| Download | - View accepted manuscript: Analysis method for quantifying the morphology of nanotube networks (PDF, 1.2 MiB)
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| DOI | Resolve DOI: https://doi.org/10.1021/acs.langmuir.6b02475 |
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| Author | Search for: Vobornik, Dusan1; Search for: Zou, Shan1; Search for: Lopinski, Gregory P.1 |
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| Affiliation | - National Research Council of Canada. Measurement Science and Standards
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| Format | Text, Article |
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| Abstract | While atomic force microscopy (AFM) is a powerful technique for imaging assemblies and networks of nanoscale materials, approaches for quantitative assessment of the morphology of these materials are lacking. Here we present a volume-based approach for analyzing AFM images of assemblies of nano-objects that enables the extraction of relevant parameters describing their morphology. Random networks of single-walled carbon nanotubes (SWCNTs) deposited via solution-phase processing are used as an example to develop the method and demonstrate its utility. AFM imaging shows that the morphology of these networks depends on details of processing and is influenced by choice of substrate, substrate cleaning method, and postdeposition rinsing protocols. A method is outlined to analyze these images and extract relevant parameters describing the network morphology such as the density of SWCNTs and the degree to which tubes are bundled. Because this volume-based approach depends on accurate measurements of the height of individual tubes and their networks, a procedure for obtaining reliable height measurements is also discussed. Obtaining quantitative parameters that describe the network morphology allows going beyond qualitative descriptions of images and will facilitate optimizing network preparation methods based on measurable criteria and correlating performance with morphology. |
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| Publication date | 2016-08-10 |
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| Publisher | American Chemical Society |
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| In | |
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| Language | English |
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| Peer reviewed | Yes |
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| NPARC number | 23000921 |
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| Export citation | Export as RIS |
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| Report a correction | Report a correction (opens in a new tab) |
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| Record identifier | 2aa69af3-2e4c-4587-90a4-b412e0dee323 |
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| Record created | 2016-11-15 |
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| Record modified | 2020-06-04 |
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