Memory-electroluminescence for multiple action-potentials combination in bio-inspired afferent nerves.

The development of optoelectronics mimicking the functions of the biological nervous system is important to artificial intelligence. This work demonstrates an optoelectronic, artificial, afferent-nerve strategy based on memory-electroluminescence spikes, which can realize multiple action-potentials combination through a single optical channel. The memory-electroluminescence spikes have diverse morphologies due to their history-dependent characteristics and can be used to encode distributed sensor signals. As the key to successful functioning of the optoelectronic, artificial afferent nerve, a driving mode for light-emitting diodes, namely, the non-carrier injection mode, is proposed, allowing it to drive nanoscale light-emitting diodes to generate a memory-electroluminescence spikes that has multiple sub-peaks. Moreover, multiplexing of the spikes can be obtained by using optical signals with different wavelengths, allowing for a large signal bandwidth, and the multiple action-potentials transmission process in afferent nerves can be demonstrated. Finally, sensor-position recognition with the bio-inspired afferent nerve is developed and shown to have a high recognition accuracy of 98.88%. This work demonstrates a strategy for mimicking biological afferent nerves and offers insights into the construction of artificial perception systems.

Silver Nanowire Networks with Moisture-Enhanced Learning Ability.

The human brain possesses a remarkable ability to memorize information with the assistance of a specific external environment. Therefore, mimicking the human brain's environment-enhanced learning abilities in artificial electronic devices is essential but remains a considerable challenge. Here, a network of Ag nanowires with a moisture-enhanced learning ability, which can mimic long-term potentiation (LTP) synaptic plasticity at an ultralow operating voltage as low as 0.01 V, is presented. To realize a moisture-enhanced learning ability and to adjust the aggregations of Ag ions, we introduced a thin polyvinylpyrrolidone (PVP) coating layer with moisture-sensitive properties to the surfaces of the Ag nanowires of Ag ions. That Ag nanowire network was shown to exhibit, in response to the humidity of its operating environment, different learning speeds during the LTP process. In high-humidity environments, the synaptic plasticity was significantly strengthened with a higher learning speed compared with that in relatively low-humidity environments. Based on experimental and simulation results, we attribute this enhancement to the higher electric mobility of the Ag ions in the water-absorbed PVP layer. Finally, we demonstrated by simulation that the moisture-enhanced synaptic plasticity enabled the device to adjust connection weights and delivery modes based on various input patterns. The recognition rate of a handwritten data set reached 94.5% with fewer epochs in a high-humidity environment. This work shows the feasibility of building our electronic device to achieve artificial adaptive learning abilities.

Highly Reliable Performance of Flexible Synaptic Devices Based on PVP-GO QD Nanocomposites Due to the Formation of Directional Filaments.

The metallic conductive filament (CF) model, which serves as an important conduction mechanism for realizing synaptic functions in electronic devices, has gained recognition and is the subject of extensive research. However, the formation of CFs within the active layer is plagued by issues such as uncontrolled and random growth, which severely impacts the stability of the devices. Therefore, controlling the growth of CFs and improving the performance of the devices have become the focus of that research. Herein, a synaptic device based on polyvinylpyrrolidone (PVP)/graphene oxide quantum dot (GO QD) nanocomposites is proposed. Doping GO QDs in the PVP provides a large number of active centers for the reduction of silver ions, which allows, to a certain extent, the growth of CFs to be controlled. Because of this, the proposed device can simulate a variety of synaptic functions, including the transition from long-term potentiation to long-term depression, paired-pulse facilitation, post-tetanic potentiation, transition from short-term memory to long-term memory, and the behavior of the "learning experience". Furthermore, after being bent repeatedly, the devices were still able to simulate multiple synaptic functions accurately. Finally, the devices achieved a high recognition accuracy rate of 89.39% in the learning and inference tests, producing clear digit classification results.

Electron Oscillation-Induced Splitting Electroluminescence from Nano-LEDs for Device-Level Encryption.

Data security is a major concern in digital age, which generally relies on algorithm-based mathematical encryption. Recently, encryption techniques based on physical principles are emerging and being developed, leading to the new generation of encryption moving from mathematics to the intersection of mathematics and physics. Here, device-level encryption with ideal security is ingeniously achieved using modulation of the electron-hole radiative recombination in a GaN-light-emitting diode (LED). When a nano-LED is driven in the non-carrier injection mode, the oscillation of confined electrons can split what should be a single light pulse into multiple pulses. The morphology (amplitude, shape, and pulse number) of those history-dependent multiple pulses that act as carriers for transmitted digital information depends highly on the parameters of the driving signals, which makes those signals mathematically uncrackable and can increase the volume and security of transmitted information. Moreover, a hardware and software platform are designed to demonstrate the encrypted data transmission based on the device-level encryption method, enabling recognition of the entire ASCII code table. The device-level encryption based on splitting electroluminescence provides an encryption method during the conversion process of digital signals to optical signals and can improve the security of LED-based communication.

Biocompatible memristive device based on an agarose@gold nanoparticle-nanocomposite layer obtained from nature for neuromorphic computing.

Natural, organic, materials-based artificial synaptic devices have been in the spotlight for wearable/flexible devices due to their lightweight, biocompatibility, and scalability. In this study, an electronic memristive device based on agarose extracted from plants in the Rhodophyceae class was fabricated, and its memory characteristics and analog data processing capabilities were evaluated. The Al/agarose@gold nanoparticle (AuNP) film/indium-tin-oxide (ITO)-structured memristive device exhibited reliable resistive switching characteristics with excellent retention with a large Ron/Roff ratio of 104. Also, analog conductance changes in our device were achieved with power consumption at the pJ level. This notable behavior could be maintained under mechanical deformations from a flat to a 4-mm bent state. In the recognition simulation based on the device's performance, an 91% accuracy and clear digit classification were achieved.

Directional Formation of Conductive Filaments for a Reliable Organic-Based Artificial Synapse by Doping Molybdenum Disulfide Quantum Dots into a Polymer Matrix.

The conductive filament (CF) model, as an important means to realize synaptic functions, has received extensive attention and has been the subject of intense research. However, the random and uncontrollable growth of CFs seriously affects the performances of such devices. In this work, we prepared a neural synaptic device based on polyvinyl pyrrolidone-molybdenum disulfide quantum dot (MoS2 QD) nanocomposites. The doping with MoS2 QDs was found to control the growth mode of Ag CFs by providing active centers for the formation of Ag clusters, thus reducing the uncertainty surrounding the growth of Ag CFs. As a result, the device, with a low power consumption of 32.8 pJ/event, could be used to emulate a variety of synaptic functions, including long-term potentiation (LTP), long-term depression (LTD), paired-pulse facilitation, post-tetanic potentiation, short-term memory to long-term memory conversion, and "learning experience" behavior. After having undergone consecutive stimulation with different numbers of pulses, the device stably realized a "multi-level LTP to LTD conversion" function. Moreover, the synaptic characteristics of the device experienced no degradation due to mechanical stress. Finally, the simulation result based on the synaptic characteristics of our devices achieved a high recognition accuracy of 91.77% in learning and inference tests and showed clear digital classification results.

Low Activation of Knee Extensors and High Activation of Knee Flexors in Female Fencing Athletes Is Related to the Response Time during the Marche-Fente.

Reaction time is important to determine the performance of fencing. The purpose of this study was to investigate the reaction time and muscle activity and compare the movement among genders during Marche-fente. Fifteen Korean national Fleuret fencing athletes participated and were instructed to perform Marche-fente. Reaction time was measured with Plug & Play equipment and muscle activation was obtained by electromyography. The male athletes (0.94 ± 0.08 s) were faster than female athletes, who a performance of 1.03 ± 0.05 s. As the knee extensors activation was increased, the movement and response time was shorter (rectus femoris (RF); r = −0.526, p < 0.05, vastus lateralis oblique (VLO); r = −0.628, p < 0.05). In phase 1, men activated more knee extensors in the dominant leg, whereas the activation of knee flexors was increased to maintain a stable posture in women. Additionally, women used other muscles instead of large muscles such as RF and VLO in phase 2. In conclusion, female athletes activated knee flexors rather than knee extensors when moving the center of mass or generating a greater force. Less use of knee extensors is associated with knee injuries; therefore, exercise which activates knee extensors is required for females.

Biocompatible artificial synapses based on a zein active layer obtained from maize for neuromorphic computing.

Artificial synaptic devices based on natural organic materials are becoming the most desirable for extending their fields of applications to include wearable and implantable devices due to their biocompatibility, flexibility, lightweight, and scalability. Herein, we proposed a zein material, extracted from natural maize, as an active layer in an artificial synapse. The synaptic device exhibited notable digital-data storage and analog data processing capabilities. Remarkably, the zein-based synaptic device achieved recognition accuracy of up to 87% and exhibited clear digit-classification results on the learning and inference test. Moreover, the recognition accuracy of the zein-based artificial synapse was maintained within a difference of less than 2%, regardless of mechanically stressed conditions. We believe that this work will be an important asset toward the realization of wearable and implantable devices utilizing artificial synapses.

Highly Self-Healable Write-Once-Read-Many-Times Devices Based on Polyvinylalcohol-Imidazole Modified Graphene Nanocomposites.

Repetitious mechanical stress or external mechanical impact can damage wearable electronic devices, leading to serious degradations in their electrical performances, which limits their applications. Because self-healing would be an excellent solution to the above-mentioned issue, this paper presents a self-healable memory device based on a novel nanocomposite layer consisting of a polyvinyl alcohol matrix and imidazole-modified graphene quantum dots. The device exhibits reliable electrical performance over 600 cycles, and the electrical properties of the device are maintained without any failure under this bending stress. Further, it is confirmed that the damaged device can recover its original electric characteristics after the self-healing process. It is believed that such outstanding results will lead the way to the realization of future wearable electronic systems.

Light-Emitting Memristors for Optoelectronic Artificial Efferent Nerve.

The central nervous system sends a neural impulse through an efferent nerve system toward muscles to drive movement. In an electronically artificial neural system, the electronic neural devices and interconnections prevent achieving highly connected and long-distance artificial impulse transmission and exhibit a narrow bandwidth. Here we design and demonstrate light-emitting memristors (LEMs) for the realization of an optoelectronic artificial efferent nerve, in which the LEM combines the functions of a light receiver, a light emitter, and an optoelectronic synapse in a single device. The optical signal from the pre-LEM (presynaptic membrane) acts as the input signal for the post-LEM (postsynaptic membrane), leading to one-to-many transmission, dynamic adjustable transmission, and light-trained synaptic plasticity, thus removing the physical limitation in artificially electronic neural systems. Furthermore, we construct an optoelectronic artificial efferent nerve with LEMs to control manipulators intelligently. These results promote the construction of an artificial optoelectronic nerve for further development of sensorimotor functionalities.

A Feasibility Study of Kinematic Characteristics on the Upper Body According to the Shooting of Elite Disabled Archery Athletes.

The purpose of this study is to compare and analyze the kinematic characteristics of the upper limb segments during the archery shooting of Paralympic Wheelchair Class archers (ARW2-second wheelchair class-paraplegia or comparable disability) and Paralympic Standing Class archers (ARST-standing archery class-loss of 25 points in the upper limbs or lower limbs), where archers are classified according to their disability grade among elite disabled archers. The participants of this study were selected as seven elite athletes with disabilities by the ARW2 (n = 4) and ARST (n = 3). The analysis variables were (1) the time required for each phase, (2) the angle of inclination of the body center, (3) the change of trajectory of body center, and (4) the change of the movement trajectory of the bow center by phase when performing six shots in total. The ARW2 group (drawing phase; M = 2.228 s, p < 0.05, holding phase; M = 4.414 s, p < 0.05) showed a longer time than the ARST group (drawing phase; M = 0.985 s, holding phase; M = 3.042 s), and the angle of the body did not show a significant difference between the two groups. Additionally, in the direction of the anteroposterior axis in the drawing phase, the change in the movement trajectory of the body center showed a more significant amount of change in the ARW2 group than in the ARST group, and the change in the movement trajectory of the bow center did not show a significant difference between the two groups.

Organic light-emitting devices based on conducting polymer treated with benzoic acid.

We report on the enhanced conductivity of the benzoic-acid-treated poly(3,4-ethlenedioxythiophene):poly(styrene sulfonate) (PEDOT:PSS) electrode for use in highly flexible, organic light-emitting devices (OLEDs). The conductivity of the benzoic-acid-treated PEDOT:PSS electrode increased from 1 to 1583.2 S/cm, in comparison with that of the pristine PEDOT:PSS electrode, due to a complex factor of the H+ mole % and the dielectric constant of the benzoic solution. Among the post-treatment methods of the PEDOT:PSS electrodes, the operating voltage at 1000 cd/m2 of OLEDs fabricated utilizing the PEDOT:PSS electrode with the benzoic acid treatment has the lowest value, and its maximum luminance is 24,400 cd/m2, which are 1.54 and 2.15 times higher than those of OLEDs using PEDOT:PSS electrodes treated with dimethyl sulfoxide and methanol, respectively. The luminance of a flexible OLED with a benzoic-acid-treated PEDOT:PSS electrode after 1400 bending cycles decreased to 83% of the initial luminance, resulting in excellent mechanical stability.

A Case Study on Curling Stone and Sweeping Effect According to Sweeping Conditions.

The purpose of this study was to use the same ice temperature and air temperature as the Pyeongchang Curling Stadium by using an Ice Chamber. Then, launch the stone at the same speed, and move according to the sweeping conditions (perpendicular to the axis of motion, along the axis of motion) of male and female elite curlers. The aim is to provide sports science information required for curling athletes by analyzing the distance of the stone, the change in the speed of a moved stone, the change in broom acceleration, and athletes' muscle activity. The results of experiments conducted on four male and four elite female curlers are as follows. Under gender, the stone's movement distance was long after the sweeping of male athletes, and the speed of the stone was not different according to the conditions. The broom's acceleration did not show a difference in both the sweeping condition and the athlete's gender condition, and the muscle activity did not show a significant difference in both the sweeping condition and the gender condition. In summary, it is thought that male athletes moved the stone farther by raising the ice surface temperature by vertical load than female athletes. Also, there was no statistically significant difference in muscle activity results, but it was found that there was a specific pattern of muscle activity in the pushing and pulling motions during the sweeping of male and female athletes. It is expected to be used as primary data.

Recent advances in energy-saving chemiresistive gas sensors: A review.

With the tremendous advances in technology, gas-sensing devices are being popularly used in many distinct areas, including indoor environments, industries, aviation, and detectors for various toxic domestic gases and vapors. Even though the most popular type of gas sensor, namely, resistive-based gas sensors, have many advantages over other types of gas sensors, their high working temperatures lead to high energy consumption, thereby limiting their practical applications, especially in mobile and portable devices. As possible ways to deal with the high-power consumption of resistance-based sensors, different strategies such as self-heating, MEMS technology, and room-temperature operation using especial morphologies, have been introduced in recent years. In this review, we discuss different types of energy-saving chemisresitive gas sensors including self-heated gas sensors, MEMS based gas sensors, room temperature operated flexible/wearable sensor and their application in the fields of environmental monitoring. At the end, the review will be concluded by providing a summary, challenges, recent trends, and future perspectives.

Bio-inspired smart electronic-skin based on inorganic perovskite nanoplates for application in photomemories and mechanoreceptors.

The development of artificial skin, such as electronic skin, is critical to emerging artificial intelligence systems. Electronic skins reported to date are mechanically flexible, and can detect various stimuli, but lack the ability to regulate themselves and learn information from the outside world. The integration of bio-inspired multifunction in a single electronic platform is critical to the development of e-skin systems. Here, we demonstrate a self-powered, light-stimulated, smart e-skin based on a photosensitive perovskite material. The electronic skin implements the functions of both tactile sensing and photoelectric neural computing. The strategy for developing such a material system and architecture of the electronic skin meets the requirement of multifunctional smart human-machine interfaces and has promising potential for application in future artificial intelligence systems.

Sustainable resistance switching performance from composite-type ReRAM device based on carbon Nanotube@Titania core-shell wires.

A novel nanocomposite-based non-volatile resistance switching random access memory device introducing single-walled carbon nanotube (SWCNT)@TiO2 core-shell wires was proposed for flexible electronics. The SWCNT was de-bundled by ultrasonication with sodium dodecylbenzene sulfonate (SDBS), and then the TiO2 skin layer on the SWCNT surface was successfully introduced by adding benzyl alcohol as a weak surfactant. The nanocomposite resistance switching layer was composed of the SWCNT@TiO2 core-shell wires and poly(vinyl alcohol) (PVA) matrix by a simple spin-coating method. The device exhibited reproducible resistance switching performance with a remarkably narrow distribution of operating parameters (VSET and VRESET were 2.63 ± 0.16 and 0.95 ± 0.11 V, respectively) with a large RON/ROFF ratio of 105 for 200 consecutive switching cycles. Furthermore, the excellent resistance switching behavior in our device was maintained against mechanical stress up to 105 bending test. We believe that the nanocomposite memory device with SWCNT@TiO2 core-shell wires would be a critical asset to realize practical application for a flexible non-volatile memory field.

Highly Stable and Flexible Memristive Devices Based on Polyvinylpyrrolidone: WS2 Quantum Dots.

Tungsten disulfide (WS2) quantum dots (QDs) embedded in polyvinylpyrrolidone (PVP) based flexible memristive devices were prepared, and those devices exhibited typical bistable electrical switching and remarkable nonvolatile memristive behaviors. Maximum electricity ON/OFF ratio obtained from the current-voltage (I-V) curves of the device is close to 104. The set voltage of the device is +0.7 V, which effectively reduced the energy consumption. The retention times extracted from data for the devices were as large as 1 × 104 s, which points to these devices having nonvolatile characteristics. Moreover, the highly flexible characteristics of the devices were demonstrated by bending the devices. The carrier transport mechanisms were explained by fitting the I-V curves, and possible operating mechanisms of the devices can be described based on the electron trapping and detrapping processes. WS2 QDs uniformly dispersed in pure transparent N, N-Dimethylformamide (DMF) were obtained by using ultrasonication and a hydrothermal process in this work.

Binary Electronic Synapses for Integrating Digital and Neuromorphic Computation in a Single Physical Platform.

As a promising advanced computation technology, the integration of digital computation with neuromorphic computation into a single physical platform holds the advantage of a precise, deterministic, fast data process as well as the advantage of a flexible, paralleled, fault-tolerant data process. Even though two-terminal memristive devices have been respectively proved as leading electronic elements for digital computation and neuromorphic computation, it is difficult to steadily maintain both sudden-state-change and gradual-state-change in a single device due to the entirely different operating mechanisms. In this work, we developed a digital-analog compatible memristive device, namely, binary electronic synapse, through realizing controllable cation drift in a memristive layer. The devices feature nonvolatile binary memory as well as artificial neuromorphic plasticity with high operation endurance. With strong nonlinearity in switching dynamics, binary switching, neuromorphic plasticity, two-dimension information store, and trainable memory can be implemented by a single device.

Biosynaptic devices based on chicken egg albumen:graphene quantum dot nanocomposites.

Biosynaptic devices based on chicken egg albumen (CEA):graphene quantum dot (GQD) hybrid nanocomposites were fabricated to achieve stable synaptic behaviors. Current-voltage (I-V) curves for the biosynaptic devices under consecutive negative and positive voltage sweeps showed clockwise pinched hysteresis, which is a critical feature of a biological synapse. The effect of the GQD concentration in the CEA layer on the device performance was studied. The retention time of the biosynaptic devices was relatively constant, maintaining a value above 104 s under ambient conditions. The carrier transport mechanisms of the biosynaptic devices were described and analyzed on the basis of the slopes of the I-V curves and their fittings.

Self-Powered Tactile Sensor with Learning and Memory.

Fabrication of human-like intelligent tactile sensors is an intriguing challenge for developing human-machine interfaces. As inspired by somatosensory signal generation and neuroplasticity-based signal processing, intelligent neuromorphic tactile sensors with learning and memory based on the principle of a triboelectric nanogenerator are demonstrated. The tactile sensors can actively produce signals with various amplitudes on the basis of the history of pressure stimulations because of their capacity to mimic neuromorphic functions of synaptic potentiation and memory. The time over which these tactile sensors can retain the memorized information is alterable, enabling cascaded devices to have a multilevel forgetting process and to memorize a rich amount of information. Furthermore, smart fingers by using the tactile sensors are constructed to record a rich amount of information related to the fingers' current actions and previous actions. This intelligent active tactile sensor can be used as a functional element for artificial intelligence.