Work Description

Title: Data for Layer-by-Layer Assembled Nanowire Networks Enable Graph-Theoretical Design of Multifunctional Coatings Open Access Deposited

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Methodology
  • Coatings were synthesized and characterized by layer-by-layer assembly. They were imaged with electron and atomic force microscopy. These images were used to build graph theoretic (GT) models, using our StructuralGT package, available at the COMPASS-STC GitHub organization:  https://github.com/compass-stc. Experimental results, microscopy images, scripts for GT models, and notebooks for figure plotting are all included in this dataset.
Description
  • The goal of this project is to relate properties of nanowire networks to their structure. The structure of these networks was determined from electron and atomic force microscopy, which were used as the basis for property predictions. Properties include sheet resistance, conductive anisotropy, absorption spectra, and current capacity.
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Depositor
Contact information
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Funding agency
  • National Science Foundation (NSF)
ORSP grant number
  • Project/Grant # F070420
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Date coverage
  • 2022-05-01 to 2024-05-01
Citations to related material
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Last modified
  • 10/09/2024
Published
  • 10/09/2024
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DOI
  • https://doi.org/10.7302/23mt-jr71
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To Cite this Work:
Wu, W., Kadar, A., Lee, S. H., Jung, H. J., Park, B. C., Raymond, J., Tsotsis, T., Cesnik, C., Glotzer, S., Goss, V., Kotov, N. (2024). Data for Layer-by-Layer Assembled Nanowire Networks Enable Graph-Theoretical Design of Multifunctional Coatings [Data set], University of Michigan - Deep Blue Data. https://doi.org/10.7302/23mt-jr71

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Date: 27 September, 2024

Dataset Title: Layer-by-Layer Assembled Nanowire Networks Enable Graph-Theoretical Design of Multifunctional Coatings

Dataset Creators: Alain Kadar, Wenbing Wu

Dataset Contact: Alain Kadar [email protected]

Key Points:
- We experimentally characterise films synthesised by layer-by-layer assembly.
- We image their structure with electron microscopy.
- We calculate experimental properties using a graph-theoretic model

Abstract:
Complex multifunctional coatings combining order and disorder are central for information, biomedical, transportation and energy technologies. Their scalable fabrication is possible using nanostructured composites made by layer-by-layer assembly (LBL). Here, we show that structural description encompassing their non-random disorder and related property-focused design are possible using graph theory (GT). Two-dimensional images of LBL films of silver and gold nanowires (NWs) were used to calculate GT representations. We found that random stick computational models often used to describe NW, nanofiber, and nanotube materials give inaccurate predictions of their structure. Concurrently, image-informed GT models, accurately predict the structure and properties of the LBL films including the unexpected non-linearity of charge transport vs LBL cycles. The conductivity anisotropy in LBL composites, not readily detectable from microscopy, was accurately predicted using GT models. Spray-assisted LBL offers direct translation of the GT predictions to additive, scalable coatings for drones and potentially other technologies.

Folders and their contents contained here:
- Experimental:
* Contains data for sheet resistance, current carrying capacity, discharge time, UV-Vis spectra, Terahertz, Kapton, 12 inch sample.
- Computational:
* Contains scripts for generating results in the main text Figures. Scripts will generate relevant files which can be used to plot figures in the jupyter notebook.
* To run the scripts, the env.yml file can be used to recreate the conda environment which was used.
* Computational/Figure1 contains a notebook for generating results. The notebook analyses the images in the other 3 directories in Computational/Figure1 (AgNWN, Au, RandomStick) with StructuralGT to produce the results. For details on how StructuralGT works, visit the StructuralGT-Examples repository at the COMPASS-STC GitHub organization at https://github.com/compass-stc.
* Computational/Figure2 and Computational/Figure3 contain python files for generating results. The other directories contain the images used by StructuralGT to produce the results.

To use:
- Experimental results can be replotted with Microsoft Excel or another xlsx file interpreter.
- GT results generation requires a python interpreter and the StructuralGT package, which can be accessed from the COMPASS-STC GitHub organization at https://github.com/compass-stc.
- To replot these figures, the provided jupyter notebook can be used, which contains further instructions.

Related publication(s):
Structural Analysis of Nanoscale Network Materials Using Graph Theory, Drew A. Vecchio, Samuel H. Mahler, Mark D. Hammig, and Nicholas A. Kotov, ACS Nano 2021 15 (8), 12847-12859, 10.1021/acsnano.1c04711.

Use and Access:
This data set is made available under a Attribution 4.0 International (CC BY 4.0) license.

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