Rapid, Sensitive, Label-Free Electrical Detection of SARS-CoV-2 in Nasal Swab Samples

Deep Learning-Based Kinetic Analysis in Paper-Based Analytical Cartridges Integrated with Field-Effect Transistors

This study explores the fusion of a field-effect transistor (FET), a paper-based analytical cartridge, and the computational power of deep learning (DL) for quantitative biosensing via kinetic analyses. The FET sensors address the low sensitivity challenge observed in paper analytical devices, enabling electrical measurements with kinetic data. The paper-based cartridge eliminates the need for surface chemistry required in FET sensors, ensuring economical operation (cost < $0.15/test). The DL analysis mitigates chronic challenges of FET biosensors such as sample matrix interference, by leveraging kinetic data from target-specific bioreactions. In our proof-of-concept demonstration, our DL-based analyses showcased a coefficient of variation of <6.46% and a decent concentration measurement correlation with an r2 value of >0.976 for cholesterol testing when blindly compared to results obtained from a CLIA-certified clinical laboratory. These integrated technologies have the potential to advance FET-based biosensors, potentially transforming point-of-care diagnostics and at-home testing through enhanced accessibility, ease-of-use, and accuracy.

Reduced graphene oxide electrodes meet lateral flow assays: A promising path to advanced point-of-care diagnostics

Research in electrochemical detection in lateral flow assays (LFAs) has gained significant momentum in recent years. The primary impetus for this surge in interest is the pursuit of achieving lower limits of detection, especially given that LFAs are the most widely employed point-of-care biosensors. Conventionally, the strategy for merging electrochemistry and LFAs has centered on the superposition of screen-printed electrodes onto nitrocellulose substrates during LFA fabrication. Nevertheless, this approach poses substantial limitations regarding scalability. In response, we have developed a novel method for the complete integration of reduced graphene oxide (rGO) electrodes into LFA strips. We employed a CO2 laser to concurrently reduce graphene oxide and pattern nitrocellulose, exposing its backing to create connection sites impervious to sample leakage. Subsequently, rGO and nitrocellulose were juxtaposed and introduced into a roll-to-roll system using a wax printer. The exerted pressure facilitated the transfer of rGO onto the nitrocellulose. We systematically evaluated several electrochemical strategies to harness the synergy between rGO and LFAs. While certain challenges persist, our rGO transfer technology presents compelling potential for setting a new standard in electrochemical LFA fabrication.

Enhancing Electrochemical Sensing through Molecular Engineering of Reduced Graphene Oxide-Solution Interfaces and Remote Floating-Gate FET Analysis

Two-dimensional nanomaterials such as reduced graphene oxide (rGO) have captured significant attention in the realm of field-effect transistor (FET) sensors due to their inherent high sensitivity and cost-effective manufacturing. Despite their attraction, a comprehensive understanding of rGO-solution interfaces (specifically, electrochemical interfacial properties influenced by linker molecules and surface chemistry) remains challenging, given the limited capability of analytical tools to directly measure intricate solution interface properties. In this study, we introduce an analytical tool designed to directly measure the surface charge density of the rGO-solution interface leveraging the remote floating-gate FET (RFGFET) platform. Our methodology involves characterizing the electrochemical properties of rGO, which are influenced by adhesion layers between SiO2 and rGO, such as (3-aminopropyl)trimethoxysilane (APTMS) and hexamethyldisilazane (HMDS). The hydrophilic nature of APTMS facilitates the acceptance of oxygen-rich rGO, resulting in a noteworthy pH sensitivity of 56.8 mV/pH at the rGO-solution interface. Conversely, hydrophobic HMDS significantly suppresses the pH sensitivity from the rGO-solution interface, attributed to the graphitic carbon-rich surface of rGO. Consequently, the carbon-rich surface facilitates a denser arrangement of 1-pyrenebutyric acid N-hydroxysuccinimide ester linkers for functionalizing capturing probes on rGO, resulting in an enhanced sensitivity of lead ions by 32% in our proof-of-concept test.

Graphene transistor-based biosensors for rapid detection of SARS-CoV-2

Field-effect transistor (FET) biosensors use FETs to detect changes in the amount of electrical charge caused by biomolecules like antigens and antibodies. COVID-19 can be detected by employing these biosensors by immobilising bio-receptor molecules that bind to the SARS-CoV-2 virus on the FET channel surface and subsequent monitoring of the changes in the current triggered by the virus. Graphene Field-effect Transistor (GFET)-based biosensors utilise graphene, a two-dimensional material with high electrical conductivity, as the sensing element. These biosensors can rapidly detect several biomolecules including the SARS-CoV-2 virus, which is responsible for COVID-19. GFETs are ideal for real-time infectious illness diagnosis due to their great sensitivity and specificity. These graphene transistor-based biosensors could revolutionise clinical diagnostics by generating fast, accurate data that could aid pandemic management. GFETs can also be integrated into point-of-care (POC) diagnostic equipment. Recent advances in GFET-type biosensors for SARS-CoV-2 detection are discussed here, along with their associated challenges and future scope.