Mimicking Axon Growth and Pruning by Photocatalytic Growth and Chemical Dissolution of Gold on Titanium Dioxide Patterns

Biological neural circuits are based on the interplay of excitatory and inhibitory events to achieve functionality. Axons form long-range information highways in neural circuits. Axon pruning, i.e., the removal of exuberant axonal connections, is essential in network remodeling. We propose the photo...

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Bibliographic Details
Main Authors: Fatemeh Abshari, Moritz Paulsen, Salih Veziroglu, Alexander Vahl, Martina Gerken
Format: Article
Language:English
Published: MDPI AG 2024-12-01
Series:Molecules
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Online Access:https://www.mdpi.com/1420-3049/30/1/99
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Summary:Biological neural circuits are based on the interplay of excitatory and inhibitory events to achieve functionality. Axons form long-range information highways in neural circuits. Axon pruning, i.e., the removal of exuberant axonal connections, is essential in network remodeling. We propose the photocatalytic growth and chemical dissolution of gold lines as a building block for neuromorphic computing mimicking axon growth and pruning. We predefine photocatalytic growth areas on a surface by structuring titanium dioxide (TiO<sub>2</sub>) patterns. Placing the samples in a gold chloride (HAuCl<sub>4</sub>) precursor solution, we achieve the controlled growth of gold microstructures along the edges of the indium tin oxide (ITO)/TiO<sub>2</sub> patterns under ultraviolet (UV) illumination. A potassium iodide (KI) solution is employed to dissolve the gold microstructures. We introduce a real-time monitoring setup based on an optical transmission microscope. We successfully observe both the growth and dissolution processes. Additionally, scanning electron microscopy (SEM) analysis confirms the morphological changes before and after dissolution, with dissolution rates closely aligned to the growth rates. These findings demonstrate the potential of this approach to emulate dynamic biological processes, paving the way for future applications in adaptive neuromorphic systems.
ISSN:1420-3049