Dressing AgNWs with MXenes Nanosheets: Transparent Printed Electrodes Combining High‐Conductivity and Tunable Work Function for High‐Performance Opto‐Electronics

Advanced Materials, Journal Year: 2024, Volume and Issue: 36(48)

Published: Oct. 14, 2024

High-work function transparent electrodes (HWFTEs) are key for establishing Schottky and Ohmic contacts with n-type p-type semiconductors, respectively. However, the development of printable materials that combine high transmittance, low sheet resistance, tunable work remains an outstanding challenge. This reports a high-performance HWFTE composed Ag nanowires enveloped conformally by Ti

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 Computing Using Synaptic Plasticity of Supercapacitors

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

Published: March 24, 2025

Neuromorphic computing systems convert multimodal signals to electrical responses for artificial intelligence recognition. Energy is consumed during both the response enhancement and depression, making suffer from high energy consumption. This study presents a neuromorphic pathway based on supercapacitors. MXene Ti₃C₂Tx supercapacitors are fabricated current stimuli voltage responses. The depression tunable through adjusting charging discharging stimuli, thus exhibiting synaptic plasticity. Typical behaviors demonstrated, including short-term memory, long-term paired-pulse facilitation, learning experience. Next, used recognize Braille numbers represented by 3 × 4 arrays. A charging/discharging pulse train representing each array applied supercapacitor. collected converted 12-pixel greyscale images. Once images 0-9 input into neural networks deep diffraction networks, 100% accuracy can be achieved recognizing ten numbers. Because stored in supercapacitor released once declines, this research demonstrates potential applications of storage devices computing, providing an innovative way develop energy-efficient brain-like systems.

Language: Английский

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Responsive Molecules for Organic Neuromorphic Devices: Harnessing Memory Diversification

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

Published: March 26, 2025

In the brain, both recording and decaying of memory information following external stimulus spikes are fundamental learning rules that determine human behaviors. The former is essential to acquire new knowledge update database, while latter filters noise autorefresh cache data reduce energy consumption. To execute these functions, brain relies on different neuromorphic transmitters possessing various kinetics, which can be classified as nonvolatile volatile memory. Inspired by electronic devices have been employed realize artificial neural networks spiking networks, respectively, emerged tools in machine learning. Molecular switches, capable responding electrical, optical, electrochemical, magnetic stimuli, display a disruptive potential for emulating storage devices. This Review highlights recent developments responsive molecules, their interfacing with low-dimensional nanostructures nanomaterials, integration into By capitalizing concepts, unique account neurotransmitter-transfer based molecules ad hoc kinetics provided. Finally, future directions, challenges, opportunities discussed use engineer more complex logic operations computing functions at hardware level.

Language: Английский

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Asymmetric D-A-D’ type diphenylsulfone derivative modified by carbazole and tetraphenylethylene showing high solid-state luminescence efficiency, aggregation-induced emission and reversible mechanochromic luminescence

Journal of Molecular Structure, Journal Year: 2024, Volume and Issue: unknown, P. 140201 - 140201

Published: Sept. 1, 2024

Language: Английский

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Asymmetric D-a-D’ Type Diphenylsulfone Derivative Modified by Carbazole and Tetraphenylethylene Showing High Solid-State Luminescence Efficiency, Aggregation-Induced Emission and Reversible Mechanochromic Luminescence

Published: Jan. 1, 2024

Language: Английский

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Highly integrated optocoupler based on monolithic III-nitride diodes for on-chip data transfer

Optics & Laser Technology, Journal Year: 2024, Volume and Issue: 183, P. 112274 - 112274

Published: Dec. 12, 2024

Language: Английский

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