Wearable Pressure Sensor Based on Triboelectric Nanogenerator for Information Encoding, Gesture Recognition, and Wireless Real‐Time Robot Control

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

Abstract Wearable sensor has attracted a broad interesting in application prospect of human‐machine interaction (HMI). However, most sensors are assembled the shape gloves to accurately capture complex hand motion information, thereby seriously blocking complete tasks. Herein, wearable pressure based on drum‐structured triboelectric nanogenerator (DS‐TENG) is developed subtle signals for physiological signal detection, information encoding, gesture recognition, and wireless real‐time robot control. The DS‐TENG enables limit detection down 3.9 Pa pressure, which can sensitively human micromotion pulse, throat sounds, wrist muscles contraction. Especially, combined with microprocessor Morse code, worn detect single‐finger translate into regular voltage signals, employed encode 26 letters subsequently decode corresponding letters. Furthermore, an aid machine learning, array (2 × 2) successfully achieve recognition high accuracy 92% wirelessly perform Consequently, encoding control, demonstrates extreme potential field HMI artificial intelligence.

Language: Английский

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Bionic Recognition Technologies Inspired by Biological Mechanosensory Systems

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 21, 2025

Abstract Mechanical information is a medium for perceptual interaction and health monitoring of organisms or intelligent mechanical equipment, including force, vibration, sound, flow. Researchers are increasingly deploying recognition technologies (MIRT) that integrate acquisition, pre‐processing, processing functions expected to enable advanced applications. However, this also poses significant challenges acquisition performance efficiency. The novel exciting mechanosensory systems in nature have inspired us develop superior bionic (MIBRT) based on materials, structures, devices address these challenges. Herein, first strategies pre‐processing presented their importance high‐performance highlighted. Subsequently, design considerations sensors by mechanoreceptors described. Then, the concepts neuromorphic summarized order replicate biological nervous system. Additionally, ability MIBRT investigated recognize basic information. Furthermore, further potential applications robots, healthcare, virtual reality explored with view solve range complex tasks. Finally, future opportunities identified from multiple perspectives.

Language: Английский

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Self-Adhesive, Stretchable, and Thermosensitive Iontronic Hydrogels for Highly Sensitive Neuromorphic Sensing–Synaptic Systems

Nano Letters, Journal Year: 2024, Volume and Issue: 24(33), P. 10265 - 10274

Published: Aug. 8, 2024

Artificial sensory afferent nerves that emulate receptor nanochannel perception and synaptic ionic information processing in chemical environments are highly desirable for bioelectronics. However, challenges persist achieving life-like nanoscale conformal contact, agile multimodal sensing response, feedback with ions. Here, a precisely tuned phase transition poly(

Language: Английский

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Emerging Artificial Synaptic Devices Based on Organic Semiconductors: Molecular Design, Structure and Applications

ACS Applied Materials & Interfaces, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

In modern computing, the Von Neumann architecture faces challenges such as memory bottleneck, hindering efficient processing of large datasets and concurrent programs. Neuromorphic inspired by brain's architecture, emerges a promising alternative, offering unparalleled computational power while consuming less energy. Artificial synaptic devices play crucial role in this paradigm shift. Various material systems, from organic to inorganic, have been explored for neuromorphic devices, with materials attracting attention their excellent photoelectric properties, diverse choices, versatile preparation methods. Organic semiconductors, particular, offer advantages over transition-metal dichalcogenides, including ease flexibility, making them suitable large-area films. This review focuses on emerging artificial based discussing different branches within semiconductor system, various fabrication methods, device structure designs, applications synapse. Critical considerations achieving truly human-like dynamic perception systems semiconductors are also outlined, reflecting ongoing evolution computing.

Language: Английский

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Neuromorphic Hardware for Artificial Sensory Systems: A Review

Journal of Electronic Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 10, 2025

Abstract Senses are crucial for an organism’s survival, and there have been numerous efforts to artificially replicate sensory perception elicit desired responses specific stimuli. Recent research is increasingly focused on developing artificial nervous systems based the unsupervised learning capabilities of neural networks (ANNs) using unstructured data. However, future ANNs, which require precise sensing in complex environments, must be capable processing a large number signals real time, ideally from continuous domains. This need massive data driving evolution hardware systems, leading development devices specifically designed (ASSs) at level. To address this challenge, sensor not only detect target substances but also enable computational functions by utilizing their inherent material properties. Research neuromorphic sensors advancing towards integration with next-generation effectively addressing scenarios we aim identify. review offers perspectives human-like computing these challenges. It examines progress implementing five representative senses device level, explores methods integrating them into ASS, provides comprehensive overview potential applications. In particular, emphasize approaches cognitively utilize discussed as neurons synapses, enabling inputs. We offer nerve future.

Language: Английский

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Electrolyte Gated Transistors for Brain Inspired Neuromorphic Computing and Perception Applications: A Review

Nanomaterials, Journal Year: 2025, Volume and Issue: 15(5), P. 348 - 348

Published: Feb. 24, 2025

Emerging neuromorphic computing offers a promising and energy-efficient approach to developing advanced intelligent systems by mimicking the information processing modes of human brain. Moreover, inspired high parallelism, fault tolerance, adaptability, low power consumption brain perceptual systems, replicating these efficient at hardware level will endow artificial intelligence (AI) engineering with unparalleled appeal. Therefore, construction devices that can simulate neural synaptic behaviors are crucial for achieving perception computing. As novel memristive devices, electrolyte-gated transistors (EGTs) stand out among numerous due their unique interfacial ion coupling effects. Thus, present review discusses applications EGTs in electronics. First, operational discussed briefly. Second, advancements biological synapses/neurons functions introduced. Next, utilizing discussed. Finally, brief outlook on future developments challenges is presented.

Language: Английский

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Mxenes for Wearable Multifunctional Sensing and Artificial Intelligence Devices

IntechOpen eBooks, Journal Year: 2025, Volume and Issue: unknown

Published: March 28, 2025

The exponential growth of artificial intelligence (AI) has led to an escalating demand for energy-efficient, data-intensive computing solutions. Conventional von Neumann architectures, constrained by inherent memory-processor bottlenecks, struggle meet these requirements. Neuromorphic devices enable scalable, and high-speed neuromorphic computing, potentially addressing the bottleneck limits Moore’s Law. Two-dimensional MXene materials, with their excellent mechanical electrical properties, have become a transformative platform developing devices, providing unparalleled advantages in sensing, nonvolatile memory, bio-inspired computation. This chapter systematically summarizes recent advances MXene-based flexible memristor devices. First, we delineate materials engineering strategies synthesizing thin films tailored electronic properties. Next, classify MXene-derived elucidate switching mechanisms, including ion migration charge trapping. A critical analysis MXene-enabled highlights breakthroughs in-memory, synapses, circuits, multimodal in-sensor computing. Finally, discuss persistent challenges stability, scalability, interfacial engineering, while projecting future directions MXene-integrated sensing-memory-processing systems. provides potential pathway leveraging MXenes transcend limitations conventional paradigms.

Language: Английский

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Flexible Tunable‐Plasticity Synaptic Transistors for Mimicking Dynamic Cognition and Reservoir Computing

Advanced Materials, Journal Year: 2025, Volume and Issue: unknown

Published: April 9, 2025

Abstract Inspired by biological systems, neuromorphic computing can process extensive data and complex tasks more efficiently than traditional architectures. Artificial synaptic devices, serving as fundamental components in computing, needto closely mimic characteristics construct neural network systems. However, most existing multifunctional synapse devices are structurally lack tunability, making them unsuitable for building smarter In this work, a flexible tunable‐plasticity transistor (TST) is realized with memory modulation capabilities using indium gallium zinc oxide channel hybrid layer of polyimide Al 2 O 3 dielectric. The TST exhibits novel transition from short‐term plasticity to long‐term one adjusting stimulus amplitude, mirroring dynamic human forgetting behaviors across various scenarios. A system low non‐linearity wide range conductance variations constructed, it demonstrates 94.1% recognition rate on classical datasets. reservoir 4‐bit coding also developed, which significantly reduces computational complexity size without sacrificing accuracy. the work foundation intelligent efficient

Language: Английский

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Integration of Perovskite/Low‐Dimensional Material Heterostructures for Optoelectronics and Artificial Visual Systems

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: April 14, 2025

Abstract Heterojunctions combining halide perovskites with low‐dimensional materials are revolutionizing optoelectronic device design by leveraging complementary properties. Halide perovskites, known for their tunable bandgaps, excellent light‐harvesting, and efficient charge carrier mobility, provide a robust foundation photodetectors (PDs) imaging sensors. Low‐dimensional contribute ultrafast enhanced light‐matter interactions, mechanical flexibility. When integrated into heterostructures, these enable precise control over dynamics, leading to significant improvements in efficiency, stability, response speed. This synergy addresses critical challenges optoelectronics, advancing flexible electronics, wearable sensors, high‐sensitivity systems. Ongoing advancements interface engineering material synthesis continually enhancing the reliability operational efficacy of devices across various environmental conditions. Additionally, heterostructures show substantial promise neuromorphic computing, where properties support energy‐efficient, event‐driven data processing. By mimicking adaptive hierarchical nature biological visual systems, they offer new possibilities real‐time image analysis intelligent decision‐making. review highlights latest developments perovskite‐based heterojunctions transformative role bridging gap between artificial vision, driving technologies such as robotics bio‐inspired

Language: Английский

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Robust Neuromorphic Computing Enabled by Femtosecond Laser‐Modulated Divergent Ion Dynamics in CuInP₂S₆

Advanced Functional Materials, Journal Year: 2025, Volume and Issue: unknown

Published: April 26, 2025

Abstract Inspired by the high efficiency and robustness of human brains, ion‐based artificial synaptic devices have been widely explored for neuromorphic computing. However, achieving stability reliable functionality without additional components remains challenging due to inherent variability ion dynamics. Herein, homeostatic plasticity is demonstrated in a CuInP 2 S 6 (CIPS) synapse via femtosecond laser treatment. The laser‐induced modulation exhibits state‐dependent effects, enhancing ionic mobility low conductance state (activation) redistributing ions (inhibition). activation effect facilitates short‐term improving postsynaptic current, while inhibition enhances long‐term through reducing switching variability. Furthermore, tunable two‐terminal CIPS device synergistically achieved combined influence an electric field, optical illumination, Finally, spiking neural network simulation based on single representative demonstrated, improvement accuracy Modified National Institute Standards Technology (MNIST) handwritten digit recognition from 92.5% 98.2% modulation. This work presents novel strategy efficiently manipulating dynamics 2D van der Waals ferroionic materials, contributing development adaptive robust computing systems.

Language: Английский

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