Multiscale Modeling of Organic Electronic Biosensor Response

Abstract

[eng] There have been significant advances in organic bioelectronic devices in recent years. These devices are capable of stimulating excitable cells and can generate data to facilitate disease diagnostics and monitoring. Electrolyte-gated organic field-effect transistors (EGOFETs) are powerful organic bioelectronic devices. EGOFETs are a group of thin-film transistors used as the sensing units within organic bioelectronic devices due to their ability to strongly amplify the signal and natural biocompatibility (Macchia et al., 2019). EGOFETs can detect minor voltage variations of electrically excitable cells or when analyzing biomarkers (Kyndiah et al., 2020). Organic semiconductors offer various advantages over inorganic ones, such as low-cost production, flexibility, printability as well as allowing easy integration into sensing devices or textiles (Torricelli et al., 2021). Due to the lack of specific physical-mathematical modeling of EGOFETs, they are often approximated using ideal field-effect transistor (FET) models. Whilst these models can be useful, they are not capable of accommodating ionic diffusion effects generated by nanoscale variations at the electrolyte/semiconductor and electrolyte/gate interfaces within EGOFETs. This thesis presents the physical modeling of EGOFETs to provide a deeper physical understanding of these devices. We show the changes in the macroscale current correlated to the nanoscale conductivity when changing the device geometries. Further, we observe the voltage shifts due to ionic concentrations and evaluate the role of interfacial layers and fixed charges at the gate electrode. We address these problems with finite-element models coupling the device physics of the electrolyte and the semiconductor. Different levels of complexity of the models have been considered. The simpler Helmholtz model, where the electrolyte is mimicked as a Helmholtz capacitance, was selected initially. Using this, we determined that many of the transfer and output current-voltage curves of EGOFETs could be reproduced. This enabled the identification of local conductivity changes in the different operating regimes. We subsequently expanded the physical model by incorporating the electrolyte's ionic diffusive effects and compact interfacial layers' presence using the NPP framework. Initially, a one-dimensional capacitor structure model was used to gain fast results without neglecting the physics of the device. This model demonstrated the change in device characteristics following the addition of biorecognition layers to the gate electrode for biosensing applications. Developing this further, we considered the NPP model in two-dimensional structures, which allowed investigating changes to devise geometry, including channel and gate length in the NPP framework. This provides a deep insight into the voltage and charge density distributions to reveal the formation of space charge layers, including accumulation and ionic diffusive layers. The potential profiles over semiconductors and electrolytes demonstrate the differences in charge accumulation for gate modifications with self-assembled monolayers, ion concentrations, and material parameters. We correlate the charge accumulation along the conductive channel with the distribution of ions. The results of these studies allowed us to provide further explanations of the behavior of EGOFETs and have opened the door to a rational design and characterization of the devices for future biosensing applications. Kyndiah, A. et al. (2020) “Bioelectronic Recordings of Cardiomyocytes with Accumulation Mode Electrolyte Gated Organic Field Effect Transistors,” Biosensors and Bioelectronics, 150, p. 111844. Available at: https://doi.org/10.1016/j.bios.2019.111844. Macchia, E. et al. (2019) Organic Bioelectronic Transistors: From Fundamental Investigation of Bio-Interfaces to Highly Performing Biosensors. Available at: https://doi.org/10.21741/9781644900376-1. Torricelli, F. et al. (2021) “Electrolyte-gated transistors for enhanced performance bioelectronics,” Nature Reviews Methods Primers 2021 1:1, 1(1), pp. 1–24. Available at: https://doi.org/10.1038/S43586-021-00065-8.

[spa] En los últimos años se han generado avances importantes en los dispositivos bioelectrónicos orgánicos. Estos dispositivos son capaces de estimular células excitables y pueden generar datos que faciliten el diagnóstico y el seguimiento de enfermedades. Los transistores orgánicos de efecto de campo activados por electrolitos (EGOFET) son un grupo de transistores de capa fina que se utilizan como unidades de detección dentro de los dispositivos bioelectrónicos orgánicos debido a su capacidad de amplificar fuertemente las señales y a su biocompatibilidad natural. Los EGOFET pueden detectar pequeñas variaciones de voltaje de las células eléctricamente excitables o al analizar biomarcadores. Debido a la falta de una modelización físico-matemática específica de los EGOFET, a menudo éstos se modelizan utilizando modelos de transistores de efecto de campo ideales. Aunque estos modelos pueden ser útiles, no son capaces de incluir los efectos de difusión iónica que tienen lugar a la nanoescala en las interfaces electrolito/semiconductor y electrolito/puerta dentro de los EGOFET. Esta tesis presenta el modelado físico de los EGOFETs para proporcionar una comprensión física más profunda de estos dispositivos. Abordamos estos problemas con modelos de elementos finitos que acoplan la física del dispositivo del electrolito y la del semiconductor. Se han considerado diferentes niveles de complejidad de los modelos. Inicialmente se seleccionó el modelo de Helmholtz, más sencillo, en el que el electrolito se modela como una capacitancia de Helmholtz. Posteriormente, ampliamos el modelo físico incorporando los efectos iónicos difusivos del electrolito y la presencia de capas interfaciales compactas utilizando el marco de la teoria de Nernst-Planck-Poisson. Un modelo unidimensional demostró el cambio en las características del dispositivo tras la adición de capas de biorreconocimiento al electrodo de puerta para aplicaciones de biosensado. Desarrollando esto aún más, consideramos el modelo NPP en estructuras bidimensionales, lo que permitió investigar los efectos producidos por cambios en la geometría del dispositivo, incluyendo la longitud del canal y de la puerta.