期刊
ADVANCED FUNCTIONAL MATERIALS
卷 30, 期 45, 页码 -出版社
WILEY-V C H VERLAG GMBH
DOI: 10.1002/adfm.202002882
关键词
artificial synapses; chemical cross-link; flexible electronics; keratin; memristors
类别
资金
- China Scholarship Council
- NUS AcRF Tier 1 [R-144-000-416-114]
- 111 project [B16029]
- Doctoral Fund of the Ministry of Education [20130121110018]
- Science and Technology Project of Xiamen City [3502Z20183012]
- Science and Technology Planning Project of Guangdong Province [2018B030331001]
- Shenzhen Science and technology plan project [JCYJ20180504170208402]
- Natural Science Foundation of China [61306098, 61674050, 61422407, 61874158]
- Natural Science Foundation of Hebei Province [E2012201088, E2013201176]
- Science Research Program of University in Hebei Province [ZH2012019]
- project of enhancement comprehensive strength of the Midwest universities of Hebei University
- Outstanding Youth Project of Hebei Province [F2016201220]
Designing suitable material systems to construct artificial synapses and exploring novel synaptic functions is a crucial step toward the realization of efficient large-scale bioinspired neuromorphic systems. In this work, flexible and insoluble bio-memristor devices are fabricated by precisely engineering the molecular structures of wool keratin. This flexible Ag/keratin/indium tin oxide-polyethylene naphthalate synaptic device possesses enhanced mechanical resistance, which is achieved by photo-cross-linking keratin molecules, and can withstand a bending radius of up to 1.2 mm. This device is promising for implantable applications because it is water-resistant. When modulated by triangle-wave DC voltages and pulsed voltages, this flexible electronic device emulates typical memristor characteristics and synaptic functions, including potentiation/depression, spike timing dependent plasticity, and long-term/short-term plasticity. Simulation results indicate that a memristor network made by this wool-keratin based device has approximate to 95.8% memory learning accuracy and capability for pattern learning. Combined, these features prove that the cross-linked wool-keratin based device has potential in wearable and flexible neuron computing systems.
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