Search

The first Telluride Science meeting (formerly TSRC) on organic mixed ionic and electronic conductors (OMIECs), Oct 3-7, 2022, brought together researchers across the field to understand the fundamental processes and identify out-standing questions related to this exciting class of materials. OMIECs are organic materials that promote the transport of mobile electronic charge carriers while simultaneously supporting ionic transport and ionic-electronic coupling. These properties open up broad areas of applications from energy to bioelectronics. Devices include batteries, supercapacitors, actuators, electrochromic displays, and organic electrochemical transistors (OECTs). They possess the key strengths of traditional organic electronic materials, such as synthetic tunability and low-temperature processing. Despite the recent advances in devices and applications achieved with such materials, many challenges and gaps in understanding remain. These topics hold the key to designing next-generation materials and devices that continue to push the limits of performance and stability and facilitate novel functionality. This perspective aims to summarize the current understanding, conversations, and debates that made this TSRC particularly engaging, enabling new directions and searching for missing pieces of the OMIEC puzzle. 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., Funding Agencies|The authors and attendees thank the Telluride Science organization for their hospitality and support. Further sponsorship was provided by the Journal of Materials Chemistry C. The full list of TS workshop attendees was: the authors of this perspective (lis; Journal of Materials Chemistry C

Efficient transport of both ionic and electronic charges in conjugated polymers (CPs) has enabled a wide range of novel electrochemical devices spanning applications from energy storage to bioelectronic devices. In this Perspective, we provide an overview of the fundamental physical processes which underlie the operation of mixed conducting polymer (MCP) devices. While charge injection and transport have been studied extensively in both ionic and electronic conductors, translating these principles to mixed conducting systems proves challenging due to the complex relationships among the individual materials properties. We break down the process of electrochemical (de)doping, the basic feature exploited in mixed conducting devices, into its key steps, highlighting recent advances in the study of these physical processes in the context of MCPs. Furthermore, we identify remaining challenges in further extending fundamental understanding of MCP-based device operation. Ultimately, a deeper understanding of the elementary processes governing operation in MCPs will drive the advancement in both materials design and device performance., Funding Agencies|European Union [101022365]; Knut and Alice Wallenberg Foundation; Wallenberg Wood Science Center; Swedish Government Strategic Research Area in Materials Science on Advanced Functional Materials at Linkoping University [2009-00971]; Swedish Foundation for Strategic Research; H2020-EU-FET Open MITICS [964677]

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 1018 calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

Modern computation based on von Neumann architecture is now a mature cutting-edge science. In the von Neumann architecture, processing and memory units are implemented as separate blocks interchanging data intensively and continuously. This data transfer is responsible for a large part of the power consumption. The next generation computer technology is expected to solve problems at the exascale with 10$^{18}$ calculations each second. Even though these future computers will be incredibly powerful, if they are based on von Neumann type architectures, they will consume between 20 and 30 megawatts of power and will not have intrinsic physically built-in capabilities to learn or deal with complex data as our brain does. These needs can be addressed by neuromorphic computing systems which are inspired by the biological concepts of the human brain. This new generation of computers has the potential to be used for the storage and processing of large amounts of digital information with much lower power consumption than conventional processors. Among their potential future applications, an important niche is moving the control from data centers to edge devices. The aim of this roadmap is to present a snapshot of the present state of neuromorphic technology and provide an opinion on the challenges and opportunities that the future holds in the major areas of neuromorphic technology, namely materials, devices, neuromorphic circuits, neuromorphic algorithms, applications, and ethics. The roadmap is a collection of perspectives where leading researchers in the neuromorphic community provide their own view about the current state and the future challenges for each research area. We hope that this roadmap will be a useful resource by providing a concise yet comprehensive introduction to readers outside this field, for those who are just entering the field, as well as providing future perspectives for those who are well established in the neuromorphic computing community.

In living organisms, sensory and motor processes are distributed, locally merged, and capable of forming dynamic sensorimotor associations. We introduce a simple and efficient organic neuromorphic circuit for local sensorimotor merging and processing on a robot that is placed in a maze. While the robot is exposed to external environmental stimuli, visuomotor associations are formed on the adaptable neuromorphic circuit. With this on-chip sensorimotor integration, the robot learns to follow a path to the exit of a maze, while being guided by visually indicated paths. The ease of processability of organic neuromorphic electronics and their unconventional form factors, in combination with education-purpose robotics, showcase a promising approach of an affordable, versatile, and readily accessible platform for exploring, designing, and evaluating behavioral intelligence through decentralized sensorimotor integration.

Brain-inspired computing paradigms have led to substantial advances in the automation of visual and linguistic tasks by emulating the distributed information processing of biological systems1. The similarity between artificial neural networks (ANNs) and biological systems has inspired ANN implementation in biomedical interfaces including prosthetics2 and brain-machine interfaces3. While promising, these implementations rely on software to run ANN algorithms. Ultimately, it is desirable to build hardware ANNs4,5 that can both directly interface with living tissue and adapt based on biofeedback6,7. The first essential step towards biologically integrated neuromorphic systems is to achieve synaptic conditioning based on biochemical signalling activity. Here, we directly couple an organic neuromorphic device with dopaminergic cells to constitute a biohybrid synapse with neurotransmitter-mediated synaptic plasticity. By mimicking the dopamine recycling machinery of the synaptic cleft, we demonstrate both long-term conditioning and recovery of the synaptic weight, paving the way towards combining artificial neuromorphic systems with biological neural networks.

A high-speed, high-gain enhancement-mode ion-gated transistor shows promise for low-power chronically implanted bioelectronic systems.

Organic electrochemical transistors (OECTs) show great promise for flexible, low-cost, and low-voltage sensors for aqueous solutions. The majority of OECT devices are made using the polymer blend poly(ethylenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS), in which PEDOT is intrinsically doped due to inclusion of PSS. Because of this intrinsic doping, PEDOT:PSS OECTs generally operate in depletion mode, which results in a higher power consumption and limits stability. Here, a straightforward method to de-dope PEDOT:PSS using commercially available amine-based molecular de-dopants to achieve stable enhancement-mode OECTs is presented. The enhancement-mode OECTs show mobilities near that of pristine PEDOT:PSS (≈2 cm2 V−1 s−1) with stable operation over 1000 on/off cycles. The electron and proton exchange among PEDOT, PSS, and the molecular de-dopants are characterized to reveal the underlying chemical mechanism of the threshold voltage shift to negative voltages. Finally, the effect of the de-doping on the microstructure of the spin-cast PEDOT:PSS films is investigated.

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.

Poly(3,4-ethylenedioxythiophene) blended with polystyrenesulfonate and poly(styrenesulfonic acid), PEDOT:PSS, has found widespread use in organic electronics. Although PEDOT:PSS is commonly used in its doped electrically conducting state, the ability to efficiently convert PEDOT:PSS to its undoped nonconducting state is of interest for a wide variety of applications ranging from biosensors to organic neuromorphic devices. Exposure to aliphatic monoamines, acting as an electron donor and Brønsted-Lowry base, has been reported to be partly successful, but monoamines are unable to fully dedope PEDOT:PSS. Remarkably, some - but not all - polyamines can dedope PEDOT:PSS very efficiently to very low conductivity levels, but the exact chemical mechanism involved is not understood. Here, we study the dedoping efficacy of 21 different aliphatic amines. We identify the presence of two or more primary amines, which can participate in an intramolecular reaction, as the key structural motif that endows polyamines with high PEDOT:PSS dedoping strength. A multistep reaction mechanism, involving sequential electron transfer and deprotonation steps, is proposed that consistently explains the experimental results. Finally, we provide a simple method to convert the commonly used aqueous PEDOT:PSS dispersion into a precursor formulation that forms fully dedoped PEDOT:PSS films after spin coating and subsequent thermal annealing.

Neuromorphic devices are becoming increasingly appealing as efficient emulators of neural networks used to model real world problems. However, no hardware to date has demonstrated the necessary high accuracy and energy efficiency gain over CMOS in both (1) training via backpropagation and (2) in read via vector matrix multiplication. Such shortcomings are due to device non-idealities, particularly asymmetric conductance tuning in response to uniform voltage pulse inputs. Here, by formulating a general circuit model for capacitive ion-exchange neuromorphic devices, we show that asymmetric nonlinearity in organic electrochemical neuromorphic devices (ENODes) can be suppressed by an appropriately chosen write scheme. Simulations based upon our model suggest that a nonlinear write-selector could reduce the switching voltage and energy, enabling analog tuning via a continuous set of resistance states (100 states) with extremely low switching energy (∼170 fJ • μm-2). This work clarifies the pathway to neural algorithm accelerators capable of parallelism during both read and write operations.

A great deal of interest has been paid to the application of carbon-based nano-and microstructured materials as electrodes due to their relatively low-cost production, abundance, large surface area, high chemical stability, wide operating temperature range, and ease of processing including many more excellent features. The nanostructured carbon materials usually offer various micro-textures due to their varying degrees of graphitisation, a rich variety in terms of dimensionality as well as morphologies, extremely large surface accessibility and high electrical conductivity, etc. The possibilities of activating them by chemical and physical methods allow these materials to be produced with further higher surface area and controlled distribution of pores from nanoscale upto macroscopic dimensions, which actually play the most crucial role towards construction of the efficient electrode/electrolyte interfaces for capacitive processes in energy storage applications. Development of new carbon materials with extremely high surface areas could exhibit significant potential in this context and motivated by this in present work, we report for the first time the utilization of ultralight and extremely porous nano-microtubular Aerographite tetrapodal network as a functional interface to probe the electrochemical properties for capacitive energy storage. A simple and robust electrode fabrication strategy based on surface functionalized Aerographite with optimum porosity leads to significantly high specific capacitance (640 F/g) with high energy (14.2 Wh/kg) and power densities (9.67x103 W/kg) which has been discussed in detail.