COVID-19 Detection via Silicon Nanowire Field-Effect Transistor: Setup and Modeling of Its Function

COVID-19: An overview on possible transmission ways, sampling matrices and diagnosis

COVID-19 is an RNA virus belonging to the SARS family of viruses and includes a wide range of symptoms along with effects on other body organs in addition to the respiratory system. The high speed of transmission, severe complications, and high death rate caused scientists to focus on this disease. Today, many different investigation types are performed on COVID-19 from various points of view in the literature. This review summarizes most of them to provide a useful guideline for researchers in this field. After a general introduction, this review is divided into three parts. In the first one, various transmission ways COVID-19 are classified and explained in detail. The second part reviews the used biological samples for the detection of virus and the final section describes the various methods reported for the diagnosis of COVID-19 in various biological matrices.

Graphene-based biosensors for detecting coronavirus: a brief review

The coronavirus (SARS-CoV-2) disease has affected the globe with 770 437 327 confirmed cases, including about 6 956 900 deaths, according to the World Health Organization (WHO) as of September 2023. Hence, it is imperative to develop diagnostic technologies, such as a rapid cost-effective SARS-CoV-2 detection method. A typical biosensor enables biomolecule detection with an appropriate transducer by generating a measurable signal from the sample. Graphene can be employed as a component for ultrasensitive and selective biosensors based on its physical, optical, and electrochemical properties. Herein, we briefly review graphene-based electrochemical, field-effect transistor (FET), and surface plasmon biosensors for detecting the SARS-CoV-2 target. In addition, details on the surface modification, immobilization, sensitivity and limit of detection (LOD) of all three sensors with regard to SARS-CoV-2 were reported. Finally, the point-of-care (POC) detection of SARS-CoV-2 using a portable smartphone and a wearable watch is a current topic of interest.

Nanostructures for prevention, diagnosis, and treatment of viral respiratory infections: from influenza virus to SARS-CoV-2 variants

Viruses are a major cause of mortality and socio-economic downfall despite the plethora of biopharmaceuticals designed for their eradication. Conventional antiviral therapies are often ineffective. Live-attenuated vaccines can pose a safety risk due to the possibility of pathogen reversion, whereas inactivated viral vaccines and subunit vaccines do not generate robust and sustained immune responses. Recent studies have demonstrated the potential of strategies that combine nanotechnology concepts with the diagnosis, prevention, and treatment of viral infectious diseases. The present review provides a comprehensive introduction to the different strains of viruses involved in respiratory diseases and presents an overview of recent advances in the diagnosis and treatment of viral infections based on nanotechnology concepts and applications. Discussions in diagnostic/therapeutic nanotechnology-based approaches will be focused on H1N1 influenza, respiratory syncytial virus, human parainfluenza virus type 3 infections, as well as COVID-19 infections caused by the SARS-CoV-2 virus Delta variant and new emerging Omicron variant.

The design of a point of care FET biosensor to detect and screen COVID-19

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.

Graphene Nanoribbon Field Effect Transistor Simulations for the Detection of Sugar Molecules: Semi-Empirical Modeling

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.

Sugar Molecules Detection via C2N Transistor-Based Sensor: First Principles Modeling

Real-time detection of sugar molecules is critical for preventing and monitoring diabetes and for food quality evaluation. In this article, a field effect transistor (FET) based on two-dimensional nitrogenated holey graphene (C₂N) was designed, developed, and tested to identify the sugar molecules including xylose, fructose, and glucose. Both density functional theory and non-equilibrium Green's function (DFT + NEGF) were used to study the designed device. Several electronic characteristics were studied, including work function, density of states, electrical current, and transmission spectrum. The proposed sensor is made of a pair of gold electrodes joint through a channel of C₂N and a gate was placed underneath the channel. The C₂N monolayer distinctive characteristics are promising for glucose sensors to detect blood sugar and for sugar molecules sensors to evaluate food quality. The electronic transport characteristics of the sensor resulted in a unique signature for each of the sugar molecules. This proposed work suggests that the developed C₂N transistor-based sensor could detect sugar molecules with high accuracy.

Polyethylene Glycol Functionalized Silicon Nanowire Field-Effect Transistor Biosensor for Glucose Detection

Accurate monitoring of blood glucose levels is crucial for the diagnosis of diabetes patients. In this paper, we proposed a simple "mixed-catalyzer layer" modified silicon nanowire field-effect transistor biosensor that enabled direct detection of glucose with low-charge in high ionic strength solutions. A stable screening system was established to overcome Debye screening effect by forming a porous biopolymer layer with polyethylene glycol (PEG) modified on the surface of SiNW. The experimental results show that when the optimal ratio (APTMS:silane-PEG = 2:1) modified the surface of silicon nanowires, glucose oxidase can detect glucose in the concentration range of 10 nM to 10 mM. The sensitivity of the biosensor is calculated to be 0.47 μAcm^-2mM^-1, its fast response time not exceeding 8 s, and the detection limit is up to 10 nM. This glucose sensor has the advantages of high sensitivity, strong specificity and fast real-time response. Therefore, it has a potential clinical application prospect in disease diagnosis.