ABSTRACT
Early diagnosis of pathological markers can significantly shorten the rate of viral transmission, reduce the probability of infection, and improve the cure rate of diseases. Therefore, analytical techniques for identifying pathological markers and environmental toxicants have received considerable attention from researchers worldwide. However, the most popular techniques used in clinical settings involve expensive precision instruments and complex detection processes. Thus, a simpler, more efficient, rapid, and intelligent means of analysis must be urgently developed. Electrochemical biosensors have the advantages of simple processing, low cost, low sample preparation requirements, rapid analysis, easy miniaturization, and integration. Thus, they have become popular in extensive research. Machine learning is widely used in material-assisted synthesis, sensor design, and other fields owing to its powerful data analysis and simulation learning capabilities. In this study, a machine learning-assisted carbon black-graphene oxide conjugate polymer (CB-GO/CP) electrode, in conjunction with a flexible wearable device, is proposed for the smart portable detection of tyrosine (Tyr). Input feature value data are obtained for the artificial neural network (ANN) and support vector machines (SVM) model learning via multiple data collections in artificial urine and by recording the pH and temperature values. The results reveal that a machine-learning model that integrates multiple external factors is more accurate for the prediction of Tyr concentration.
ABSTRACT
In the background of the rapid development of artificial intelligence, big data, IoT, 5G/6G, and other technologies, electrochemical sensors pose higher requirements for high-throughput detection. In this study, we developed a workstation with up to 10 channels, which supports both parallel signal stimulation and online electrochemical analysis functions. The platform was wired to a highly integrated Bluetooth chip used for wireless data transmission and can be visualized on a smartphone. We used this electrochemical test platform with carbon-graphene oxide/screen-printed carbon electrodes (CB-GO/SPCE) for the online analysis of L-tyrosine (Tyr), and the electrochemical performance and stability of the electrodes were examined by differential pulse voltammetry (DPV). The CB-GO-based screen-printed array electrodes with a multichannel electrochemical platform for Tyr detection showed a low detection limit (20 µM), good interference immunity, and 10-day stability in the range of 20-200 µM. This convenient electrochemical analytical device enables high-throughput detection and has good economic benefits that can contribute to the improvement of the accuracy of electrochemical analysis and the popularization of electrochemical detection methods in a wide range of fields.
ABSTRACT
In recent years, a variety of film thickness measurement techniques for copper chemical mechanical planarization (CMP) are subsequently proposed. In this paper, the eddy-current technique is used. In the control system of the CMP tool developed in the State Key Laboratory of Tribology, there are in situ module and off-line module for measurement subsystem. The in situ module can get the thickness of copper film on wafer surface in real time, and accurately judge when the CMP process should stop. This is called end-point detection. The off-line module is used for multi-points measurement after CMP process, in order to know the thickness of remained copper film. The whole control system is structured with two levels, and the physical connection between the upper and the lower is achieved by the industrial Ethernet. The process flow includes calibration and measurement, and there are different algorithms for two modules. In the process of software development, C++ is chosen as the programming language, in combination with Qt OpenSource to design two modules' GUI and OPC technology to implement the communication between the two levels. In addition, the drawing function is developed relying on Matlab, enriching the software functions of the off-line module. The result shows that the control system is running stably after repeated tests and practical operations for a long time.
