Utilizing the electrochemical properties of organic mixed conductors for bioelectronic devices
Keywords: Materials Science and Engineering.

Organic mixed ionic-electronic conductors (OMIECs) have risen as a promising material choice for bioelectronic and biochemical devices due to their low impedance, soft mechanical properties, water stability, and ability to transduce ionic signals to electronic currents. These promising characteristics have led to widespread utilization of OMIECs for bioelectronic devices such as biosensors, neural interfaces, and flexible printed circuits. The bulk of the work in this field has been focused on using the polymer blend poly(ethylene dioxythiophene)-poly(styrene sulfonate) (PEDOT:PSS) as the OMIEC material, with a common goal of improving the characteristics of one type of device in particular called the organic electrochemical transistor (OECT). While OECTs are promising for bioelectronics, the unique properties of OMIECs extends beyond this one device and have until now been left unexploited. In this dissertation, I report my work on using and optimizing PEDOT:PSS for a wide range of bioelectronic devices including tuning OECTs for new applications, high-performance organic neuromorphic devices, and interfacing OMIEC-based electrodes with living cells. In the first section of my dissertation, I will describe how organic mixed conductors and interfaces can be modified for unique applications. First, I will describe how chemical additives can be used to tune the thresholding characteristics of OECTs to fabricate enhancement-mode electrochemical transistors. The additives are introduced in solution and result in nearly identical device performance metrics when compared to pristine PEDOT:PSS, offering a simple method for tuning the threshold voltage of OECTs. Then, I will describe how ion-selective membranes can be introduced to OECTs to engineer ion sensors with high gain and selectivity for sensing in human sweat. Through the integration of a protective microcapillary sweat acquisition later, the ion-selective OECT is also used as a wearable sensor. With this wearable sensor, I show the recording of both ammonium and calcium ion concentration in sweat for three healthy subjects. In the next section, I will describe how OMIECs can be utilized as neuromorphic devices to accelerate artificial neural network algorithms (ANNs). First, I will describe how chemical additives can be introduced in vapor phase to help stabilize the individual resistance states of organic neuromorphic devices. I show that parasitic reactions, primarily with environmental oxygen, can result in short lived memory states and how this can be mitigated by introducing reducing amines and encapsulating the device. Next, I will introduce a mathematical model which describes the switching response of a three-terminal neuromorphic device. This model is then utilized to choose the device geometry and programming conditions to achieve nearly ideal (fast, linear switching) performance. At the end of the section, I will show how the improved neuromorphic devices can be integrated with volatile filament forming access devices to make a fully parallel programmable array. I will also show how the device can be tuned to achieve a high resistance (10 M-Ohms) and long endurance (10^9 switching events), enabling scalable neuromorphic arrays. In the last section, I will describe how organic mixed conductors can be used to interface with in vitro cell cultures. First, I will describe how micron-sized features can be introduced to the surface of OMIEC electrodes to enhance the adhesion of cells to the electrode surface. By utilizing a femtosecond laser to introduce these topological features, we ensure no Joule heating of the organic electrode during the patterning process, leaving the polymer electrode intact. Finally, I will show how the biocompatibility and chemical sensitivity of organic neuromorphic devices enables direct coupling with biological cells to form a biohybrid synapse controlled by neurotransmitter activity. By directly integrating brain cells with the neuromorphic device, I show how a neuromorphic device can directly detect and respond to dopamine vesicle release, constituting a bioinspired learning rule