Quantitative analysis of printed nanostructured networks using high-resolution 3D FIB-SEM nanotomography

Cian Gabbett, Luke Doolan, Kevin Synnatschke, Laura Gambini, Emmet Coleman, Adam G. Kelly, Shixin Liu, Eoin Caffrey, Jose Munuera, Catriona Murphy, Stefano Sanvito, Lewys Jones and Jonathan N. Coleman ()
Additional contact information
Cian Gabbett: Trinity College Dublin
Luke Doolan: Trinity College Dublin
Kevin Synnatschke: Trinity College Dublin
Laura Gambini: Trinity College Dublin
Emmet Coleman: Trinity College Dublin
Adam G. Kelly: Trinity College Dublin
Shixin Liu: Trinity College Dublin
Eoin Caffrey: Trinity College Dublin
Jose Munuera: Trinity College Dublin
Catriona Murphy: Trinity College Dublin
Stefano Sanvito: Trinity College Dublin
Lewys Jones: Trinity College Dublin
Jonathan N. Coleman: Trinity College Dublin

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Networks of solution-processed nanomaterials are becoming increasingly important across applications in electronics, sensing and energy storage/generation. Although the physical properties of these devices are often completely dominated by network morphology, the network structure itself remains difficult to interrogate. Here, we utilise focused ion beam – scanning electron microscopy nanotomography (FIB-SEM-NT) to quantitatively characterise the morphology of printed nanostructured networks and their devices using nanometre-resolution 3D images. The influence of nanosheet/nanowire size on network structure in printed films of graphene, WS2 and silver nanosheets (AgNSs), as well as networks of silver nanowires (AgNWs), is investigated. We present a comprehensive toolkit to extract morphological characteristics including network porosity, tortuosity, specific surface area, pore dimensions and nanosheet orientation, which we link to network resistivity. By extending this technique to interrogate the structure and interfaces within printed vertical heterostacks, we demonstrate the potential of this technique for device characterisation and optimisation.

Date: 2024
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DOI: 10.1038/s41467-023-44450-1

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