Microscopy-Guided 3D Reconstruction of Nano-Dendrites in Biosensors

Sue Yuan Fan, Yi Pin Huang, Sucharita Khuntia, Jen Wen Chang, Ci Ruei Liou, Bing Zhang, Li Chia Tai

Research output: Contribution to journalArticlepeer-review


Nano-dendritic structures have gained increasing popularity in electrochemical sensors. However, it is still rare to generate a 3-dimensional model in a short period of time to understand the structure-function relationship of the sensors. Here, we report the construction of a 3-dimensional model for nano-dendritic, metallic structures frequently grown on top of bioelectronics. This is achieved by merging two sources of 2-dimensional dendritic information, which includes top-view images from scanning electron microscopy and side-view visualization from Monte-Carlo simulations. The microscopy images provide the boundary conditions to tune the Monte-Carlo simulations to construct the 3-dimensional dendritic morphology. We validated the 3-dimensional model by comparing the dendritic area densities predicted via this model with those computed from microscopy images. Additionally, tuning the simulation parameters in the 3-dimensional model can be used to find the optimized dendritic density, which is an essential indicator for sensitivity enhancement. The success of this model provides a means to understand the sensitivity limits of bio-electronics through dendritic growth without the need for the time-consuming sensor fabrication and testing. Further, our SEM-guided Monte-Carlo technique provides a dendritic model with a significant resemblance to experimental images. It possesses the potential for applications in 3-dimensional morphological investigations for future biosensor design.

Original languageEnglish
Pages (from-to)1
Number of pages1
JournalIEEE Sensors Journal
StateAccepted/In press - 2023


  • Biosensors
  • Monte Carlo methods
  • nano-dendrites
  • scanning electron microscopy


Dive into the research topics of 'Microscopy-Guided 3D Reconstruction of Nano-Dendrites in Biosensors'. Together they form a unique fingerprint.

Cite this