含三极的三核钯(II)复合物:自旋极化电子传输、模拟电阻开关和神经形态的进步。

IF 12.2 2区 材料科学 Q1 CHEMISTRY, MULTIDISCIPLINARY Materials Horizons Pub Date : 2024-10-29 DOI:10.1039/d4mh00928b
Prasenjit Sarkar, Litty Thomas Manamel, Puranjay Saha, Chinmay Jana, Amrit Sarmah, Kannan Udaya Mohanan, Bikas C Das, Chandan Mukherjee
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引用次数: 0

摘要

神经形态计算已成为解决传统计算中冯-诺依曼瓶颈问题的潜在替代方案。在此背景下,开发基于电阻开关的忆阻器器件来模拟各种突触功能引起了极大关注。在这里,我们报告了一种含三极的三核 Pd(II) 簇,其环己烷样框架由 Pd-Se 配位基团构成,作为一种薄膜器件显示出具有神经形态功能的简便记忆器特性。金属配体复合物(复合物 1)与钯(II)离子配位的三个配体基有机自由基(SR = 1/2)之间存在自旋受挫型磁耦合现象,因而具有 St = 1/2基态。在该复合物的循环伏安图中发现了三个可逆的单电子还原波与一个单电子和一个双电子可逆氧化波,证实了该复合物在低电位(即 +0.2 V 至 -1.1 V)下的电子接受和释放能力。利用夹在两个正交电极之间的含自由基复合物 1 作为活性薄膜,成功模拟了基于电阻开关的忆阻器特性和生物突触作用。有趣的是,人工神经网络(ANN)利用该装置记录的电位和抑制曲线模拟了高效的模式识别,这为神经形态计算的硬件实现迈出了一步。我们还进一步评估了 ANN 在图像分辨率降低的 MNIST 数据上的性能。基于密度泛函理论(DFT)的理论计算预测,自旋极化电子传输证实了中性复合物 1 的记忆特性。
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A triradical-containing trinuclear Pd(II) complex: spin-polarized electronic transmission, analog resistive switching and neuromorphic advancements.

Neuromorphic computation has emerged as a potential alternative to subvert the von Neumann bottleneck issue in conventional computing. In this context, the development of resistive switching-based memristor devices mimicking various synaptic functionalities has engendered paramount attention. Here, we report a triradical-containing trinuclear Pd(II) cluster with a cyclohexane-like framework constituted by the Pd-Se coordination motif displaying facile memristor property with neuromorphic functionality as a thin-film device. The metal-ligand complex (complex 1) possessed an St = 1/2 ground state by experiencing a spin-frustrated-type magnetic coupling phenomenon amongst the three ligand-based organic radicals (SR = 1/2), coordinated to the Pd(II) ions. Three reversible one-electron reduction waves countered with a one-electron and one two-electron reversible oxidation waves were noticed in the cyclic voltammogram of the complex, confirming electrons accepting and releasing capacity of the complex at low potentials, i.e., within +0.2 V to -1.1 V. Employing the radical-containing complex 1 as the active thin-film sandwiched between two orthogonal electrodes, resistive switching based memristor property with biological synaptic actions were successfully emulated. Intriguingly, the artificial neural network (ANN) simulated efficient pattern recognition demonstrated using the recorded potentiation and depression curves from the device, which is a step ahead for the hardware realization of neuromorphic computing. The performance of the ANN on MNIST data with reduced image resolution has further been evaluated. Density functional theory (DFT)-based theoretical calculation predicted that the spin-polarized electronic transmission substantiated the memristive property in the neutral complex 1.

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来源期刊
Materials Horizons
Materials Horizons CHEMISTRY, MULTIDISCIPLINARY-MATERIALS SCIENCE, MULTIDISCIPLINARY
CiteScore
18.90
自引率
2.30%
发文量
306
审稿时长
1.3 months
期刊介绍: Materials Horizons is a leading journal in materials science that focuses on publishing exceptionally high-quality and innovative research. The journal prioritizes original research that introduces new concepts or ways of thinking, rather than solely reporting technological advancements. However, groundbreaking articles featuring record-breaking material performance may also be published. To be considered for publication, the work must be of significant interest to our community-spanning readership. Starting from 2021, all articles published in Materials Horizons will be indexed in MEDLINE©. The journal publishes various types of articles, including Communications, Reviews, Opinion pieces, Focus articles, and Comments. It serves as a core journal for researchers from academia, government, and industry across all areas of materials research. Materials Horizons is a Transformative Journal and compliant with Plan S. It has an impact factor of 13.3 and is indexed in MEDLINE.
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