2D material inks have the potential to strongly impact printed electronics, offering exciting opportunities for flexible and wearable devices. However, their electrical performance is often hindered by the resistive nature of inter-nanosheet junctions within randomly assembled nanosheet networks, limiting their efficiency compared to individual nanosheets. Overcoming this challenge necessitates a comprehensive understanding of the conduction mechanisms governing charge transport in these networks. In this study, a water-based graphene ink is prepared via liquid-phase exfoliation (LPE), deposited onto Si/SiO₂ substrates through inkjet printing, and electrically characterized over a wide temperature range (80–400 K) following thermal annealing at different temperatures. To interpret the temperature-dependent conductivity, a Random Resistor Network (RRN) model is employed that accounts for spatial and energetic variability among nodes. With this approach low and high temperature transport regimes are effectively studied, identifying inter-flake and intra-flake hopping mechanisms and providing valuable insights into the factors governing charge transport. Using Monte Carlo simulations, the RRN model delivers statistically robust predictions while capturing temperature-dependent transitions and annealing effects, achieving excellent agreement with experimental observations.

Effects of Temperature Annealing on the Intrinsic Transport Mechanisms of Solution Processed Graphene Nanosheet Networks

Grillo, Alessandro
Writing – Original Draft Preparation
;
Pelella, Aniello
Investigation
;
Faella, Enver
Investigation
;
Passacantando, Maurizio
Investigation
;
Di Bartolomeo, Antonio
Writing – Review & Editing
;
2025

Abstract

2D material inks have the potential to strongly impact printed electronics, offering exciting opportunities for flexible and wearable devices. However, their electrical performance is often hindered by the resistive nature of inter-nanosheet junctions within randomly assembled nanosheet networks, limiting their efficiency compared to individual nanosheets. Overcoming this challenge necessitates a comprehensive understanding of the conduction mechanisms governing charge transport in these networks. In this study, a water-based graphene ink is prepared via liquid-phase exfoliation (LPE), deposited onto Si/SiO₂ substrates through inkjet printing, and electrically characterized over a wide temperature range (80–400 K) following thermal annealing at different temperatures. To interpret the temperature-dependent conductivity, a Random Resistor Network (RRN) model is employed that accounts for spatial and energetic variability among nodes. With this approach low and high temperature transport regimes are effectively studied, identifying inter-flake and intra-flake hopping mechanisms and providing valuable insights into the factors governing charge transport. Using Monte Carlo simulations, the RRN model delivers statistically robust predictions while capturing temperature-dependent transitions and annealing effects, achieving excellent agreement with experimental observations.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11386/4909675
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