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This perspective offers insights from discussions conducted during the Telluride Science meeting on organic mixed ionic and electronic conductors, outlining the challenges associated with understanding the behavior of this intriguing materials class. 

The ability to control the charge density of organic mixed ionic electronic conductors (OMIECs) via reactions with redox-active analytes has enabled applications as electrochemical redox sensors. Their charge density-dependent conductivity can additionally be tuned via charge injection from electrodes, for instance in organic electrochemical transistors (OECTs), where volumetric charging of the OMIEC channel enables excellent transconductance and amplification of low potentials. Recent efforts have combined the chemical detection with the transistor function of OECTs to achieve compact electrochemical sensors. However, these sensors often fall short of the expected amplification performance of OECTs. Here, we investigate the operation mechanism of various OECT architectures to deduce the design principles required to achieve reliable chemical detection and signal amplification. By utilizing a non-polarizable gate electrode and conducting the chemical reaction in a compartment separate from the OECT, the recently developed Reaction Cell OECT achieves reliable modulation of the OECT channel's charge density. This work demonstrates that systematic and rational design of OECT chemical sensors requires understanding the electrochemical processes that result in changes in the potential (charge density) of the channel, the underlying phenomenon behind amplification. 

Neuromorphic computers could overcome efficiency bottlenecks inherent to conventional computing through parallel programming and readout of artificial neural network weights in a crossbar memory array. However, selective and linear weight updates and <10-nanoampere read currents are required for learning that surpasses conventional computing efficiency. We introduce an ionic floating-gate memory array based on a polymer redox transistor connected to a conductive-bridge memory (CBM). Selective and linear programming of a redox transistor array is executed in parallel by overcoming the bridging threshold voltage of the CBMs. Synaptic weight readout with currents <10 nanoamperes is achieved by diluting the conductive polymer with an insulator to decrease the conductance. The redox transistors endure >1 billion write-read operations and support >1-megahertz write-read frequencies. 

Abstract Recent breakthroughs in artificial neural networks (ANNs) have spurred interest in efficient computational paradigms where the energy and time costs for training and inference are reduced. One promising contender for efficient ANN implementation is crossbar arrays of resistive memory elements that emulate the synaptic strength between neurons within the ANN. Organic nonvolatile redox memory has recently been demonstrated as a promising device for neuromorphic computing, offering a continuous range of linearly programmable resistance states and tunable electronic and electrochemical properties, opening a path toward massively parallel and energy efficient ANN implementation. However, one of the key issues with implementations relying on electrochemical gating of organic materials is the state‐retention time and device stability. Here, revealed are the mechanisms leading to state loss and cycling instability in redox‐gated neuromorphic devices: parasitic redox reactions and out‐diffusion of reducing additives. The results of this study are used to design an encapsulation structure which shows an order of magnitude improvement in state retention and cycling stability for poly(3,4‐ethylenedioxythiophene)/polyethyleneimine:poly(styrene sulfonate) devices by tuning the concentration of additives, implementing a solid‐state electrolyte, and encapsulating devices in an inert environment. Finally, a comparison is made between programming range and state retention to optimize device operation.