In Liquido Computation with Electrochemical Transistors and Mixed Conductors for Intelligent Bioelectronics

Single-transistor organic electrochemical neurons

Neuromorphic devices that mimic the energy-efficient sensing and processing capabilities of biological neurons hold significant promise for developing bioelectronic systems capable of precise sensing and adaptive stimulus-response. However, current silicon-based technologies lack biocompatibility and rely on operational principles that differ from those of biological neurons. Organic electrochemical neurons (OECNs) address these shortcomings but typically require multiple components, limiting their integration density and scalability. Here, we report a single-transistor OECN (1T-OECN) that leverages the hysteretic switching of organic electrochemical memtransistors (OECmTs) based on poly(benzimidazobenzophenanthroline). By tuning the electrolyte and driving voltage, the OECmTs switch between high- and low-resistance states, enabling action potential generation, dynamic spiking, and logic operations within a single device with dimensions comparable to biological neurons. The compact 1T-OECN design (~180 µm2 footprint) supports high-density integration, achieving over 62,500 neurons/cm2 on flexible substrates. This advancement highlights the potential for scalable, bio-inspired neuromorphic computing and seamless integration with biological systems.

Ephaptic Coupling in Ultralow-Power Ion-Gel Nanofiber Artificial Synapses for Enhanced Working Memory

Neuromorphic devices are designed to replicate the energy-efficient information processing advantages found in biological neural networks by emulating the working mechanisms of neurons and synapses. However, most existing neuromorphic devices focus primarily on functionally mimicking biological synapses, with insufficient emphasis on ion transport mechanisms. This limitation makes it challenging to achieve the complexity and connectivity inherent in biological systems, such as ephaptic coupling. Here, an ionic biomimetic synaptic device based on a flexible ion-gel nanofiber network is proposed, which transmits information and enables ephaptic coupling through capacitance formation by ion transport with an extremely low energy consumption of just 6 femtojoules. The hysteretic ion transport behavior endows the device with synaptic-like memory effects, significantly enhancing the performance of the reservoir computing system for classifying the MNIST handwritten digit dataset and demonstrating high efficiency in edge learning. More importantly, the devices in an array establish communication connections, exhibiting global oscillatory behaviors similar to ephaptic coupling in biological neural networks. This connectivity enables the array to perform working memory tasks, paving the way for developing brain-like systems characterized by high complexity and vast connectivity.

The Rising of Flexible Organic Electrochemical Transistors in Sensors and Intelligent Circuits

Flexible electronic devices in biomedicine, environmental monitoring, and brain-like computing have garnered significant attention. Among these, organic electrochemical transistors (OECTs) have been spotlighted in flexible sensors and neuromorphic circuits for their low power consumption, high signal amplification, excellent biocompatibility, chemical stability, stretchability, and flexibility. However, OECTs will also face some challenges on the way to commercialized applications, including the need for improved long-term stability, enhanced performance of N-type materials, integration with existing technologies, and cost-effective manufacturing processes. This review presents the device physics of OECTs in detail, including the evaluation of their various properties and the introduction of different configurations of the aforementioned OECTs. Subsequently, the components of this device and their roles are explained in depth, and the main ways to design and fabricate flexible OECTs are summarized. Following this, we summarize and analyze the principles and applications of OECTs for electrophysiological signal sensing, chemical sensing, biosensing, and sensor arrays. In addition, the concepts of OECT-based digital and neuromorphic circuits and their applications are presented. Finally, the paper summarizes the opportunities and challenges of OECT-based flexible electronics.

Biocompatible Acellular Dermal Matrix-Based Neuromorphic Device with Ultralow Voltage, Ion Channel Emulation, and Synaptic Forgetting Visualization Computation

Neuromorphic bioelectronics aim to integrate electronics with biological systems yet encounter challenges in biocompatibility, operating voltages, power consumption, and stability. This study presents biocompatible neuromorphic devices fabricated from acellular dermal matrix (ADM) derived from porcine dermis using low-temperature supercritical CO2 extraction. The ADM preserves the natural scaffold structure of collagen and minimizes immunogenicity by eliminating cells, fats, and noncollagenous impurities, ensuring excellent biocompatibility. The ADM-based devices emulate biological ion channels with biphasic membrane current modulation, exhibiting temperature dependency and pH sensitivity. It operates at an ultralow voltage of 1 mV and demonstrates reliable synaptic modulation exceeding 4 × 104 endurance cycles. The activation voltage can be theoretically as low as 59 μV, comparable to brainwave signals with a power of merely 7 aJ/event. Furthermore, a brain-like forgetting visualization algorithm is developed, leveraging the synaptic forgetting plasticity of ADM-based devices to achieve complex computing tasks in a highly energy-efficient manner. Neuromorphic devices based on ADM not only hold potential in implantable biointerfaces due to their exceptional biocompatibility, ultralow voltage, and power but also provide a feasible way for energy-efficient computing paradigms through a synergistic hardware-software approach.

Exploiting Spatial Ionic Dynamics in Solid-State Organic Electrochemical Transistors for Multi-Tactile Sensing and Processing

The human nervous system inspires the next generation of sensory and communication systems for robotics, human-machine interfaces (HMIs), biomedical applications, and artificial intelligence. Neuromorphic approaches address processing challenges; however, the vast number of sensors and their large-scale distribution complicate analog data manipulation. Conventional digital multiplexers are limited by complex circuit architecture and high supply voltage. Large sensory arrays further complicate wiring. An 'in-electrolyte computing' platform is presented by integrating organic electrochemical transistors (OECTs) with a solid-state polymer electrolyte. These devices use synapse-like signal transport and spatially dependent bulk ionic doping, achieving over 400 times modulation in channel conductance, allowing discrimination of locally random-access events without peripheral circuitry or address assignment. It demonstrates information processing from 12 tactile sensors with a single OECT output, showing clear advantages in circuit simplicity over existing all-electronic, all-digital implementations. This self-multiplexer platform offers exciting prospects for circuit-free integration with sensory arrays for high-quality, large-volume analog signal processing.

Single ambipolar OECT-based inverter with volatility and nonvolatility on demand

Organic electrochemical transistor (OECT)-based inverter introduces new prospects for energy-efficient brain-inspired artificial intelligence devices. Here, we report single-component OECT-based inverters by incorporating ambipolar p(gDPP-V). Notably, p(gDPP-V) shows state-of-the-art ambipolar OECT performances in both conventional (p/n-type mode transconductance of 29/25 S cm-1) and vertical (transconductance of 297.2/292.4 μS μm-2 under p/n operation) device architectures. Especially, the resulting highly stable vertical OECT-based inverter shows a high voltage gain of 105 V V-1 under a low driving voltage of 0.8 V. The inverter exhibits undiscovered voltage-regulated dual mode: volatile receptor and nonvolatile synapse. Moreover, applications of physiology signal recording and demonstrations of NAND/NOR logic circuits are investigated within the volatile feature, while neuromorphic simulations with a convolutional neural network and image memorizing capabilities are explored under the nonvolatile behavior. The ambipolar OECT-based inverter, capable of both volatile and nonvolatile operations, provides possibilities for the applications of reconfigurable complementary logic circuits in novel neuromorphic computing paradigms.

Bistable organic electrochemical transistors: enthalpy vs. entropy

Organic electrochemical transistors (OECTs) underpin a range of emerging technologies, from bioelectronics to neuromorphic computing, owing to their unique coupling of electronic and ionic charge carriers. In this context, various OECT systems exhibit significant hysteresis in their transfer curve, which is frequently leveraged to achieve non-volatility. Meanwhile, a general understanding of its physical origin is missing. Here, we introduce a thermodynamic framework that readily explains the emergence of bistable OECT operation via the interplay of enthalpy and entropy. We validate this model through temperature-resolved characterizations, material manipulation, and thermal imaging. Further, we reveal deviations from Boltzmann statistics for the subthreshold swing and reinterpret existing literature. Capitalizing on these findings, we finally demonstrate a single-OECT Schmitt trigger, thus compacting a multi-component circuit into a single device. These insights provide a fundamental advance for OECT physics and its application in non-conventional computing, where symmetry-breaking phenomena are pivotal to unlock new paradigms of information processing.

An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning

Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a major challenge especially exploiting time-dependent biosignalling such as neurotransmitter release. Here, we present an integrated organic platform capable of cooperating with standard silicon technologies, to achieve brain-inspired computing via adaptive synaptic potentiation and depression, in a closed-loop fashion. The microfabricated platform could be interfaced and control a robotic hand which ultimately was able to learn the grasping of differently sized objects, autonomously.

Finite-element modeling of neuromodulation via controlled delivery of potassium ions using conductive polymer-coated microelectrodes

Objective. The controlled delivery of potassium is an interesting neuromodulation modality, being potassium ions involved in shaping neuron excitability, synaptic transmission, network synchronization, and playing a key role in pathological conditions like epilepsy and spreading depression. Despite many successful examples of pre-clinical devices able to influence the extracellular potassium concentration, computational frameworks capturing the corresponding impact on neuronal activity are still missing.Approach. We present a finite-element model describing a PEDOT:PSS-coated microelectrode (herein, simplyionic actuator) able to release potassium and thus modulate the activity of a cortical neuron in anin-vitro-like setting. The dynamics of ions in the ionic actuator, the neural membrane, and the cellular fluids are solved self-consistently.Main results. We showcase the capability of the model to describe on a physical basis the modulation of the intrinsic excitability of the cell and of the synaptic transmission following the electro-ionic stimulation produced by the actuator. We consider three case studies for the ionic actuator with different levels of selectivity to potassium: ideal selectivity, no selectivity, and selectivity achieved by embedding ionophores in the polymer.Significance. This work is the first step toward a comprehensive computational framework aimed to investigate novel neuromodulation devices targeting specific ionic species, as well as to optimize their design and performance, in terms of the induced modulation of neural activity.