Photo-Induced Multiple-State Switching of Polyoxometalate−Chromophore Hybrid Compounds: Charge- vs. Resistive-based Data Storage

• Applicants: Privatdozent Dr. Axel Kahnt; Dr. Kirill Monakhov
• Subject Area: Inorganic Molecular Chemistry - Synthesis and Characterisation ; Physical Chemistry of Molecules, Liquids and Interfaces, Biophysical Chemistry
• Term: since 2022
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 495326276

The cross-disciplinary project (acronym: PhotOcellMatrix) is aimed at exploring the multi-electron charge storage, transfer, and its switching for donor-acceptor organic-inorganic polyoxometalate (POM) hybrid compounds furnished with specific porphyrinoid chromophore moieties. These new assemblies will be prepared by wet-chemical synthesis and their structure-property-reactivity relationships be explored in solution and on conducting and semiconducting surfaces by using photons from an external light source and scanning tunneling microscope as the charge-state readout (see Chart below). The central research goals are twofold: (1) to give rise to light-controlled, large-area molecular multiple-state switching, and (2) to answer the question of the physical mechanism triggered by light in the designed memory cell setup - "Charge-based photo-capacitor-like or photo-induced Resistive Random Access Memory (ReRAM)-like system?" A formation of intermolecular porphyrinoid-directed radical chains as conductive filaments (a finger print of a practical ReRAM device) at the nanophotonic POM interfaces embedded in a polymer layer will be probed by electron beam radiolysis and laser photolysis at surfaces as well as Kelvin probe force microscope with and without photoexcitation. Local conductivity atomic force microscopy measurements will be used to elucidate the dimensions, the morphology, and the electronic properties of these filaments. The findings are expected to accelerate solutions for energy-efficient high-performance, molecule-based memristive switches that are envisioned to meet requirements of deep learning applications with neuromorphic nano-sized computer chips for the Internet of Things (IoT).

• DFG Programme: Research Grants