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Future brain-computer interfaces will require local and highly individualized signal processing of fully integrated electronic circuits within the nervous system and other living tissue. New devices will need to be developed that can receive data from a sensor array, process data into meaningful information, and translate that information into a format that living systems can interpret. Here, we report the first example of interfacing a hardware-based pattern classifier with a biological nerve. The classifier implements the Widrow-Hoff learning algorithm on an array of evolvable organic electrochemical transistors (EOECTs). The EOECTs' channel conductance is modulated in situ by electropolymerizing the semiconductor material within the channel, allowing for low voltage operation, high reproducibility, and an improvement in state retention of two orders of magnitude over state-of-the-art OECT devices. The organic classifier is interfaced with a biological nerve using an organic electrochemical spiking neuron to translate the classifier's output to a simulated action potential. The latter is then used to stimulate muscle contraction selectively based on the input pattern, thus paving the way for the development of closed-loop therapeutic systems.

Bio-integrated neuromorphic systems promise for new protocols to record and regulate the signaling of biological systems. Making such artificial neural circuits successful requires minimal circuit complexity and ion-based operating mechanisms similar to that of biology. However, simple leaky integrate-and-fire model neurons, commonly realized in either silicon or organic semiconductor neuromorphic systems, can emulate only a few neural features. More functional neuron models, based on traditional complex Si-based complementary-metal-oxide-semiconductor (CMOS) or negative differential resistance (NDR) device circuits, are complicated to fabricate, not biocompatible, and lack ion- and chemical-based modulation features. Here we report a biorealistic conductance-based organic electrochemical neuron (c-OECN) using a mixed ion-electron conducting ladder-type polymer with reliable ion-tunable antiambipolarity. The latter is used to emulate the activation/inactivation of Na channels and delayed activation of K channels of biological neurons. These c-OECNs can then spike at bioplausible frequencies nearing 100 Hz, emulate most critical biological neural features, demonstrate stochastic spiking, and enable neurotransmitter and Ca2+-based spiking modulation. These combined features are impossible to achieve using previous technologies.

Organic electrochemical transistors (OECTs) are currently being investigated for various applications, ranging from sensors to logics and neuromorphic hardware. The fabrication process must be compatible with flexible and scalable digital techniques to address this wide spectrum of applications. Here, we report a direct-write additive process to fabricate fully 3D printed OECTs. We developed 3D printable conducting, semiconducting, insulating, and electrolyte inks to achieve this. The 3D-printed OECTs, operating in the depletion mode, can be fabricated on thin and flexible substrates, yielding high mechanical and environmental stability. We also developed a 3D printable nanocellulose formulation for the OECT substrate, demonstrating one of the first examples of fully 3D printed electronic devices. Good dopamine biosensing capabilities (limit of detection down to 6 uM without metal gate electrodes) and long-term (~1 hour) synapses response underscore that the present OECT manufacturing strategy is suitable for diverse applications requiring rapid design change and digitally enabled direct-write techniques.

The ability to accurately extract low-amplitude voltage signals is crucial in several fields, ranging from single-use diagnostics and medical technology to robotics and the Internet of Things. The organic electrochemical transistor, which features large transconductance values at low operation voltages, is ideal for monitoring small signals. Its large transconductance translates small gate voltage variations into significant changes in the drain current. However, a current-to-voltage conversion is further needed to allow proper data acquisition and signal processing. Low power consumption, high amplification, and manufacturability on flexible and low-cost carriers are also crucial and highly anticipated for targeted applications. Here, we report low-power and high-gain flexible circuits based on printed complementary organic electrochemical transistors (OECTs). We leverage the low threshold voltage of both p-type and n-type enhancement-mode OECTs to develop complementary voltage amplifiers that can sense voltages as low as 100 $\mu$V, with gains of 30.4 dB and at a power consumption < 2.7 $\mu$W (single-stage amplifier). At the optimal operating conditions, the voltage gain normalized to power consumption reaches 169 dB/$\mu$W, which is > 50 times larger than state-of-the-art OECT-based amplifiers. In a two-stage configuration, the complementary voltage amplifiers reach a DC voltage gain of 193 V/V, which is the highest among emerging CMOS-like technologies operating at supply voltages below 1 volt. Our findings demonstrate that flexible complementary circuits based on printed OECTs define a power-efficient platform for sensing and amplifying low-amplitude voltage signals in several emerging beyond-silicon applications.

Precise manipulation of light-matter interaction has enabled a wide variety of approaches to create bright and vivid structural colours. Techniques utilizing photonic crystals, Fabry-P\'erot cavities, plasmonics, or high-refractive index dielectric metasurfaces have been studied for applications ranging from optical coatings to reflective displays. However, complicated fabrication procedures for sub-wavelength nanostructures, limited active areas, and inherent absence of tunability with these approaches significantly impede their further developments towards flexible, large-scale, and switchable devices compatible with facile and cost-effective production. Herein, we present a way to generate structural colours based on conducting polymer thin films prepared on metallic surfaces via vapour phase polymerization and ultraviolet (UV) light patterning. Varying the UV dose leads to synergistic variation of film absorption and thickness, which generates controllable colours from violet to red. Together with greyscale photomasks this enables fabrication of high-resolution colour images using single exposure steps. We further demonstrate spatiotemporal tuning of the structurally coloured surfaces and images via electrochemical modulation of the polymer redox state. The simple structure, facile fabrication, wide colour gamut, and dynamic colour tuning make this concept competitive for future multi-functional and smart displays.