Two-Dimensional Transition Metal Dichalcogenide Based Biosensors: From Fundamentals to Healthcare Applications.

There has been an exponential surge in reports on two-dimensional (2D) materials ever since the discovery of graphene in 2004. Transition metal dichalcogenides (TMDs) are a class of 2D materials where weak van der Waals force binds individual covalently bonded X-M-X layers (where M is the transition metal and X is the chalcogen), making layer-controlled synthesis possible. These individual building blocks (single-layer TMDs) transition from indirect to direct band gaps and have fascinating optical and electronic properties. Layer-dependent opto-electrical properties, along with the existence of finite band gaps, make single-layer TMDs superior to the well-known graphene that paves the way for their applications in many areas. Ultra-fast response, high on/off ratio, planar structure, low operational voltage, wafer scale synthesis capabilities, high surface-to-volume ratio, and compatibility with standard fabrication processes makes TMDs ideal candidates to replace conventional semiconductors, such as silicon, etc., in the new-age electrical, electronic, and opto-electronic devices. Besides, TMDs can be potentially utilized in single molecular sensing for early detection of different biomarkers, gas sensors, photodetector, and catalytic applications. The impact of COVID-19 has given rise to an upsurge in demand for biosensors with real-time detection capabilities. TMDs as active or supporting biosensing elements exhibit potential for real-time detection of single biomarkers and, hence, show promise in the development of point-of-care healthcare devices. In this review, we provide a historical survey of 2D TMD-based biosensors for the detection of bio analytes ranging from bacteria, viruses, and whole cells to molecular biomarkers via optical, electronic, and electrochemical sensing mechanisms. Current approaches and the latest developments in the study of healthcare devices using 2D TMDs are discussed. Additionally, this review presents an overview of the challenges in the area and discusses the future perspective of 2D TMDs in the field of biosensing for healthcare devices.

Performance of Machine Learning Techniques Before and After COVID-19 on Indian Foreign Exchange Rate

The FOREX marketplace has seen sudden boom during last couple of decades. The changes carry out a critical function in balancing the dynamics of the marketplace. As a result, the correct prediction of the change price is a critical aspect for the fulfillment of many companies and fund managers. In-spite of the reality, the marketplace is famous for its flightiness and volatility;there exists groups like agencies, banks, and pandemic for awaiting change by several techniques. The goal of this article is to locate and advocate a neural community version to forecast exchange rate to the United States dollar against Indian rupees. In this article, we have analyzed the performance of different machine learning techniques during COVID-19 pandemic situation. This is further extended to find the best model to our purpose. In this paper, we implemented three different types of techniques to predict the foreign exchange rate of US dollar against the Indian rupees with high accuracy rate, before and after the COVID-19 pandemic. The three types of neural network models implemented in this article are artificial neural network (ANN), long short-term memory network (LSTM), and gated recurring units (GRU). The results from the above three models are compared so as to find out which model performs the best as compared to other models, before and after the COVID-19 pandemic. From the empirical analysis of all the models, we concluded that GRU outperformed both ANN and LSTM. We have five sections in this article. Section 41.1 briefly describes about prediction of foreign exchange rate. In Sect. 41.2, we have discussed the methods used in this article for the prediction of foreign exchange rate. Data collection and experimental results have been discussed in Sects. 41.3 and 41.4. Finally, in Sect. 41.5, we have given the conclusion and future scope of this experimental article. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.