Decoupling Junction and Nanosheet Transport in Graphene Networks via Simple DC Temperature‐Dependent Measurements

Printed nanosheet networks are important for various applications in electronics, sensing, and energy storage. Understanding charge transport in such systems requires separately assessing the contributions of both the nanosheets themselves as well as the inter-nanosheet junctions. However, achieving this using standard electrical characterization methods can be challenging. Here, a broadly applicable method is presented for the separation and extraction of the temperature-dependent junction resistance (R<sub>J</sub>) and nanosheet resistivity (ρ<sub>NS</sub>) by combining a simple theoretical model with temperature-dependent resistivity measurements on networks fabricated using different nanosheet sizes. R<sub>J</sub> is found to be the bottleneck for transport in networks composed of large, thick nanosheets, whereas ρ<sub>NS</sub> begins to dominate in networks composed of smaller, thinner nanosheets. The extracted ρ<sub>NS</sub> shows weak temperature dependence consistent with semiconducting behavior arising from a mixture of Bernal and rhombohedral layer stacking. R<sub>J</sub> exhibits a power-law dependence which is best described by inter-sheet hopping via delocalized states just above a disorder-induced mobility edge. This approach enables simultaneous quantification of junction and nanosheet transport in any nanomaterial network. It provides a simple yet powerful method to achieve an extensive understanding of the transport mechanisms, facilitating the design of optimized printed electronic devices.

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Publication Details

Journal
Small
Published
2025-11-06
DOI
https://doi.org/10.1002/smll.202509314
Citations
1
Primary Topic
Graphene research and applications
Type
article
Field-Weighted Citation Impact
0.52

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article

Decoupling Junction and Nanosheet Transport in Graphene Networks via Simple DC Temperature‐Dependent Measurements

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1 citations
Small
Graphene research and applications
0.52
article

Decoupling Junction and Nanosheet Transport in Graphene Networks via Simple DC Temperature‐Dependent Measurements

Jagdish Kumar Vij, Weimiao Wang, Jonathan N. Coleman, Cian Gabbett, Emmet Coleman, Eoin Caffrey, Luke Doolan, Anthony Dawson, Kevin Synatschke
article en
1 citations

Abstract

Printed nanosheet networks are important for various applications in electronics, sensing, and energy storage. Understanding charge transport in such systems requires separately assessing the contributions of both the nanosheets themselves as well as the inter-nanosheet junctions. However, achieving this using standard electrical characterization methods can be challenging. Here, a broadly applicable method is presented for the separation and extraction of the temperature-dependent junction resistance (R<sub>J</sub>) and nanosheet resistivity (ρ<sub>NS</sub>) by combining a simple theoretical model with temperature-dependent resistivity measurements on networks fabricated using different nanosheet sizes. R<sub>J</sub> is found to be the bottleneck for transport in networks composed of large, thick nanosheets, whereas ρ<sub>NS</sub> begins to dominate in networks composed of smaller, thinner nanosheets. The extracted ρ<sub>NS</sub> shows weak temperature dependence consistent with semiconducting behavior arising from a mixture of Bernal and rhombohedral layer stacking. R<sub>J</sub> exhibits a power-law dependence which is best described by inter-sheet hopping via delocalized states just above a disorder-induced mobility edge. This approach enables simultaneous quantification of junction and nanosheet transport in any nanomaterial network. It provides a simple yet powerful method to achieve an extensive understanding of the transport mechanisms, facilitating the design of optimized printed electronic devices.

SmallVol. 21(50)
Trinity College Dublin (IE), Advanced Materials and BioEngineering Research (IE), Technische Universität Dresden (DE)
Science Foundation Ireland, HORIZON EUROPE Framework Programme
Openalex Percentile: Top 31%
Graphene research and applications
0.52
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