(181d) Unbiased Coarse-Grained Monte Carlo Simulation Using SAXS-Data for Identification of Self-Assembled Nanostructures | AIChE

(181d) Unbiased Coarse-Grained Monte Carlo Simulation Using SAXS-Data for Identification of Self-Assembled Nanostructures


Pahari, S. - Presenter, TEXAS A&M UNIVERSITY
Liu, S., Texas A&M University Chemical Engineering
Akbulut, M., Texas A&M University
Kwon, J., Texas A&M University
Over the last decade, there has been an increasing interest in industry and academia to develop complex fluids that can change their rheological properties reversibly in response to the changes in solution conditions [1]. These reversible fluids find great applications in the pharmaceutical, oil and gas, and specialty chemical industry sectors [2]. Specifically, the reversible nature of these fluids is attained via the dynamic nanostructure of self-assembled amphiphiles which are the building blocks of these complex fluids [3]. By carrying out deeper characterization and detailed free energy calculations, it was revealed that these self-assemblies have unique nanostructures and identifying these nanostructures is necessary as important properties of the complex fluids, like their viscoelastic properties, are strongly correlated with the geometry and dimensions of these nanostructures [4]. Therefore, it has been widely studied to develop effective characterization methods that can help us to identify the nanostructures present in such complex fluids [4]. However, it has been difficult to develop a general methodology that can help us to determine the detailed geometry and dimensions of the nanostructures present in the self-assemblies.

It is difficult to characterize the complex self-assembles because these techniques employ artificial treatments like dehydration, which can often lead to inaccurate imaging results due to significant changes in the morphology of the complex fluids [5]. To address these limitations, methods like cryogenic transmission electron microscopy (cryo-TEM) were utilized. However, sample preparation is challenging in cryo-TEM; additionally, rapid cooling in cryo-TEM may perturb the amphiphile conformation [6]. Furthermore, when scattering methods like small-angle X-ray scattering techniques (SAXS) are used to obtain the high-resolution three-dimensional description of the nanostructures in dispersed phase, there arises an inherent challenge associated with the degeneracy resulting from the reconstruction of three-dimensional nanostructures from one-dimensional SAXS scattering profiles [7]. Henceforth, solely utilizing SAXS scattering profiles, only simple structural features such as radius of gyration and maximum diameter of the nanostructures can be extracted. Recently SAXS-guided molecular dynamics (MD) simulations have been extensively carried out to characterize to native structures of biopolymers that are difficult to obtain via conventional experimental characterization techniques [8]. In these simulations the total Hamiltonian of the system is biased by adding a potential term which is a function of the discrepancy value between the SAXS profiles obtained experimentally and SAXS profiles calculated from the molecular coordinates in the MD simulations. Although this method has been successful in characterizing native structures of biopolymers they fail to characterize complex nanostructures because it requires the calculation of the ab-initio SAXS profiles during the MD simulations which becomes computationally intractable in the cases of large-scale nanostructures.

To address the limitations of the existing experimental and simulation techniques in obtaining a detailed three-dimensional description of complex self-assembled nanostructures, a SAXS-guided unbiased coarse-grained Monte Carlo (CGMC) simulation methodology is proposed. In this method, two metrics are utilized to identify the detailed molecular description of the complex self-assemblies formed by the amphiphiles. The first metric used is the SAXS discrepancy values, and the second metric is the total potential energy calculated from the CGMC simulations. In this proposed method, the Hamiltonian of the system is not biased, so the energy calculated from coarse-grained Monte Carlo simulations is accurate and can be utilized as an additional metric for structure identification. In this method, first, a library of nanostructures is generated, and then CGMC simulations are seeded from these structures. From the results of the CGMC simulations, structures with the least SAXS discrepancy values and the lowest energies are screened for the next round of sampling. Subsequently, from the screened structures, new simulations are further seeded, and the process is continued till no improvement in the SAXS discrepancy values and the total potential energy of the system is obtained. The proposed methodology is applied to the system of complex self-assembly called the dynamic binary complexes (DBC), which are formed by the self-assembly of OAPB amphiphiles and DTA ions. Henceforth, the SAXS-guided unbiased CGMC simulations could identify the nanostructures present in the DBCs. Furthermore, the dimensions of the resulting nanostructures were validated with scanning electron microscopy (SEM) images. Overall, we resolved the fundamental ambiguity resulting from reconstructing nanostructures from SAXS scattering profiles with the proposed method.


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