The utilization of reinforcement learning (RL) within the field of education holds the potential to bring about a significant shift in the way students approach and engage with learning and how teachers evaluate student progress. The use of RL in education allows for personalized and adaptive learning, where the difficulty level can be adjusted based on a student’s performance. As a result, this could result in heightened levels of motivation and engagement among students. The aim of this article is to investigate the applications and techniques of RL in education and determine its potential impact on enhancing educational outcomes. It compares the various policies induced by RL with baselines and identifies four distinct RL techniques: the Markov decision process, partially observable Markov decision process, deep RL network, and Markov chain, as well as their application in education. The main focus of the article is to identify best practices for incorporating RL into educational settings to achieve effective and rewarding outcomes. To accomplish this, the article thoroughly examines the existing literature on using RL in education and its potential to advance educational technology. This work provides a thorough analysis of the various techniques and applications of RL in education to answer questions related to the effectiveness of RL in education and its future prospects. The findings of this study will provide researchers with a benchmark to compare the usefulness and effectiveness of commonly employed RL algorithms and provide direction for future research in education.

Graphene field effect transistor (FET) biosensors have attracted huge attention in the point-of-care and accurate detection. With the recent spread of the new emerging severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the need for rapid, and accurate detection & screening tools is arising. Employing these easy-to-handle sensors can offer cheap, rapid, and accurate detection. Herein, we propose the design of a reduced graphene oxide (rGO) FET biosensor for the detection of SARS-CoV-2. The main objective of this work is to detect the SARS-CoV-2 spike protein antigen on spot selectively and rapidly. The sensor consists of rGO channel, a pair of golden electrodes, and a gate underneath the channel. The channel is functionalized with COVID-19 spike protein antibodies to achieve selectivity, and with metal nanoparticles (MNPs) such as copper and silver to enhance the bio-sensing performance. The designed sensor successfully detects the SARS-CoV-2 spike protein and shows singular electrical behavior for detection. The semi-empirical modeling approach combined with none-equilibrium Green’s function were used to study the electronic transport properties of the rGO-FET biosensor before and after the addition of the target molecules. The sensor’s selectivity is also tested against other viruses. This study provides a promising guide for future practical fabrication.

Biomolecular detection methods have evolved from simple chemical processes to laboratory sensors capable of acquiring accurate measurements of various biological components. Recently, silicon nanowire field-effect transistors (SiNW-FETs) have been drawing enormous interest due to their potential in the biomolecular sensing field. SiNW-FETs exhibit capabilities such as providing real-time, label-free, highly selective, and sensitive detection. It is highly critical to diagnose infectious diseases accurately to reduce the illness and death spread rate. In this work, a novel SiNW-FET sensor is designed using a semiempirical approach, and the electronic transport properties are studied to detect the COVID-19 spike protein. Various electronic transport properties such as transmission spectrum, conductance, and electronic current are investigated by a semiempirical modeling that is combined with a nonequilibrium Green’s function. Moreover, the developed sensor selectivity is tested by studying the electronic transport properties for other viruses including influenza, rotavirus, and HIV. The results indicate that SiNW-FET can be utilized for accurate COVID-19 identification with high sensitivity and selectivity.

Graphene has remarkable characteristics that make it a potential candidate for optoelectronics and electronics applications. Graphene is a sensitive material that reacts to any physical variation in its environment. Due to its extremely low intrinsic electrical noise, graphene can detect even a single molecule in its proximity. This feature makes graphene a potential candidate for identifying a wide range of organic and inorganic compounds. Graphene and its derivatives are considered one of the best materials to detect sugar molecules due to their electronic properties. Graphene has low intrinsic noise, making it an ideal membrane for detecting low concentrations of sugar molecules. In this work, a graphene nanoribbon field effect transistor (GNR-FET) is designed and utilized to identify sugar molecules such as fructose, xylose, and glucose. The variation in the current of the GNR-FET in the presence of each of the sugar molecules is utilized as the detection signal. The designed GNR-FET shows a clear change in the device density of states, transmission spectrum, and current in the presence of each of the sugar molecules. The simulated sensor is made of a pair of metallic zigzag graphene nanoribbons (ZGNR) joint via a channel of armchair graphene nanoribbon (AGNR) and a gate. The Quantumwise Atomistix Toolkit (ATK) is used to design and conduct the nanoscale simulations of the GNR-FET. Semi-empirical modeling, along with non-equilibrium Green’s functional theory (SE + NEGF), is used to develop and study the designed sensor. This article suggests that the designed GNR transistor has the potential to identify each of the sugar molecules in real time with high accuracy.

DNA sequencing techniques are critical in order to investigate genes’ functions. Obtaining fast, accurate, and affordable DNA bases detection makes it possible to acquire personalized medicine. In this article, a semi-empirical technique is used to calculate the electron transport characteristics of the developed z-shaped graphene device to detect the DNA bases. The z-shaped transistor consists of a pair of zigzag graphene nanoribbon (ZGNR) connected through an armchair graphene nanoribbon (AGNR) channel with a nanopore where the DNA nucleobases are positioned. Non-equilibrium Green’s function (NEGF) integrated with semi-empirical methodologies are employed to analyze the different electronic transport characteristics. The semi-empirical approach applied is an extension of the extended Hückel (EH) method integrated with self-consistent (SC) Hartree potential. By employing the NEGF+SC-EH, it is proved that each one of the four DNA nucleobases positioned within the nanopore, with the hydrogen passivated edge carbon atoms, results in a unique electrical signature. Both electrical current signal and transmission spectrum measurements of DNA nucleobases inside the device’s pore are studied for the different bases with modification of their orientation and lateral translation. Moreover, the electronic noise effect of various factors is studied. The sensor sensitivity is improved by using nitrogen instead of hydrogen to passivate the nanopore and by adding a dual gate to surround the central semiconducting channel of the z-shaped graphene nanoribbon.