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Akepati SV, Gupta N, Jayaraman A. Computational Reverse Engineering Analysis of the Scattering Experiment Method for Interpretation of 2D Small-Angle Scattering Profiles (CREASE-2D). JACS AU 2024; 4:1570-1582. [PMID: 38665659 PMCID: PMC11040659 DOI: 10.1021/jacsau.4c00068] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/22/2024] [Revised: 03/01/2024] [Accepted: 03/05/2024] [Indexed: 04/28/2024]
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 I(q) as a function of q, the scattered wavevector with respect to the incident wave; the latter is represented by its magnitude |q| ≡ q (in inverse distance units) and azimuthal angle θ. 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, I(q, 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, I(q, 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 I(q, θ) profiles, calculated using a surrogate XGBoost machine learning model, match the input experimental I(q, 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 I(q) vs q profiles that can lose important information related to structural anisotropy.
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Affiliation(s)
| | - Nitant Gupta
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
- Department
of Materials Science and Engineering, University
of Delaware, Newark, Delaware 19716, United States
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2
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Anker AS, Butler KT, Selvan R, Jensen KMØ. Machine learning for analysis of experimental scattering and spectroscopy data in materials chemistry. Chem Sci 2023; 14:14003-14019. [PMID: 38098730 PMCID: PMC10718081 DOI: 10.1039/d3sc05081e] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 11/20/2023] [Indexed: 12/17/2023] Open
Abstract
The rapid growth of materials chemistry data, driven by advancements in large-scale radiation facilities as well as laboratory instruments, has outpaced conventional data analysis and modelling methods, which can require enormous manual effort. To address this bottleneck, we investigate the application of supervised and unsupervised machine learning (ML) techniques for scattering and spectroscopy data analysis in materials chemistry research. Our perspective focuses on ML applications in powder diffraction (PD), pair distribution function (PDF), small-angle scattering (SAS), inelastic neutron scattering (INS), and X-ray absorption spectroscopy (XAS) data, but the lessons that we learn are generally applicable across materials chemistry. We review the ability of ML to identify physical and structural models and extract information efficiently and accurately from experimental data. Furthermore, we discuss the challenges associated with supervised ML and highlight how unsupervised ML can mitigate these limitations, thus enhancing experimental materials chemistry data analysis. Our perspective emphasises the transformative potential of ML in materials chemistry characterisation and identifies promising directions for future applications. The perspective aims to guide newcomers to ML-based experimental data analysis.
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Affiliation(s)
- Andy S Anker
- Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark
| | - Keith T Butler
- Department of Chemistry, University College London Gower Street London WC1E 6BT UK
| | - Raghavendra Selvan
- Department of Computer Science, University of Copenhagen 2100 Copenhagen Ø Denmark
- Department of Neuroscience, University of Copenhagen 2200 Copenhagen N Denmark
| | - Kirsten M Ø Jensen
- Department of Chemistry and Nano-Science Center, University of Copenhagen 2100 Copenhagen Ø Denmark
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3
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Ma Y, Heil C, Nagy G, Heller WT, An Y, Jayaraman A, Bharti B. Synergistic Role of Temperature and Salinity in Aggregation of Nonionic Surfactant-Coated Silica Nanoparticles. LANGMUIR : THE ACS JOURNAL OF SURFACES AND COLLOIDS 2023; 39:5917-5928. [PMID: 37053432 PMCID: PMC10134496 DOI: 10.1021/acs.langmuir.3c00432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/03/2023] [Indexed: 06/19/2023]
Abstract
The adsorption of nonionic surfactants onto hydrophilic nanoparticles (NPs) is anticipated to increase their stability in aqueous medium. While nonionic surfactants show salinity- and temperature-dependent bulk phase behavior in water, the effects of these two solvent parameters on surfactant adsorption and self-assembly onto NPs are poorly understood. In this study, we combine adsorption isotherms, dispersion transmittance, and small-angle neutron scattering (SANS) to investigate the effects of salinity and temperature on the adsorption of pentaethylene glycol monododecyl ether (C12E5) surfactant on silica NPs. We find an increase in the amount of surfactant adsorbed onto the NPs with increasing temperature and salinity. Based on SANS measurements and corresponding analysis using computational reverse-engineering analysis of scattering experiments (CREASE), we show that the increase in salinity and temperature results in the aggregation of silica NPs. We further demonstrate the non-monotonic changes in viscosity for the C12E5-silica NP mixture with increasing temperature and salinity and correlate the observations to the aggregated state of NPs. The study provides a fundamental understanding of the configuration and phase transition of the surfactant-coated NPs and presents a strategy to manipulate the viscosity of such dispersion using temperature as a stimulus.
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Affiliation(s)
- Yingzhen Ma
- Cain
Department of Chemical Engineering, Louisiana
State University, Baton
Rouge, Louisiana 70803, United States
| | - Christian Heil
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Gergely Nagy
- Neutron
Scattering Division, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37831, United States
| | - William T. Heller
- Neutron
Scattering Division, Oak Ridge National
Laboratory, Oak Ridge, Tennessee 37831, United States
| | - Yaxin An
- Cain
Department of Chemical Engineering, Louisiana
State University, Baton
Rouge, Louisiana 70803, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Bhuvnesh Bharti
- Cain
Department of Chemical Engineering, Louisiana
State University, Baton
Rouge, Louisiana 70803, United States
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4
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Heil C, Ma Y, Bharti B, Jayaraman A. Computational Reverse-Engineering Analysis for Scattering Experiments for Form Factor and Structure Factor Determination (" P( q) and S( q) CREASE"). JACS AU 2023; 3:889-904. [PMID: 37006757 PMCID: PMC10052275 DOI: 10.1021/jacsau.2c00697] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/20/2022] [Revised: 02/09/2023] [Accepted: 02/10/2023] [Indexed: 05/11/2023]
Abstract
In this paper, we present an open-source machine learning (ML)-accelerated computational method to analyze small-angle scattering profiles [I(q) vs q] from concentrated macromolecular solutions to simultaneously obtain the form factor P(q) (e.g., dimensions of a micelle) and the structure factor S(q) (e.g., spatial arrangement of the micelles) without relying on analytical models. This method builds on our recent work on Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) that has either been applied to obtain P(q) from dilute macromolecular solutions (where S(q) ∼1) or to obtain S(q) from concentrated particle solutions when P(q) is known (e.g., sphere form factor). This paper's newly developed CREASE that calculates P(q) and S(q), termed as "P(q) and S(q) CREASE", is validated by taking as input I(q) vs q from in silico structures of known polydisperse core(A)-shell(B) micelles in solutions at varying concentrations and micelle-micelle aggregation. We demonstrate how "P(q) and S(q) CREASE" performs if given two or three of the relevant scattering profiles-I total(q), I A(q), and I B(q)-as inputs; this demonstration is meant to guide experimentalists who may choose to do small-angle X-ray scattering (for total scattering from the micelles) and/or small-angle neutron scattering with appropriate contrast matching to get scattering solely from one or the other component (A or B). After validation of "P(q) and S(q) CREASE" on in silico structures, we present our results analyzing small-angle neutron scattering profiles from a solution of core-shell type surfactant-coated nanoparticles with varying extents of aggregation.
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Affiliation(s)
- Christian
M. Heil
- Department
of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark, Delaware 19716, United States
| | - Yingzhen Ma
- Cain
Department of Chemical Engineering, Louisiana
State University, 3307 Patrick F. Taylor Hall, Baton Rouge, Louisiana 70803, United States
| | - Bhuvnesh Bharti
- Cain
Department of Chemical Engineering, Louisiana
State University, 3307 Patrick F. Taylor Hall, Baton Rouge, Louisiana 70803, United States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark, Delaware 19716, United States
- Department
of Materials Science and Engineering, University
of Delaware, 201 DuPont
Hall, Newark, Delaware 19716, United States
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Wu Z, Jayaraman A. Machine Learning-Enhanced Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) for Analyzing Fibrillar Structures in Polymer Solutions. Macromolecules 2022. [DOI: 10.1021/acs.macromol.2c02165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Affiliation(s)
- Zijie Wu
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark, Delaware19716, United States
| | - Arthi Jayaraman
- Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy St., Newark, Delaware19716, United States
- Department of Materials Science and Engineering, University of Delaware, 201 DuPont Hall, Newark, Delaware19716, United States
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6
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Antonov AP, Schweers S, Ryabov A, Maass P. Brownian dynamics simulations of hard rods in external fields and with contact interactions. Phys Rev E 2022; 106:054606. [PMID: 36559370 DOI: 10.1103/physreve.106.054606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2022] [Accepted: 10/24/2022] [Indexed: 11/12/2022]
Abstract
We propose a simulation method for Brownian dynamics of hard rods in one dimension for arbitrary continuous external force fields. It is an event-driven procedure based on the fragmentation and mergers of clusters formed by particles in contact. It allows one to treat particle interactions in addition to the hard-sphere exclusion as long as the corresponding interaction forces are continuous functions of the particle coordinates. We furthermore develop a treatment of sticky hard spheres as described by Baxter's contact interaction potential.
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Affiliation(s)
- Alexander P Antonov
- Universität Osnabrück, Fachbereich Physik, Barbarastraße 7, D-49076 Osnabrück, Germany
| | - Sören Schweers
- Universität Osnabrück, Fachbereich Physik, Barbarastraße 7, D-49076 Osnabrück, Germany
| | - Artem Ryabov
- Charles University, Faculty of Mathematics and Physics, Department of Macromolecular Physics, V Holešovičkách 2, CZ-18000 Praha 8, Czech Republic
| | - Philipp Maass
- Universität Osnabrück, Fachbereich Physik, Barbarastraße 7, D-49076 Osnabrück, Germany
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Heil C, Patil A, Dhinojwala A, Jayaraman A. Computational Reverse-Engineering Analysis for Scattering Experiments (CREASE) with Machine Learning Enhancement to Determine Structure of Nanoparticle Mixtures and Solutions. ACS CENTRAL SCIENCE 2022; 8:996-1007. [PMID: 35912348 PMCID: PMC9335921 DOI: 10.1021/acscentsci.2c00382] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/12/2023]
Abstract
We present a new open-source, machine learning (ML) enhanced computational method for experimentalists to quickly analyze high-throughput small-angle scattering results from multicomponent nanoparticle mixtures and solutions at varying compositions and concentrations to obtain reconstructed 3D structures of the sample. This new method is an improvement over our original computational reverse-engineering analysis for scattering experiments (CREASE) method (ACS Materials Au2021, 1 (2 (2), ), 140-156), which takes as input the experimental scattering profiles and outputs a 3D visualization and structural characterization (e.g., real space pair-correlation functions, domain sizes, and extent of mixing in binary nanoparticle mixtures) of the nanoparticle mixtures. The new gene-based CREASE method reduces the computational running time by >95% as compared to the original CREASE and performs better in scenarios where the original CREASE method performed poorly. Furthermore, the ML model linking features of nanoparticle solutions (e.g., concentration, nanoparticles' tendency to aggregate) to a computed scattering profile is generic enough to analyze scattering profiles for nanoparticle solutions at conditions (nanoparticle chemistry and size) beyond those that were used for the ML training. Finally, we demonstrate application of this new gene-based CREASE method for analysis of small-angle X-ray scattering results from a nanoparticle solution with unknown nanoparticle aggregation and small-angle neutron scattering results from a binary nanoparticle assembly with unknown mixing/segregation among the nanoparticles.
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Affiliation(s)
- Christian
M. Heil
- Department
of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United
States
| | - Anvay Patil
- School
of Polymer Science and Polymer Engineering, The University of Akron, 170 University Avenue, Akron, Ohio 44325, United
States
| | - Ali Dhinojwala
- School
of Polymer Science and Polymer Engineering, The University of Akron, 170 University Avenue, Akron, Ohio 44325, United
States
| | - Arthi Jayaraman
- Department
of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United
States
- Department
of Materials Science and Engineering, University
of Delaware, 201 DuPont
Hall, Newark, Delaware 19716, United States
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