The overarching goal of our research is to manipulate the light-bio-matter interaction to obtain control over the functioning of living systems. Light can control cell activity, with high space and time resolution and a virtually infinite number of configurations, free from wiring constrains. We develop and study non-genetic cell opto-stimulation techniques based on artificial light actuators that establish functional abiotic-biotic interfaces able to transduce a light signal into a biological stimulus. This talk reports on the state of our research regarding organic bio interfaces for inducing light sensitivity in cells, both in vitro and in vivo. Light actuators comes in different shapes: planar patches, nanoparticle, intra-membrane probes. Their coupling mechanism is still far from being understood and attempts to shed light will be introduced. The research aims at a new technological platform for application in life enhancing technologies or new cyborg technologies. One of the most appealing application of this emerging technology is rescue vision in blind people.

Organic electrochemical transistors (OECTs) have been shown to be excellent building blocks in a variety of applications, from digital and neuromorphic electronics, to in-vivo and in-vitro biomedical devices. Owing to a peculiar switching mechanism based on a redox reaction, the ions of an electrolyte couple with the charge carriers of the active material and unique properties, absent in other types of thin-film transistors, emerge. One such feature is a pronounced switching hysteresis, the application of which is already well known for non-volatile memory elements, but that is not adequately described by any theoretical model so far. Using a solid-state system, we show the hysteresis to be the result of an underlying bistability, and we derive a thermodynamic framework from which its presence emerges naturally. We derive predictions about its dependencies and verify them experimentally by presenting the first systematic temperature dependencies of OECTs as well as that we use the insights to eliminate the hysteresis through deliberate material changes. The model also suggests an anti-Boltzmann dependence of the subthreshold swing under certain conditions, which we have verified experimentally. Finally, we take advantage of the bistability by implementing the OECT as a Schmitt trigger, thus realizing the functionality of a comparable multicomponent circuit through a single device. This work allows us to reinterpret existing data under a new light and paves the way for using OECTs in organic, neuromorphic applications.

The use of bioelectronic devices for acquiring biological information and delivering therapeutic interventions relies on direct contact with soft bio-tissues. To ensure high-quality signal transductions, the interfaces between bioelectronic devices and bio-tissues must combine signal amplification with stable and conformable contact. Semiconductor-based transistors (e.g., organic electrochemical transistors) have been developed as one of the most advanced technologies for high-performance bio-sensing. However, the rigid mechanical properties and the lack of tissue/skin adhesion from transistors largely prevent the formation of such intimate and long-term stable bio-interfaces. In this talk, I will introduce our material and device designs for introducing three highly important biomimetic properties onto transistor-based biosensors—stretchability, tissue-like softness, and bioadhesive properties. Our rationale designs from the material to the device level allow the realization of these properties with state-of-the-art electrical performance. I will also introduce the strategies and advantages of using these new biomimetic properties in bioelectrical and biochemical sensing.

Artificial neural networks (ANN), inspired by biological nervous systems, enable signal processing beyond the capabilities of von Neumann computer architectures. Through dynamically adapting the connectivity (synaptic weights) in individual devices and by applying learning algorithms ANNs can offer in memory and tensor computing capabilities. Yet, to fully unleash the potential of hardware ANNs there is still a need for neuromorphic device concepts, which properly emulate all necessary synaptic functions adequately and allow for an easy integration into large scale hardware ANNs. In this contribution we will demonstrate organic ionic/electronic as well as plasmonic/photonic neuromorphic device concepts using different types of hybrid material systems.

Active materials based on carbon offer a diverse platform to explore an array of different sensing modalities. The versatility of organic materials allows for functional sensors to be constructed on a wide array of flexible substrates, and the ‘tunability’ of the chromophores afforded by organic synthesis allow the generation of active materials sensitive to an array of analytes - further manipulation of both the active material and the form factor allow further optimization to enhance sensitivity to particular analytes of interest. Another advantage of organics is the variety of approaches that can be used to query the output of the sensor - ranging from complicated electronic measurements all the way down to simple color changes. In this talk, I will describe first the use of organic semiconductors in an array of field-effect transistor formats, describing how changes to both the active organic material and to the structure of the film can be optimized to enhance sensitivity for analytes varying from gamma radiation to gases such as nitrogen dioxide and ammonia. In some applications, low cost and simple readout become priorities, so I will end my talk with a discussion of leuco dye formulations designed to serve as simple environmental sensors for radiation.

This work suggests a design strategy for realizing under-display biometric imaging solutions which can recognize fingerprints and veins. Using a red-light source instead of an additional near-infrared light source, and a collimator with a microlens array directs the reflected light to the detector. Microlens array is optimized with ray-tracing simulation for signal-to-noise ratio and light harvesting performance. Organic image sensor with high pixel density and a large area is suggested for recognizing fingerprints and veins. Ray tracing simulation with designed line patterns confirmed that the integrated system is capable to recognize both fingerprints and veins.