Detection of DNA Bases via Field Effect Transistor of Graphene Nanoribbon with a Nanopore: Semi-empirical Modeling

Ultra-Scaled Si Nanowire Biosensors for Single DNA Molecule Detection

In this study, we use NEGF quantum transport simulations to study the fundamental detection limit of ultra-scaled Si nanowire FET (NWT) biosensors. A N-doped NWT is found to be more sensitive for negatively charged analytes as explained by the nature of the detection mechanism. Our results predict threshold voltage shifts due to a single-charge analyte of tens to hundreds of mV in air or low-ionic solutions. However, with typical ionic solutions and SAM conditions, the sensitivity rapidly drops to the mV/q range. Our results are then extended to the detection of a single 20-base-long DNA molecule in solution. The impact of front- and/or back-gate biasing on the sensitivity and limit of detection is studied and a signal-to-noise ratio of 10 is predicted. Opportunities and challenges to reach down to single-analyte detection in such systems are also discussed, including the ionic and oxide-solution interface-charge screening and ways to recover unscreened sensitivities.

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.

Applications of graphene-based electrochemical and optical biosensors in early detection of cancer biomarkers

Graphene is a one-atom-thick carbon compound, which holds promises for detecting cancer biomarkers along with its derivatives. The atom-wide graphene layer is ideal for cancer biomarker detection due to its unique physicochemical properties like increased electrical and thermal conductivity, optical transparency, and enhanced chemical and mechanical strength. The scientific aim of any biosensor is to create a smaller and portable point of care device for easy and early cancer detection; graphene is able to live up to that. Apart from tumour detection, graphene-based biosensors can diagnose many diseases, their biomarkers, and pathogens. Many existing remarkable pieces of research have proven the candidacy of nanoparticles in most cancer biomarkers detection. This article discusses the effectiveness of graphene-based biosensors in different cancer biomarker detection. This article provides a detailed review of graphene and its derivatives that can be used to detect cancer biomarkers with high specificity, sensitivity, and selectivity. We have highlighted the synthesis procedures of graphene and its products and also discussed their significant properties. Furthermore, we provided a detailed overview of the recent studies on cancer biomarker detection using graphene-based biosensors. The different paths to create and modify graphene surfaces for sensory applications have also been highlighted in each section. Finally, we concluded the review by discussing the existing challenges of these biosensors and also highlighted the steps that can be taken to overcome them.