Contact-electrification-activated artificial afferents at femtojoule energy

AbstractLow power electronics endowed with artificial intelligence and biological afferent characters are beneficial to neuromorphic sensory network. Highly distributed synaptic sensory neurons are more readily driven by portable, distributed, and ubiquitous power sources. Here, we report a contact-electrification-activated artificial afferent at femtojoule energy. Upon the contact-electrification effect, the induced triboelectric signals activate the ion-gel-gated MoS2 postsynaptic transistor, endowing the artificial afferent with the adaptive capacity to carry out spatiotemporal recognition/sensation on external stimuli (e.g., displacements, pressures and touch patterns). The decay time of the synaptic device is in the range of sensory memory stage. The energy dissipation of the artificial afferents is significantly reduced to 11.9 fJ per spike. Furthermore, the artificial afferents are demonstrated to be capable of recognizing the spatiotemporal information of touch patterns. This work is of great significance for the construction of next-generation neuromorphic sensory network, self-powered biomimetic electronics and intelligent interactive equipment.

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Triboelectric nanogenerator based self-powered sensor for artificial intelligence

Nano Energy ◽

10.1016/j.nanoen.2021.105887 ◽

2021 ◽

Vol 84 ◽

pp. 105887

Author(s):

Yuankai Zhou ◽

Maoliang Shen ◽

Xin Cui ◽

Yicheng Shao ◽

Lijie Li ◽

...

Keyword(s):

Artificial Intelligence ◽

Triboelectric Nanogenerator ◽

Self Powered

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Long- and Short-Term Conductance Control of Artificial Polymer Wire Synapses

Polymers ◽

10.3390/polym13020312 ◽

2021 ◽

Vol 13 (2) ◽

pp. 312

Author(s):

Naruki Hagiwara ◽

Shoma Sekizaki ◽

Yuji Kuwahara ◽

Tetsuya Asai ◽

Megumi Akai-Kasaya

Keyword(s):

Artificial Intelligence ◽

Human Brain ◽

High Speed ◽

Conductive Polymer ◽

Long Term Potentiation ◽

Short Term ◽

Synaptic Functions ◽

Term Potentiation ◽

Artificial Brain ◽

External Stimuli

Networks in the human brain are extremely complex and sophisticated. The abstract model of the human brain has been used in software development, specifically in artificial intelligence. Despite the remarkable outcomes achieved using artificial intelligence, the approach consumes a huge amount of computational resources. A possible solution to this issue is the development of processing circuits that physically resemble an artificial brain, which can offer low-energy loss and high-speed processing. This study demonstrated the synaptic functions of conductive polymer wires linking arbitrary electrodes in solution. By controlling the conductance of the wires, synaptic functions such as long-term potentiation and short-term plasticity were achieved, which are similar to the manner in which a synapse changes the strength of its connections. This novel organic artificial synapse can be used to construct information-processing circuits by wiring from scratch and learning efficiently in response to external stimuli.

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2D Nanomaterials for Effective Energy Scavenging

Nano-Micro Letters ◽

10.1007/s40820-021-00603-9 ◽

2021 ◽

Vol 13 (1) ◽

Author(s):

Md Al Mahadi Hasan ◽

Yuanhao Wang ◽

Chris R. Bowen ◽

Ya Yang

Keyword(s):

Large Scale ◽

Waste Heat ◽

Material Selection ◽

Power Supplies ◽

Small Scale ◽

Energy Scavenging ◽

Power Sources ◽

Practical Applications ◽

2D Nanomaterials ◽

Self Powered

AbstractThe development of a nation is deeply related to its energy consumption. 2D nanomaterials have become a spotlight for energy harvesting applications from the small-scale of low-power electronics to a large-scale for industry-level applications, such as self-powered sensor devices, environmental monitoring, and large-scale power generation. Scientists from around the world are working to utilize their engrossing properties to overcome the challenges in material selection and fabrication technologies for compact energy scavenging devices to replace batteries and traditional power sources. In this review, the variety of techniques for scavenging energies from sustainable sources such as solar, air, waste heat, and surrounding mechanical forces are discussed that exploit the fascinating properties of 2D nanomaterials. In addition, practical applications of these fabricated power generating devices and their performance as an alternative to conventional power supplies are discussed with the future pertinence to solve the energy problems in various fields and applications.

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Artificial Intelligence of Things (AIoT) Enabled Virtual Shop Applications Using Self‐Powered Sensor Enhanced Soft Robotic Manipulator

Advanced Science ◽

10.1002/advs.202100230 ◽

2021 ◽

pp. 2100230

Author(s):

Zhongda Sun ◽

Minglu Zhu ◽

Zixuan Zhang ◽

Zhaocong Chen ◽

Qiongfeng Shi ◽

...

Keyword(s):

Artificial Intelligence ◽

Robotic Manipulator ◽

Self Powered

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Perspective of Smart Self-Powered Neuromorphic Sensor and their Challenges towards Artificial Intelligence for Next-Generation Technology

Materials Letters ◽

10.1016/j.matlet.2021.131541 ◽

2021 ◽

pp. 131541

Author(s):

Akhilesh Kumar Gupta ◽

Ravi Ranjan Kumar ◽

Advaita Ghosh ◽

Shu-Ping Lin

Keyword(s):

Artificial Intelligence ◽

Next Generation ◽

Self Powered ◽

Generation Technology

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Synchronous Physiological Electrical Fields

Exploring the Collective Unconscious in the Age of Digital Media - Advances in Psychology, Mental Health, and Behavioral Studies ◽

10.4018/978-1-4666-9891-8.ch004 ◽

2016 ◽

pp. 107-127

Author(s):

J.F. Pagel

Keyword(s):

Artificial Intelligence ◽

Cognitive Processing ◽

Current Knowledge ◽

Human Machine Interaction ◽

Similar Frequency ◽

Electrical Fields ◽

Electrical Potentials ◽

Interface Potential ◽

External Stimuli ◽

Machine Interaction

Humans utilize sensory and motor systems developed genetically, physically and socially for interfacing with our external environment. We use these same systems to interface in our interactions with artificial intelligence. There are other functioning central nervous system (CNS) systems, however, involved in cognitive processing for which the function and environmental interface is less clear. The synchronous physiologic electrical field system utilizes broadcast extracellular electrical fields for a wide variety of CNS functions. The operations of this system are usually non-conscious and most apparent during sleep (especially the conscious states of sleep that include dreaming), and un-focused waking. The electrical fields of this system are altered and affected by both internal and external stimuli. These fields can be monitored and analyzed by artificial intelligence (AI) systems, and independently of human input, AI systems can utilize similar frequency based electrical potentials to convey data, communicate, supply power, and to store memory. From both human and AI perspectives, these systems have the potential to function more fully in human/machine interaction. This chapter reviews our current knowledge as to function, current interactive approaches, and interface potential for these physiological electrical fields.

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