Self-sustained green neuromorphic interfaces

Crossmodal sensory neurons based on high-performance flexible memristors for human-machine in-sensor computing system

Constructing crossmodal in-sensor processing system based on high-performance flexible devices is of great significance for the development of wearable human-machine interfaces. A bio-inspired crossmodal in-sensor computing system can perform real-time energy-efficient processing of multimodal signals, alleviating data conversion and transmission between different modules in conventional chips. Here, we report a bio-inspired crossmodal spiking sensory neuron (CSSN) based on a flexible VO2 memristor, and demonstrate a crossmodal in-sensor encoding and computing system for wearable human-machine interfaces. We demonstrate excellent performance in the VO2 memristor including endurance (>1012), uniformity (0.72% for cycle-to-cycle variations and 3.73% for device-to-device variations), speed (<30 ns), and flexibility (bendable to a curvature radius of 1 mm). A flexible hardware processing system is implemented based on the CSSN, which can directly perceive and encode pressure and temperature bimodal information into spikes, and then enables the real-time haptic-feedback for human-machine interaction. We successfully construct a crossmodal in-sensor spiking reservoir computing system via the CSSNs, which can achieve dynamic objects identification with a high accuracy of 98.1% and real-time signal feedback. This work provides a feasible approach for constructing flexible bio-inspired crossmodal in-sensor computing systems for wearable human-machine interfaces.

Beyond 25 years of biomedical innovation in nano-bioelectronics

Nano-bioelectronics, which blend the precision of nanotechnology with the complexity of biological systems, are evolving with innovations such as silicon nanowires, carbon nanotubes, and graphene. These elements serve applications from biochemical sensing to brain-machine interfacing. This review examines nano-bioelectronics' role in advancing biomedical interventions and discusses their potential in environmental monitoring, agricultural productivity, energy efficiency, and creative fields. The field is transitioning from molecular to ecosystem-level applications, with research exploring complex cellular mechanisms and communication. This fosters understanding of biological interactions at various levels, such as suggesting transformative approaches for ecosystem management and food security. Future research is expected to focus on refining nano-bioelectronic devices for integration with biological systems and on scalable manufacturing to broaden their reach and functionality.

Sodium-Controlled Interfacial Resistive Switching in Thin Film Niobium Oxide for Neuromorphic Applications

A double layer 2-terminal device is employed to show Na-controlled interfacial resistive switching and neuromorphic behavior. The bilayer is based on interfacing biocompatible NaNbO3 and Nb2O5, which allows the reversible uptake of Na+ in the Nb2O5 layer. We demonstrate voltage-controlled interfacial barrier tuning via Na+ transfer, enabling conductivity modulation and spike-amplitude- and spike-timing-dependent plasticity. The neuromorphic behavior controlled by Na+ ion dynamics in biocompatible materials shows potential for future low-power sensing electronics and smart wearables with local processing.

Expression of filaments of the Geobacter extracellular cytochrome OmcS in Shewanella oneidensis

The physiological role of Geobacter sulfurreducens extracellular cytochrome filaments is a matter of debate and the development of proposed electronic device applications of cytochrome filaments awaits methods for large-scale cytochrome nanowire production. Functional studies in G. sulfurreducens are stymied by the broad diversity of redox-active proteins on the outer cell surface and the redundancy and plasticity of extracellular electron transport routes. G. sulfurreducens is a poor chassis for producing cytochrome nanowires for electronics because of its slow, low-yield, anaerobic growth. Here we report that filaments of the G. sulfurreducens cytochrome OmcS can be heterologously expressed in Shewanella oneidensis. Multiple lines of evidence demonstrated that a strain of S. oneidensis, expressing the G. sulfurreducens OmcS gene on a plasmid, localized OmcS on the outer cell surface. Atomic force microscopy revealed filaments with the unique morphology of OmcS filaments emanating from cells. Electron transfer to OmcS appeared to require a functional outer-membrane porin-cytochrome conduit. The results suggest that S. oneidensis, which grows rapidly to high culture densities under aerobic conditions, may be suitable for the development of a chassis for producing cytochrome nanowires for electronics applications and may also be a good model microbe for elucidating cytochrome filament function in anaerobic extracellular electron transfer.

A modular organic neuromorphic spiking circuit for retina-inspired sensory coding and neurotransmitter-mediated neural pathways

Signal communication mechanisms within the human body rely on the transmission and modulation of action potentials. Replicating the interdependent functions of receptors, neurons and synapses with organic artificial neurons and biohybrid synapses is an essential first step towards merging neuromorphic circuits and biological systems, crucial for computing at the biological interface. However, most organic neuromorphic systems are based on simple circuits which exhibit limited adaptability to both external and internal biological cues, and are restricted to emulate only specific the functions of an individual neuron/synapse. Here, we present a modular neuromorphic system which combines organic spiking neurons and biohybrid synapses to replicate a neural pathway. The spiking neuron mimics the sensory coding function of afferent neurons from light stimuli, while the neuromodulatory activity of interneurons is emulated by neurotransmitters-mediated biohybrid synapses. Combining these functions, we create a modular connection between multiple neurons to establish a pre-processing retinal pathway primitive.

Generic Air-Gen Effect in Nanoporous Materials for Sustainable Energy Harvesting from Air Humidity

Air humidity is a vast, sustainable reservoir of energy that, unlike solar and wind, is continuously available. However, previously described technologies for harvesting energy from air humidity are either not continuous or require unique material synthesis or processing, which has stymied scalability and broad deployment. Here, a generic effect for continuous energy harvesting from air humidity is reported, which can be applied to a broad range of inorganic, organic, and biological materials. The common feature of these materials is that they are engineered with appropriate nanopores to allow air water to pass through and undergo dynamic adsorption-desorption exchange at the porous interface, resulting in surface charging. The top exposed interface experiences this dynamic interaction more than the bottom sealed interface in a thin-film device structure, yielding a spontaneous and sustained charging gradient for continuous electric output. Analyses of material properties and electric outputs lead to a "leaky capacitor" model that can describe how electricity is harvested and predict current behaviors consistent with experiments. Predictions from the model guide the fabrication of devices made from heterogeneous junctions of different materials to further expand the device category. The work opens a wide door for the broad exploration of sustainable electricity from air.

Self-Sustained Programmable Hygroelectronic Interfaces for Humidity-Regulated Hierarchical Information Encryption and Display

The emerging moisture-driven energy generation (MEG) technology opens up new possibilities for humidity-responsive materials, devices, and interdisciplinary opportunities in fields like information security. However, such potential remains untapped. Here, an original MEG structure with a hygroionic energy-conversion route by selective coating of ionic hygroscopic hydrogels on a carbon black surface is reported. The hygroionic route features a process in which the scavenged energy is stored in the electrical double layers formed at the interfaces between the ionic hydrogel and the carbon nanoparticles. The resultant electrical field developed across the hydrogel-coated wet carbon and the rest of the dry carbon area is thus durably lasted. Based on this unique structure, hygroelectronic information interfaces (HEII) for humidity-regulated information encryption and display are put forward by devising hydrogel patterns on a carbon platform. Further by tuning the hygroscopicity of the ionic hydrogels and incorporating encoding methods (e.g., Morse code), it is demonstrated that the HEII platform is programmable to carry different information in certain humidity ranges. Unlike those conventional anti-counterfeiting methods that optically reveal the hidden information once the required stimulus is provided, the new HEII serves as a hierarchical solution for high-security encryption and display.

Moisture-Enabled Electricity from Hygroscopic Materials: A New Type of Clean Energy

Water utilization is accompanied with the development of human beings, whereas gaseous moisture is usually regarded as an underexploited resource. The advances of highly efficient hygroscopic materials endow atmospheric water harvesting as an intriguing solution to convert moisture into clean water. The discovery of hygroelectricity, which refers to the charge buildup at a material surface dependent on humidity, and the following moisture-enabled electric generation (MEG) realizes energy conversion and directly outputs electricity. Much progress has been made since then to optimize MEG performance, pushing forward the applications of MEG into a practical level. Herein, the evolvement and development of MEG are systematically summarized in a chronological order. The optimization strategies of MEG are discussed and comprehensively evaluated. Then, the latest applications of MEG are presented, including high-performance powering units and self-powered devices. In the end, a perspective on the future development of MEG is given for inspiring more researchers into this promising area.

Neuromorphic Nanoionics for Human-Machine Interaction: From Materials to Applications

Human-machine interaction (HMI) technology has undergone significant advancements in recent years, enabling seamless communication between humans and machines. Its expansion has extended into various emerging domains, including human healthcare, machine perception, and biointerfaces, thereby magnifying the demand for advanced intelligent technologies. Neuromorphic computing, a paradigm rooted in nanoionic devices that emulate the operations and architecture of the human brain, has emerged as a powerful tool for highly efficient information processing. This paper delivers a comprehensive review of recent developments in nanoionic device-based neuromorphic computing technologies and their pivotal role in shaping the next-generation of HMI. Through a detailed examination of fundamental mechanisms and behaviors, the paper explores the ability of nanoionic memristors and ion-gated transistors to emulate the intricate functions of neurons and synapses. Crucial performance metrics, such as reliability, energy efficiency, flexibility, and biocompatibility, are rigorously evaluated. Potential applications, challenges, and opportunities of using the neuromorphic computing technologies in emerging HMI technologies, are discussed and outlooked, shedding light on the fusion of humans with machines.

A hybrid transistor with transcriptionally controlled computation and plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Water-Based Generators with Cellulose Acetate: Uncovering the Mechanisms of Power Generation

Power generation technologies based on water movement and evaporation use water, which covers more than 70% of the Earth's surface and can also generate power from moisture in the air. Studies are conducted to diversify materials to increase power generation performance and validate energy generation mechanisms. In this study, a water-based generator was fabricated by coating cellulose acetate with carbon black. To optimize the generator, Fourier-transform infrared spectroscopy, specific surface area, zeta potential, particle size, and electrical performance analyses were conducted. The developed generator is a cylindrical generator with a diameter of 7.5 mm and length of 20 mm, which can generate a voltage of 0.15 V and current of 82 μA. Additionally, we analyzed the power generation performance using three factors (physical properties, cation effect, and evaporation environment) and proposed an energy generation mechanism. Furthermore, we developed an eco-friendly and low-cost generator using natural fibers with a simple manufacturing process. The proposed generator can contribute to the identification of energy generation mechanisms and is expected to be used as an alternative energy source in the future.

Cascade-heterogated biphasic gel iontronics for electronic-to-multi-ionic signal transmission

Currently, electronics and iontronics in abiotic-biotic systems can only use electrons and single-species ions as unitary signal carriers. Thus, a mechanism of gating transmission for multiple biosignals in such devices is needed to match and modulate complex aqueous-phase biological systems. Here we report the use of cascade-heterogated biphasic gel iontronics to achieve diverse electronic-to-multi-ionic signal transmission. The cascade-heterogated property determined the transfer free energy barriers experienced by ions and ionic hydration-dehydration states under an electric potential field, fundamentally enhancing the distinction of cross-interface transmission between different ions by several orders of magnitude. Such heterogated or chemical-heterogated iontronics with programmable features can be coupled with multi-ion cross-interface mobilities for hierarchical and selective cross-stage signal transmission. We expect that such iontronics would be ideal candidates for a variety of biotechnology applications.

Memristors based on NdNiO3 nanocrystals film as sensory neurons for neuromorphic computing

By mimicking the behavior of the human brain, artificial neural systems offer the possibility to further improve computing efficiency and solve the von Neumann bottleneck. In particular, neural systems with perceptual capability expand the application field and lay a good foundation for the construction of perceptual storage and computational systems. However, research on neurons with perceptual functions is still relatively scarce, with most works focusing on optoelectronic synapses. The neuron is important for neuromorphic computing systems because neurons output excitatory or inhibitory stimuli to regulate the weight of synapses. Therefore, the construction of sensory neurons is crucial to expand the application range of brain-like neural computing. Here, an artificial sensory neuron is proposed, which is constructed using a photosensitive bipolar threshold switching memristor based on NdNiO3 (NNO) nanocrystals. These metallic phase nanocrystals can not only enhance the local electric field, but also act as a reservoir for defects (VoS) to guide the growth of conductive filaments and stabilize the performance of the device. They present stable bipolar threshold switching behavior with a low 120 nW set power, and the operating voltages decreased in light due to photocarrier action. A leaky integrate firing (LIF) neuron has been realized, which achieved key biological neuron functions, such as all-or-nothing spiking, threshold-driven firing, refractory period, and spiking frequency modulation. The LIF neurons receiving optical inputs have the properties of an artificial sensory neuron. It could regulate the spiking output frequency at different light densities, which could be used for a ship approaching a port. This work provides a promising hardware implementation towards constructing high-performance artificial intelligence to assist ships at night in a sensory system.

Emerging Iontronic Neural Devices for Neuromorphic Sensory Computing

Living organisms have a very mysterious and powerful sensory computing system based on ion activity. Interestingly, studies on iontronic devices in the past few years have proposed a promising platform for simulating the sensing and computing functions of living organisms, because: 1) iontronic devices can generate, store, and transmit a variety of signals by adjusting the concentration and spatiotemporal distribution of ions, which analogs to how the brain performs intelligent functions by alternating ion flux and polarization; 2) through ionic-electronic coupling, iontronic devices can bridge the biosystem with electronics and offer profound implications for soft electronics; 3) with the diversity of ions, iontronic devices can be designed to recognize specific ions or molecules by customizing the charge selectivity, and the ionic conductivity and capacitance can be adjusted to respond to external stimuli for a variety of sensing schemes, which can be more difficult for electron-based devices. This review provides a comprehensive overview of emerging neuromorphic sensory computing by iontronic devices, highlighting representative concepts of both low-level and high-level sensory computing and introducing important material and device breakthroughs. Moreover, iontronic devices as a means of neuromorphic sensing and computing are discussed regarding the pending challenges and future directions.

A Hybrid Transistor with Transcriptionally Controlled Computation and Plasticity

Organic electrochemical transistors (OECTs) are ideal devices for translating biological signals into electrical readouts and have applications in bioelectronics, biosensing, and neuromorphic computing. Despite their potential, developing programmable and modular methods for living systems to interface with OECTs has proven challenging. Here we describe hybrid OECTs containing the model electroactive bacterium Shewanella oneidensis that enable the transduction of biological computations to electrical responses. Specifically, we fabricated planar p-type OECTs and demonstrated that channel de-doping is driven by extracellular electron transfer (EET) from S. oneidensis. Leveraging this mechanistic understanding and our ability to control EET flux via transcriptional regulation, we used plasmid-based Boolean logic gates to translate biological computation into current changes within the OECT. Finally, we demonstrated EET-driven changes to OECT synaptic plasticity. This work enables fundamental EET studies and OECT-based biosensing and biocomputing systems with genetically controllable and modular design elements.

Highly Reliable Textile-Type Memristor by Designing Aligned Nanochannels

Information-processing devices are the core components of modern electronics. Integrating them into textiles is the indispensable demand for electronic textiles to form close-loop functional systems. Memristors with crossbar configuration are regarded as promising building blocks to design woven information-processing devices that seamlessly unify with textiles. However, the memristors always suffer from severe temporal and spatial variations due to the random growth of conductive filaments during filamentary switching processes. Here, inspired by the ion nanochannels across synaptic membranes, a highly reliable textile-type memristor made of Pt/CuZnS memristive fiber with aligned nanochannels, showing small set voltage variation (<5.6%) under ultralow set voltage (≈0.089 V), high on/off ratio (≈106 ), and low power consumption (0.1 nW), is reported. Experimental evidence indicate that nanochannels with abundant active S defects can anchor silver ions and confine their migrations to form orderly and efficient conductive filaments. Such memristive performances enable the resultant textile-type memristor array to have high device-to-device uniformity and process complex physiological data like brainwave signals with high recognition accuracy (95%). The textile-type memristor arrays are mechanically durable to withstand hundreds of bending and sliding deformations, and seamlessly unified with sensing, power-supplying, and displaying textiles/fibers to form all-textile integrated electronic systems for new generation human-machine interactions.

Microbial nanowires with genetically modified peptide ligands to sustainably fabricate electronic sensing devices

Nanowires have substantial potential as the sensor component in electronic sensing devices. However, surface functionalization of traditional nanowire and nanotube materials with short peptides that increase sensor selectivity and sensitivity requires complex chemistries with toxic reagents. In contrast, microorganisms can assemble pilin monomers into protein nanowires with intrinsic conductivity from renewable feedstocks, yielding an electronic material that is robust and stable in applications, but also biodegradable. Here we report that the sensitivity and selectivity of protein nanowire-based sensors can be modified with a simple plug and play genetic approach in which a short peptide sequence, designed to bind the analyte of interest, is incorporated into the pilin protein that is microbially assembled into nanowires. We employed a scalable Escherichia coli chassis to fabricate protein nanowires that displayed either a peptide previously demonstrated to effectively bind ammonia, or a peptide known to bind acetic acid. Sensors comprised of thin films of the nanowires amended with the ammonia-specific peptide had a ca. 100-fold greater response to ammonia than sensors made with unmodified protein nanowires. Protein nanowires with the peptide that binds acetic acid yielded a 4-fold higher response than nanowires without the peptide. The protein nanowire-based sensors had greater responses than previously reported sensors fabricated with other nanomaterials. The results demonstrate that protein nanowires with enhanced sensor response for analytes of interest can be fabricated with a flexible genetic strategy that sustainably eliminates the energy, environmental, and health concerns associated with other common nanomaterials.

Recent progress in bio-voltage memristors working with ultralow voltage of biological amplitude

Neuromorphic systems built from memristors that emulate bioelectrical information processing in the brain may overcome the limitations of traditional computing architectures. However, functional emulation alone may still not attain all the merits of bio-computation, which uses action potentials of 50-120 mV at least 10 times lower than signal amplitude in conventional electronics to achieve extraordinary power efficiency and effective functional integration. Reducing the functional voltage in memristors to this biological amplitude can thus advance neuromorphic engineering and bio-emulated integration. This review aims to provide a timely update on the effort and progress in this burgeoning research direction, covering the aspects of device material composition, performance, working mechanism, and potential application.

Low-Dimensional-Materials-Based Flexible Artificial Synapse: Materials, Devices, and Systems

With the rapid development of artificial intelligence and the Internet of Things, there is an explosion of available data for processing and analysis in any domain. However, signal processing efficiency is limited by the Von Neumann structure for the conventional computing system. Therefore, the design and construction of artificial synapse, which is the basic unit for the hardware-based neural network, by mimicking the structure and working mechanisms of biological synapses, have attracted a great amount of attention to overcome this limitation. In addition, a revolution in healthcare monitoring, neuro-prosthetics, and human-machine interfaces can be further realized with a flexible device integrating sensing, memory, and processing functions by emulating the bionic sensory and perceptual functions of neural systems. Until now, flexible artificial synapses and related neuromorphic systems, which are capable of responding to external environmental stimuli and processing signals efficiently, have been extensively studied from material-selection, structure-design, and system-integration perspectives. Moreover, low-dimensional materials, which show distinct electrical properties and excellent mechanical properties, have been extensively employed in the fabrication of flexible electronics. In this review, recent progress in flexible artificial synapses and neuromorphic systems based on low-dimensional materials is discussed. The potential and the challenges of the devices and systems in the application of neuromorphic computing and sensory systems are also explored.

Emerging memristive neurons for neuromorphic computing and sensing

Inspired by the principles of the biological nervous system, neuromorphic engineering has brought a promising alternative approach to intelligence computing with high energy efficiency and low consumption. As pivotal components of neuromorphic system, artificial spiking neurons are powerful information processing units and can achieve highly complex nonlinear computations. By leveraging the switching dynamic characteristics of memristive device, memristive neurons show rich spiking behaviors with simple circuit. This report reviews the memristive neurons and their applications in neuromorphic sensing and computing systems. The switching mechanisms that endow memristive devices with rich dynamics and nonlinearity are highlighted, and subsequently various nonlinear spiking neuron behaviors emulated in these memristive devices are reviewed. Then, recent development is introduced on neuromorphic system with memristive neurons for sensing and computing. Finally, we discuss challenges and outlooks of the memristive neurons toward high-performance neuromorphic hardware systems and provide an insightful perspective for the development of interactive neuromorphic electronic systems.

An Effective Sneak-Path Solution Based on a Transient-Relaxation Device

An efficient strategy for addressing individual devices is required to unveil the full potential of memristors for high-density memory and computing applications. Existing strategies using two-terminal selectors that are preferable for compact integration have trade-offs in reduced generality or functional window. A strategy that applies to broad memristors and maintains their full-range functional window is proposed. This strategy uses a type of unipolar switch featuring a transient relaxation or retention as the selector. The unidirectional current flow in the switch suppresses the sneak-path current, whereas the transient-relaxation window is exploited for bidirectional programming. A unipolar volatile memristor with ultralow switching voltage (e.g., <100 mV), constructed from a protein nanowire dielectric harvested from Geobacter sulfurreducens, is specifically employed as the example switch to highlight the advantages and scalability in the strategy for array integration.

Reconfigurable neuromorphic memristor network for ultralow-power smart textile electronics

Neuromorphic computing memristors are attractive to construct low-power- consumption electronic textiles due to the intrinsic interwoven architecture and promising applications in wearable electronics. Developing reconfigurable fiber-based memristors is an efficient method to realize electronic textiles that capable of neuromorphic computing function. However, the previously reported artificial synapse and neuron need different materials and configurations, making it difficult to realize multiple functions in a single device. Herein, a textile memristor network of Ag/MoS₂/HfAlO_x/carbon nanotube with reconfigurable characteristics was reported, which can achieve both nonvolatile synaptic plasticity and volatile neuron functions. In addition, a single reconfigurable memristor can realize integrate-and-fire function, exhibiting significant advantages in reducing the complexity of neuron circuits. The firing energy consumption of fiber-based memristive neuron is 1.9 fJ/spike (femtojoule-level), which is at least three orders of magnitude lower than that of the reported biological and artificial neuron (picojoule-level). The ultralow energy consumption makes it possible to create an electronic neural network that reduces the energy consumption compared to human brain. By integrating the reconfigurable synapse, neuron and heating resistor, a smart textile system is successfully constructed for warm fabric application, providing a unique functional reconfiguration pathway toward the next-generation in-memory computing textile system.

Microbial biofilms for electricity generation from water evaporation and power to wearables

Employing renewable materials for fabricating clean energy harvesting devices can further improve sustainability. Microorganisms can be mass produced with renewable feedstocks. Here, we demonstrate that it is possible to engineer microbial biofilms as a cohesive, flexible material for long-term continuous electricity production from evaporating water. Single biofilm sheet (~40 µm thick) serving as the functional component in an electronic device continuously produces power density (~1 μW/cm²) higher than that achieved with thicker engineered materials. The energy output is comparable to that achieved with similar sized biofilms catalyzing current production in microbial fuel cells, without the need for an organic feedstock or maintaining cell viability. The biofilm can be sandwiched between a pair of mesh electrodes for scalable device integration and current production. The devices maintain the energy production in ionic solutions and can be used as skin-patch devices to harvest electricity from sweat and moisture on skin to continuously power wearable devices. Biofilms made from different microbial species show generic current production from water evaporation. These results suggest that we can harness the ubiquity of biofilms in nature as additional sources of biomaterial for evaporation-based electricity generation in diverse aqueous environments.

Though water evaporation-driven electricity generation is an attractive sustainable energy production strategy, existing electronic devices suffer from poor performance or is costly. Here, the authors report sustainable biofilms for efficient, low-cost evaporation-based electricity production

Multifunctional Flexible Humidity Sensor Systems Towards Noncontact Wearable Electronics

In the past decade, the global industry and research attentions on intelligent skin-like electronics have boosted their applications in diverse fields including human healthcare, Internet of Things, human-machine interfaces, artificial intelligence and soft robotics. Among them, flexible humidity sensors play a vital role in noncontact measurements relying on the unique property of rapid response to humidity change. This work presents an overview of recent advances in flexible humidity sensors using various active functional materials for contactless monitoring. Four categories of humidity sensors are highlighted based on resistive, capacitive, impedance-type and voltage-type working mechanisms. Furthermore, typical strategies including chemical doping, structural design and Joule heating are introduced to enhance the performance of humidity sensors. Drawing on the noncontact perception capability, human/plant healthcare management, human-machine interactions as well as integrated humidity sensor-based feedback systems are presented. The burgeoning innovations in this research field will benefit human society, especially during the COVID-19 epidemic, where cross-infection should be averted and contactless sensation is highly desired.

Ionic liquid multistate resistive switching characteristics in two terminal soft and flexible discrete channels for neuromorphic computing

By exploiting ion transport phenomena in a soft and flexible discrete channel, liquid material conductance can be controlled by using an electrical input signal, which results in analog neuromorphic behavior. This paper proposes an ionic liquid (IL) multistate resistive switching device capable of mimicking synapse analog behavior by using IL BMIM FeCL₄ and H₂O into the two ends of a discrete polydimethylsiloxane (PDMS) channel. The spike rate-dependent plasticity (SRDP) and spike-timing-dependent plasticity (STDP) behavior are highly stable by modulating the input signal. Furthermore, the discrete channel device presents highly durable performance under mechanical bending and stretching. Using the obtained parameters from the proposed ionic liquid-based synaptic device, convolutional neural network simulation runs to an image recognition task, reaching an accuracy of 84%. The bending test of a device opens a new gateway for the future of soft and flexible brain-inspired neuromorphic computing systems for various shaped artificial intelligence applications.

Microbial nanowires

In this Quick guide, Derek Lovley introduces microbial nanowires-conductive extracellular appendages made by some bacteria and archaea.