Microscopy-Guided 3-D Reconstruction of Nanodendrites in Biosensors

Nanodendritic structures have gained increasing popularity in electrochemical sensors. However, it is still rare to generate a 3-D model in a short period of time to understand the structure–function relationship of the sensors. Here, we report the construction of a 3-D model for nanodendritic, metallic structures frequently grown on top of bioelectronics. This is achieved by merging two sources of 2-D dendritic information, which includes top-view images from scanning electron microscopy (SEM) 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-D dendritic morphology. We validated the 3-D model by comparing the dendritic area densities predicted via this model with those computed from microscopy images. In addition, tuning the simulation parameters in the 3-D 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 bioelectronics through dendritic growth without the need for time-consuming sensor fabrication and testing. Furthermore, 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-D morphological investigations for future biosensor design.

I. Introduction

Nanodendritic structures have gained popularity in the fields of electrochemical sensors, due to their characteristics such as larger surface area, more adsorption sites, and higher catalytic activity due to their highly branched morphologies [1], [2], [3], [4]. Since electrochemical sensors have important applications in various areas, such as medicine, agriculture, and forensic analysis (see Fig. 1) [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], the ability to optimize the sensors’ sensitivity via nanodendritic structures is attractive [1], [17]. Nanodendritic growth resembles the diffusion-limited aggregation (DLA) behavior and provides the unique characteristics for biosensing and plasmonic enhancement purposes [17], [18], [19]. For instance, nanodendrites increase biosensors’ sensitivities to detect low-concentration biomolecules [1], [2], [3]. The growth of nanodendritic structures depends on multiple parameters. Taking gold nanodendrites as examples, they can be grown on a sensing electrode by immersing the electrode surface in a chloroauric acid (HAuCl₄) solution [4], [20]. The growth rate and density of the dendrites depend on various parameters, such as the concentration of HAuCl₄, growth time, and temperature [20]. It is critical to maximizing the dendrites’ density to optimize the sensitivity and reliability of the sensing electrode [21]. However, the optimization procedure often involves lengthy fabrication and electrochemical testing. For some biosensors, the nanodendritic growth procedure, conditioning, and testing require several hours before they can be characterized for further usage [1], [2]. This process is time-consuming, cost-inefficient, and possesses higher batch-to-batch variation [2]. A 3-D model provides us with a morphological view of the dendritic structures without requiring a lengthy fabrication and imaging process. Importantly, it allows us to estimate the maximum possible density of nanodendrites in a DLA process. This highest density limit is important information when one aims to achieve a high sensor sensitivity to detect trace biomolecules. Because of the reasons above, the 3-D dendritic model is a crucial tool for estimation purposes based on nanodendritic structures. Fig. 1.

Applications of biosensors with nanodendritic structures grown on the electrode’s surface in medicine, agriculture, and forensic analysis. Electrodes can be produced with dendrites and mass-fabricated for subsequent integration into a portable sensing device.