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Investigation on Junction Contacts of Semiconducting Carbon Nanotube Networks Using Conductive Atomic Force Microscopy.

Zebin LiuXiaoxiao GuanBingxian LiHuimin YinChuan-Hong Jin
Published in: ACS applied materials & interfaces (2024)
Semiconductor single-walled carbon nanotube (s-SWNT) networks have gained prominence in electronic devices due to their cost-effectiveness, relatively production-naturality, and satisfactory performance. Configuration, density, and resistance of SWNT-SWNT junctions are considered crucial factors influencing the overall conductivity of s-SWNT networks. In this study, we present a method for inferring the lower bounds of the SWNT-SWNT junction resistance in s-SWNT networks based on conductive atomic force microscopy TUNA images. This method further enables the proposal of a classification for SWNT-SWNT junctions based on the current behavior relative to their surroundings. The three types of SWNT-SWNT junctions are denoted as (i) true contact (T), (ii) poor contact (P), and (iii) false contact (F). Of them, the true and poor contacts, respectively, represent good and poor electrical contact for the subject SWNT-SWNT junctions whose electrical conductivity hardly improves under external tip pressure, while that of the false contact can be further improved by external pressure. Statistical analysis demonstrates that while T-type junctions make a significant contribution to network conductivity, their proportion accounts for only approximately 40%. The P-type and F-type junctions, which constitute over 60% of the total, may be a contributing factor that constrains the overall conductivity of the s-SWNT networks. The height ratio of the junction to the sum of two SWNTs was also observed to exhibit variations among the three types. Finally, we propose a three-dimensional model to elucidate the formation mechanism underlying each type of junction. The present study provides insights into the performance of spontaneous contacts between s-SWNTs in the networks, and the systematic image acquisition and junction classification processes may provide support for future advancements in these networks.
Keyphrases
  • single molecule
  • atomic force microscopy
  • carbon nanotubes
  • deep learning
  • machine learning
  • high speed
  • reduced graphene oxide
  • current status