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Updated: Sep 5, 2025

Scalable Nanohelices for Predictive Studies and Enhanced 3D Visualization
Published on: November 12, 2014
SAXS-guided unbiased coarse-grained Monte Carlo simulation for identification of self-assembly nanostructures and
Silabrata Pahari1,2, Shuhao Liu1, Chi Ho Lee1,2
1Texas A&M University, Artie McFerrin Department of Chemical Engineering, Texas A&M University, College Station, TX 77843, USA. kwonx075@tamu.edu.
Abstract:
Recent studies have shown that solvated amphiphiles can form nanostructured self-assemblies called dynamic binary complexes (DBCs) in the presence of ions. Since the nanostructures of DBCs are directly related to their viscoelastic properties, it is important to understand how the nanostructures change under different solution conditions. However, it is challenging to obtain a three-dimensional molecular description of these nanostructures by utilizing conventional experimental characterization techniques or thermodynamic models. To this end, we combined the structural data from small angle X-ray scattering (SAXS) experiments and thermodynamic knowledge from coarse-grained Monte Carlo (CGMC) simulations to identify the detailed three-dimensional nanostructure of DBCs. Specifically, unbiased CGMC simulations are performed with SAXS-guided initial conditions, which aids us to sample accurate nanostructures in a computationally efficient fashion. As a result, an elliptical bilayer nanostructure is obtained as the most probable nanostructure of DBCs whose dimensions are validated by scanning electron microscope (SEM) images. Then, utilizing the obtained molecular model of DBCs, we could also explain the pH tunability of the system. Overall, our results from SAXS-guided unbiased CGMC simulations highlight that using potential energy combined with SAXS data, we can distinguish otherwise degenerate nanostructures resulting from the inherent ambiguity of SAXS patterns.
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