3 research outputs found

    Three new Anthraquinones, one new Benzochromene and one new Furfural glycoside from <i>Lasianthus acuminatissimus</i>

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    <p>Three new anthraquinones, lasianthurin B (1), C (2), lasianthuoside D (3), a new benzochromene, lasianthurin D (4), and a new furfural glycoside, lasianthuoside E (5), together with one known compound 4- hydroxymethyl-2-furaldehyde (6) were isolated from an alcohol extract of the root of <i>Lasianthus acuminatissimus</i>. Their structures were elucidated on the basis of extensive spectroscopic data analysis (including 1D, 2D NMR, X-ray, and MS experiments) and comparsion to literature data.</p

    Competition between Metallic and Vacancy Defect Conductive Filaments in a CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub>‑Based Memory Device

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    Ion migration, which can be classified into cation migration and anion migration, is at the heart of redox-based resistive random access memory. However, the coexistence of these two types of ion migration and the resultant conductive filaments (CFs) have not been experimentally demonstrated in a single memory cell. Here we investigate the competition between metallic and vacancy defect CFs in a Ag/CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub>/Pt structure, where Ag and CH<sub>3</sub>NH<sub>3</sub>PbI<sub>3</sub> serve as the top electrode and memory medium, respectively. When the medium layer thickness is hundreds of nanometers, the formation/diffusion of iodine vacancy (V<sub>I</sub>) CFs dominates the resistive switching behaviors. The V<sub>I</sub>-based CFs provide a unique opportunity for the electrical-write and optical-erase operation in a memory cell. The Ag CFs emerge and coexist with V<sub>I</sub> ones as the medium layer thickness is reduced to ∼90 nm. Our work not only enriches the mechanisms of the resistive switching but also would advance the multifunctionalization of resistive random access memory

    Fully memristive neural networks for pattern classification with unsupervised learning

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    Neuromorphic computers comprised of artificial neurons and synapses could provide a more efficient approach to implementing neural network algorithms than traditional hardware. Recently, artificial neurons based on memristors have been developed, but with limited bio-realistic dynamics and no direct interaction with the artificial synapses in an integrated network. Here we show that a diffusive memristor based on silver nanoparticles in a dielectric film can be used to create an artificial neuron with stochastic leaky integrate-and-fire dynamics and tunable integration time, which is determined by silver migration alone or its interaction with circuit capacitance. We integrate these neurons with nonvolatile memristive synapses to build fully memristive artificial neural networks. With these integrated networks, we experimentally demonstrate unsupervised synaptic weight updating and pattern classification
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