Literature DB >> 31497951

Computational Reverse-Engineering Analysis for Scattering Experiments on Amphiphilic Block Polymer Solutions.

Daniel J Beltran-Villegas1, Michiel G Wessels1, Jee Young Lee2, Yue Song3, Karen L Wooley3, Darrin J Pochan2, Arthi Jayaraman1,2.   

Abstract

In this paper, we present a computational reverse-engineering analysis for scattering experiments (CREASE) based on genetic algorithms and molecular simulation to analyze the structure within self-assembled amphiphilic polymer solutions. For a given input comprised of scattering intensity profiles and information about the amphiphilic polymers in solution, CREASE outputs the structure of the self-assembled micelles (e.g., core and corona diameters, aggregation number) as well as the conformations of the amphiphilic polymer chains in the micelle (e.g., blocks' radii of gyration, chain radii of gyration, monomer concentration profiles). First, we demonstrate CREASE's ability to reverse-engineer self-assembled nanostructures for scattering profiles obtained from molecular simulations (or in silico experiments) of generic coarse-grained bead-spring polymer chains in an implicit solvent. We then present CREASE's outputs for scattering profiles obtained from small-angle neutron scattering (SANS) experiments of poly(d-glucose carbonate) block copolymers in solution that exhibit assembly into spherical nanoparticles. The success of this method is demonstrated by its ability to replicate, quantitatively, the results from in silico experiments and by the agreement in micelle core and corona sizes obtained from microscopy of the in vitro solutions. The primary strength of CREASE is its ability to analyze scattering profiles without an off-the-shelf scattering model and the ability to provide chain and monomer level structural information that is otherwise difficult to obtain from scattering and microscopy alone.

Entities:  

Year:  2019        PMID: 31497951     DOI: 10.1021/jacs.9b08028

Source DB:  PubMed          Journal:  J Am Chem Soc        ISSN: 0002-7863            Impact factor:   15.419


  2 in total

1.  Dual responsive PMEEECL-PAE block copolymers: a computational self-assembly and doxorubicin uptake study.

Authors:  Amin Koochaki; Mohammad Reza Moghbeli; Sousa Javan Nikkhah; Alessandro Ianiro; Remco Tuinier
Journal:  RSC Adv       Date:  2020-01-20       Impact factor: 3.361

2.  Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions.

Authors:  Christian M Heil; Anvay Patil; Ali Dhinojwala; Arthi Jayaraman
Journal:  ACS Cent Sci       Date:  2022-07-01       Impact factor: 18.728

  2 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.