive sensing structure consists of PCB-based electrodes enclosing an insulating pipe that contains the fluidic flow of interest. The conductivity of the fluidic flow and the appearance of foreign objects within the flow can be determined by analysing the resonant frequency of the detection path in the proposed system. Experimental results demonstrate that the resonant frequency increases according to the increase in electrical conductivity of the fluidic flow. In addition, the sensing performance is also confirmed by the detection of sizes and electrical conductivities of NaCl droplets passing through the detection zone. Furthermore, this work indirectly verifies the effectiveness and feasibility of the integration of passive wireless sensing technique into the fluidic flow detector by using the PCB fabrication technique and demonstrates great potential for use in various applications in biomedical and chemical fields, especially in biomedical applications

Nanoparticles as a control for cyanobacterial bloom

This study aims to investigate the toxicity of copper material synthesized by chemical reduction method and effects of environmental variables on growth of phytoplankton community (dominated by Microcystis genus) in the Tien eutrophic lake, Hanoi, Vietnam. The variables analyzed include: physical (pH and Turbidity), chemical (content of NH4+, PO43- and copper metal), biological (content of Chlorophyll-a, cell density). The characteristic of nanomaterial was confirmed by using UVvisible spectrophotometer, XRD, SEM and TEM methods. The CuNPs showed they spherical form and uniform size about 20-40 nm. The experimental results showed that the treated with CuNPs inhibition on growth against phytoplankton after 8 days. The cell density of phytoplankton community and Microcystis genus in samples exposure with CuNPs declined after 8 days from 647.037 and 467.037 down to 381.111 and 202.592, respectively.Mục đích của nghiên cứu này là khảo sát độc tính của vật liệu nano đồng được tổng hợp bằng phương pháp khử hóa học và ảnh hưởng của các yếu tố môi trường đến sinh trưởng và phát triển của quần xã thực vật nổi (chủ yếu là chi Microcystis) trong nước hồ Tiền phú dưỡng, tại Hà Nội, Việt Nam. Các thông số phân tích bao gồm: thủy lý (pH và độ đục), hóa học (hàm lượng amoni, photphat và hàm lượng đồng kim loại), sinh học (hàm lượng chất diệp lục, mật độ tế bào). Đặc trưng của vật liệu được xác định bằng các phương pháp quang phổ UV-VIS, XRD, SEM và TEM. Vật liệu nano đồng có dạng hình cầu, kích thước đồng nhất từ 20 đến 40 nm. Kết quả thử nghiệm sau 8 ngày cho thấy các mẫu có bổ sung vật liệu nano đồng ức chế sinh trưởng quần xã thực vật nổi ở nồng độ 1mg/l. Mật độ quần xã thực vật nổi và chi Microcystis trong mẫu xử lý với CuNPs đã giảm tương ứng sau 8 ngày từ 647.037 và 467.037 xuống còn 381.111 và 202.592

STT-BNN: A Novel Energy-efficient and Scalable BNN Accelerator Based on STT-MRAM

IEEE ISCAS 2021United State

Factors structuring phytoplankton community in a large tropical river: Case study in the red river (vietnam)

International audienceAlgal assemblages have been widely used as an ecological indicator of aquatic ecosystem health conditions because of their specific sensitivity to a wide variety of environmental conditions. In turbid rivers, as in other aquatic systems, phytoplankton structure plays an important role in structuring aquatic food webs. Worldwide, phytoplankton is less studied in turbid, large tropical rivers compared to temperate river systems. The present study aimed to describe the phytoplankton diversity and abundance in a turbid tropical river (the Red River, northern part of Vietnam from 20 degrees 00 to 25 degrees 30 North; from 100 degrees 00 to 107 degrees 10 East) and to determine the importance of a series of environmental variables in controlling the phytoplankton community composition. Phytoplankton community was composed of 169 phytoplankton taxa from six algal groups including Bacillariophyceae, Chlorophyceae, Cryptophyceae, Euglenophyceae, Dinophyceae and Cyanobacteria. Community composition varied both spatially and with season. Sixteen measurement environmental variables were used as input variables for a three-layer backpropagation neural network that was developed to predict the phytoplankton abundance. Phytoplankton abundance was successfully predicted using the tagsig transfer function and the Levenberg-Marquardt backpropagation algorithm. The network was trained to provide a good overall linear fit to the total data set with a slope (R) and mean square error (MSE) of 0.808 and 0.0107, respectively. The sensitivity analysis and neutral interpretation diagram revealed that total phosphorus (TP), flow discharge, water temperature and P-PO43- were the significant variables. The results showed that the developed ANN model was able to simulate phytoplankton abundance in the Red River. These findings can help for gaining insight into and the relationship between phytoplankton and environmental factors in this complex, turbid, tropical river