Suppression of Ionic Doping by Molecular Dopants in Conjugated Polymers for Improving Specificity and Sensitivity in Biosensing Applications

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.

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.

Selective Detection of Functionalized Carbon Particles based on Polymer Semiconducting and Conducting Devices as Potential Particulate Matter Sensors

This paper reports a new mechanism for particulate matter detection and identification. Three types of carbon particles are synthesized with different functional groups to mimic the real particulates in atmospheric aerosol. After exposing polymer-based organic devices in organic field effect transistor (OFET) architectures to the particle mist, the sensitivity and selectivity of the detection of different types of particles are shown by the current changes extracted from the transfer curves. The results indicate that the sensitivity of the devices is related to the structure and functional groups of the organic semiconducting layers, as well as the morphology. The predominant response is simulated by a model that yielded values of charge carrier density increase and charge carriers delivered per unit mass of particles. The research points out that polymer semiconductor devices have the ability to selectively detect particles with multiple functional groups, which reveals a future direction for selective detection of particulate matter.

Remote Floating-Gate Field-Effect Transistor with 2-Dimensional Reduced Graphene Oxide Sensing Layer for Reliable Detection of SARS-CoV-2 Spike Proteins

Despite intensive research of nanomaterials-based field-effect transistors (FETs) as a rapid diagnostic tool, it remains to be seen for FET sensors to be used for clinical applications due to a lack of stability, reliability, reproducibility, and scalability for mass production. Herein, we propose a remote floating-gate (RFG) FET configuration to eliminate device-to-device variations of two-dimensional reduced graphene oxide (rGO) sensing surfaces and most of the instability at the solution interface. Also, critical mechanistic factors behind the electrochemical instability of rGO such as severe drift and hysteresis were identified through extensive studies on rGO-solution interfaces varied by rGO thickness, coverage, and reduction temperature. rGO surfaces in our RFGFET structure displayed a Nernstian response of 54 mV/pH (from pH 2 to 11) with a 90% yield (9 samples out of total 10), coefficient of variation (CV) < 3%, and a low drift rate of 2%, all of which were calculated from the absolute measurement values. As proof-of-concept, we demonstrated highly reliable, reproducible, and label-free detection of spike proteins of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a saliva-relevant media with concentrations ranging from 500 fg/mL to 5 μg/mL, with an R² value of 0.984 and CV < 3%, and a guaranteed limit of detection at a few pg/mL. Taken together, this new platform may have an immense effect on positioning FET bioelectronics in a clinical setting for detecting SARS-CoV-2.