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用于解释二维小角散射轮廓的散射实验方法的计算逆向工程分析(CREASE-2D)

Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D).

作者信息

Akepati Sri Vishnuvardhan Reddy, Gupta Nitant, Jayaraman Arthi

机构信息

Data Science Program, University of Delaware, Newark, Delaware 19716, United States.

Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States.

出版信息

JACS Au. 2024 Mar 20;4(4):1570-1582. doi: 10.1021/jacsau.4c00068. eCollection 2024 Apr 22.

Abstract

Small-angle scattering (SAS) is a widely used characterization technique that provides structural information in soft materials at varying length scales (nanometers to microns). The output of an SAS measurement is the scattered intensity () as a function of , the scattered wavevector with respect to the incident wave; the latter is represented by its (in inverse distance units) and . While isotropic structural arrangement can be interpreted by analysis of the azimuthally averaged one-dimensional (1D) scattering profile, to understand anisotropic arrangements, one has to interpret the two-dimensional (2D) scattering profile, (, θ). Manual interpretation of such 2D profiles usually involves fitting of approximate analytical models to azimuthally averaged sections of the 2D profile. In this paper, we present a new method called CREASE-2D that interprets, without any azimuthal averaging, the entire 2D scattering profile, (, θ), and outputs the relevant structural features. CREASE-2D is an extension of the "computational reverse engineering analysis for scattering experiments" (CREASE) method that has been used successfully to analyze 1D SAS profiles for a variety of soft materials. CREASE-2D goes beyond CREASE by enabling analysis of 2D scattering profiles, which is far more challenging to interpret than the azimuthally averaged 1D profiles. The CREASE-2D workflow identifies the structural features whose computed (, θ) profiles, calculated using a surrogate XGBoost machine learning model, match the input experimental (, θ). We expect that this CREASE-2D method will be a valuable tool for materials' researchers who need direct interpretation of the 2D scattering profiles in contrast to analyzing azimuthally averaged 1D () vs profiles that can lose important information related to structural anisotropy.

摘要

小角散射(SAS)是一种广泛应用的表征技术,可在不同长度尺度(纳米到微米)下提供软材料的结构信息。SAS测量的输出是散射强度()作为的函数,是相对于入射波的散射波矢;后者由其(以逆距离单位)和表示。虽然各向同性结构排列可以通过分析方位角平均的一维(1D)散射轮廓来解释,但要理解各向异性排列,则必须解释二维(2D)散射轮廓(,θ)。对这种二维轮廓的人工解释通常涉及将近似分析模型拟合到二维轮廓的方位角平均部分。在本文中,我们提出了一种名为CREASE-2D的新方法,该方法无需任何方位角平均即可解释整个二维散射轮廓(,θ),并输出相关的结构特征。CREASE-2D是“散射实验的计算逆向工程分析”(CREASE)方法的扩展,该方法已成功用于分析各种软材料的一维SAS轮廓。CREASE-2D通过能够分析二维散射轮廓而超越了CREASE,二维散射轮廓的解释比方位角平均的一维轮廓要困难得多。CREASE-2D工作流程识别其计算的(,θ)轮廓(使用替代XGBoost机器学习模型计算)与输入实验(,θ)匹配的结构特征。我们期望这种CREASE-2D方法对于需要直接解释二维散射轮廓的材料研究人员来说将是一个有价值的工具,这与分析方位角平均的一维()与轮廓相比,后者可能会丢失与结构各向异性相关的重要信息。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85b3/11040659/ee93a4a28bbd/au4c00068_0001.jpg

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