1
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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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Narayanan T. Recent advances in synchrotron scattering methods for probing the structure and dynamics of colloids. Adv Colloid Interface Sci 2024; 325:103114. [PMID: 38452431 DOI: 10.1016/j.cis.2024.103114] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2023] [Revised: 02/07/2024] [Accepted: 02/14/2024] [Indexed: 03/09/2024]
Abstract
Recent progress in synchrotron based X-ray scattering methods applied to colloid science is reviewed. An important figure of merit of these techniques is that they enable in situ investigations of colloidal systems under the desired thermophysical and rheological conditions. An ensemble averaged simultaneous structural and dynamical information can be derived albeit in reciprocal space. Significant improvements in X-ray source brilliance and advances in detector technology have overcome some of the limitations in the past. Notably coherent X-ray scattering techniques have become more competitive and they provide complementary information to laboratory based real space methods. For a system with sufficient scattering contrast, size ranges from nm to several μm and time scales down to μs are now amenable to X-ray scattering investigations. A wide variety of sample environments can be combined with scattering experiments further enriching the science that could be pursued by means of advanced X-ray scattering instruments. Some of these recent progresses are illustrated via representative examples. To derive quantitative information from the scattering data, rigorous data analysis or modeling is required. Development of powerful computational tools including the use of artificial intelligence have become the emerging trend.
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3
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Patel RA, Webb MA. Data-Driven Design of Polymer-Based Biomaterials: High-throughput Simulation, Experimentation, and Machine Learning. ACS APPLIED BIO MATERIALS 2024; 7:510-527. [PMID: 36701125 DOI: 10.1021/acsabm.2c00962] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Polymers, with the capacity to tunably alter properties and response based on manipulation of their chemical characteristics, are attractive components in biomaterials. Nevertheless, their potential as functional materials is also inhibited by their complexity, which complicates rational or brute-force design and realization. In recent years, machine learning has emerged as a useful tool for facilitating materials design via efficient modeling of structure-property relationships in the chemical domain of interest. In this Spotlight, we discuss the emergence of data-driven design of polymers that can be deployed in biomaterials with particular emphasis on complex copolymer systems. We outline recent developments, as well as our own contributions and takeaways, related to high-throughput data generation for polymer systems, methods for surrogate modeling by machine learning, and paradigms for property optimization and design. Throughout this discussion, we highlight key aspects of successful strategies and other considerations that will be relevant to the future design of polymer-based biomaterials with target properties.
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Affiliation(s)
- Roshan A Patel
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States
| | - Michael A Webb
- Department of Chemical and Biological Engineering, Princeton University, Princeton, New Jersey 08540, United States
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4
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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: 5] [Impact Index Per Article: 5.0] [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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5
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Lu S, Jayaraman A. Pair-Variational Autoencoders for Linking and Cross-Reconstruction of Characterization Data from Complementary Structural Characterization Techniques. JACS AU 2023; 3:2510-2521. [PMID: 37772182 PMCID: PMC10523369 DOI: 10.1021/jacsau.3c00275] [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: 05/30/2023] [Revised: 07/11/2023] [Accepted: 07/11/2023] [Indexed: 09/30/2023]
Abstract
In materials research, structural characterization often requires multiple complementary techniques to obtain a holistic morphological view of a synthesized material. Depending on the availability and accessibility of the different characterization techniques (e.g., scattering, microscopy, spectroscopy), each research facility or academic research lab may have access to high-throughput capability in one technique but face limitations (sample preparation, resolution, access time) with other technique(s). Furthermore, one type of structural characterization data may be easier to interpret than another (e.g., microscopy images are easier to interpret than small-angle scattering profiles). Thus, it is useful to have machine learning models that can be trained on paired structural characterization data from multiple techniques (easy and difficult to interpret, fast and slow in data collection or sample preparation) so that the model can generate one set of characterization data from the other. In this paper we demonstrate one such machine learning workflow, Pair-Variational Autoencoders (PairVAE), that works with data from small-angle X-ray scattering (SAXS) that present information about bulk morphology and images from scanning electron microscopy (SEM) that present two-dimensional local structural information on the sample. Using paired SAXS and SEM data of newly observed block copolymer assembled morphologies [open access data from Doerk G. S.; et al. Sci. Adv.2023, 9 ( (2), ), eadd3687], we train our PairVAE. After successful training, we demonstrate that the PairVAE can generate SEM images of the block copolymer morphology when it takes as input that sample's corresponding SAXS 2D pattern and vice versa. This method can be extended to other soft material morphologies as well and serves as a valuable tool for easy interpretation of 2D SAXS patterns as well as an engine for generating ensembles of similar microscopy images to create a database for other downstream calculations of structure-property relationships.
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Affiliation(s)
- Shizhao Lu
- 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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6
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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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7
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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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8
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Martin TB, Audus DJ. Emerging Trends in Machine Learning: A Polymer Perspective. ACS POLYMERS AU 2023. [DOI: 10.1021/acspolymersau.2c00053] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/19/2023]
Affiliation(s)
- Tyler B. Martin
- National Institute of Standards and Technology, Gaithersburg, Maryland20899, United States
| | - Debra J. Audus
- National Institute of Standards and Technology, Gaithersburg, Maryland20899, United States
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9
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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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10
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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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11
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Paruchuri BC, Gopal V, Sarupria S, Larsen J. Toward enzyme-responsive polymersome drug delivery. Nanomedicine (Lond) 2021; 16:2679-2693. [PMID: 34870451 DOI: 10.2217/nnm-2021-0194] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
In drug delivery, enzyme-responsive drug carriers are becoming increasingly relevant because of the growing association of disease pathology with enzyme overexpression. Polymersomes are of interest to such applications because of their tunable properties. While polymersomes open up a wide range of chemical and physical properties to explore, they also present a challenge in developing generalized rules for the synthesis of novel systems. Motivated by this issue, in this perspective, we summarize the existing knowledge on enzyme-responsive polymersomes and outline the main design choices. Then, we propose heuristics to guide the design of novel systems. Finally, we discuss the potential of an integrated approach using computer simulations and experimental studies to streamline this design process and close the existing knowledge gaps.
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Affiliation(s)
| | - Varun Gopal
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC 29631, USA.,Department of Chemical Engineering & Material Science, University of Minnesota, Minneapolis, MN 55455, USA
| | - Sapna Sarupria
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC 29631, USA.,Center for Optical Materials Science & Engineering Technologies (COMSET), Clemson University, Clemson, SC 29670, USA.,Department of Chemistry, University of Minnesota, Minneapolis, MN 55455, USA
| | - Jessica Larsen
- Department of Chemical & Biomolecular Engineering, Clemson University, Clemson, SC 29631, USA.,Department of Bioengineering, Clemson University, Clemson, SC 29631, USA
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12
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Ye Z, Wu Z, Jayaraman A. Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) on Vesicles Assembled from Amphiphilic Macromolecular Solutions. JACS AU 2021; 1:1925-1936. [PMID: 34841410 PMCID: PMC8611670 DOI: 10.1021/jacsau.1c00305] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Indexed: 05/25/2023]
Abstract
In this paper we present the development and validation of the "Computational Reverse-Engineering Analysis for Scattering Experiments" (CREASE) method for analyzing scattering results from vesicle structures that are commonly found upon assembly of synthetic, biomimetic, or bioderived amphiphilic copolymers in solution. The two-step CREASE method takes the amphiphilic polymer chemistry and small-angle scattering intensity profile, I exp(q), as input and determines the vesicles' structural features on multiple length scales ranging from assembled vesicle wall's individual layer thicknesses to the monomer-level packing and distribution of polymer conformations. In the first step of CREASE, a genetic algorithm (GA) is used to determine the relevant vesicle dimensions from the input macromolecular solution information and I exp(q) by identifying the structure whose computed scattering profile best matches the input I exp(q). Then in the second step, the GA-determined dimensions are used for molecular reconstruction of the vesicle structure. To validate CREASE for vesicles, we test CREASE on input scattering intensity profiles generated mathematically (termed as in silico I exp(q) vs q) from a variety of vesicle sizes with known dimensions. We also test CREASE on in silico I exp(q) vs q generated from vesicles with dispersity in all relevant dimensions, resembling real experiments. After successful validation of CREASE, we compare the CREASE-determined dimensions against those obtained from the traditional approach of fitting the scattering intensity profile to relevant analytical model in SASVIEW package. We show that CREASE performs better than or as well as the core-multishell analytical model's fitting in SASVIEW in determining vesicle dimensions with dispersity. We also show that CREASE provides structural information beyond those possible from traditional scattering analysis using the core-multishell model, such as the distribution of solvophilic monomers between the vesicle wall's inner and outer layers in the vesicle wall and the chain-level packing within each vesicle layer.
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Affiliation(s)
- Ziyu Ye
- Colburn
Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Zijie Wu
- Colburn
Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Colburn
Laboratory, 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, Newark, Delaware 19716, United States
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13
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Wang Z, Mai Y, Yang Y, Shen L, Yan C. Highly Ordered Pt-Based Nanoparticles Directed by the Self-Assembly of Block Copolymers for the Oxygen Reduction Reaction. ACS APPLIED MATERIALS & INTERFACES 2021; 13:38138-38146. [PMID: 34355891 DOI: 10.1021/acsami.1c04259] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/13/2023]
Abstract
Designing Pt-based nanoparticle (NP) catalysts is of great interest for the lowering of Pt usage and the enhancement of catalytic activity on the proton-exchange membrane fuel cells (PEMFCs). However, it is still challenging to develop well-arrayed catalyst NPs on supports over multiple-length scales. Herein, we presented a facile strategy of producing well-ordered Pt-based NPs toward oxygen reduction reaction (ORR) catalysts assisted by the self-assembly of block copolymers. In contrast to the conventional Pt/C ORR catalysts with a random dispersion on carbon, the as-prepared Pt, PtCo, and PtCo@Pt NPs in our work were hexagonally arranged with a uniform quasi-spherical shape and ordered distribution. The systematic study related to their ORR activities revealed that the PtCo@Pt core-shell NP arrays were more active and more durable than PtCo, Pt, and the commercial Pt/C catalyst. In the rotating-disk electrode test, a half-wave potential (E1/2) of 0.86 V versus RHE and a 4-e ORR mechanism were found for PtCo@Pt. Single-cell performance showed that the current density and the peak power density of PtCo@Pt achieved 0.86 A/cm2@0.7 V and 1.05 W/cm2, respectively, with a Pt loading of ∼0.15 mg/cm2 on the cathode. Also, they held 81.4 and 82.9% retention, respectively, after the durability test in the single-cell test. Density functional theory calculation results revealed that PtCo@Pt NPs had a lower d-band center and a weaker oxygen binding energy compared to Pt and PtCo, which contributed to the enhancement of the ORR activity.
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Affiliation(s)
- Zhida Wang
- Hydrogen Production and Utilization Lab, CAS Key Laboratory of Renewable Energy, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China
| | - Yilang Mai
- Hydrogen Production and Utilization Lab, CAS Key Laboratory of Renewable Energy, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China
- University of Chinese Academy of Sciences, Beijing 100039, China
| | - Yi Yang
- Hydrogen Production and Utilization Lab, CAS Key Laboratory of Renewable Energy, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China
- University of Chinese Academy of Sciences, Beijing 100039, China
| | - Lisha Shen
- Hydrogen Production and Utilization Lab, CAS Key Laboratory of Renewable Energy, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China
| | - Changfeng Yan
- Hydrogen Production and Utilization Lab, CAS Key Laboratory of Renewable Energy, Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 510640, China
- Guangdong Key Laboratory of New and Renewable Energy Research and Development, Guangzhou 510640, China
- University of Chinese Academy of Sciences, Beijing 100039, China
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14
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Heil C, Jayaraman A. Computational Reverse-Engineering Analysis for Scattering Experiments of Assembled Binary Mixture of Nanoparticles. ACS MATERIALS AU 2021; 1:140-156. [PMID: 36855396 PMCID: PMC9888618 DOI: 10.1021/acsmaterialsau.1c00015] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
In this paper, we describe a computational method for analyzing results from scattering experiments on dilute solutions of supraparticles, where each supraparticle is created by the assembly of nanoparticle mixtures. Taking scattering intensity profiles and nanoparticle mixture composition and size distributions in each supraparticle as input, this computational approach called computational reverse engineering analysis for scattering experiments (CREASE) uses a genetic algorithm to output information about the structure of the assembled nanoparticles (e.g., real space pair correlation function, extent of nanoparticle mixing/segregation, sizes of domains) within a supraparticle. We validate this method by taking as input in silico scattering intensity profiles from coarse-grained molecular simulations of a binary mixture of nanoparticles, forming a close-packed structure and testing if our computational method can correctly reproduce the nanoparticle structure observed in those simulations. We test the strengths and limitations of our method using a variety of in silico scattering intensity profiles obtained from simulations of a spherical or a cubic supraparticle comprising binary nanoparticle mixtures with varying chemistries, with and without dispersity in sizes, that exhibit well-mixed to strongly segregated structures. The strengths of the presented method include its capability to analyze scattering intensity profiles even when the wavevector q range is limited, to handily provide all of the pairwise radial distribution functions, and to correctly determine the extent of segregation/mixing of the nanoparticles assembled in complex geometries.
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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
| | - 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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15
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Rizvi A, Mulvey JT, Carpenter BP, Talosig R, Patterson JP. A Close Look at Molecular Self-Assembly with the Transmission Electron Microscope. Chem Rev 2021; 121:14232-14280. [PMID: 34329552 DOI: 10.1021/acs.chemrev.1c00189] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/08/2023]
Abstract
Molecular self-assembly is pervasive in the formation of living and synthetic materials. Knowledge gained from research into the principles of molecular self-assembly drives innovation in the biological, chemical, and materials sciences. Self-assembly processes span a wide range of temporal and spatial domains and are often unintuitive and complex. Studying such complex processes requires an arsenal of analytical and computational tools. Within this arsenal, the transmission electron microscope stands out for its unique ability to visualize and quantify self-assembly structures and processes. This review describes the contribution that the transmission electron microscope has made to the field of molecular self-assembly. An emphasis is placed on which TEM methods are applicable to different structures and processes and how TEM can be used in combination with other experimental or computational methods. Finally, we provide an outlook on the current challenges to, and opportunities for, increasing the impact that the transmission electron microscope can have on molecular self-assembly.
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Affiliation(s)
- Aoon Rizvi
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Justin T Mulvey
- Department of Materials Science and Engineering, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Brooke P Carpenter
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Rain Talosig
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
| | - Joseph P Patterson
- Department of Chemistry, University of California, Irvine, Irvine, California 92697-2025, United States
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16
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Wessels M, Jayaraman A. Machine Learning Enhanced Computational Reverse Engineering Analysis for Scattering Experiments (CREASE) to Determine Structures in Amphiphilic Polymer Solutions. ACS POLYMERS AU 2021; 1:153-164. [PMID: 36855654 PMCID: PMC9954245 DOI: 10.1021/acspolymersau.1c00015] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
In this article, we present a machine learning enhancement for our recently developed "Computational Reverse Engineering Analysis for Scattering Experiments" (CREASE) method to accelerate analysis of results from small angle scattering (SAS) experiments on polymer materials. We demonstrate this novel artificial neural network (NN) enhanced CREASE approach for analyzing scattering results from amphiphilic polymer solutions that can be easily extended and applied for scattering experiments on other polymer and soft matter systems. We had originally developed CREASE to analyze SAS results [i.e., intensity profiles, I(q) vs q] of amphiphilic polymer solutions exhibiting unconventional assembled structures and/or novel polymer chemistries for which traditional fits using off-the-shelf analytical models would be too approximate/inapplicable. In this paper, we demonstrate that the NN-enhancement to the genetic algorithm (GA) step in the CREASE approach improves the speed and, in some cases, the accuracy of the GA step in determining the dimensions of the complex assembled structures for a given experimental scattering profile.
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Affiliation(s)
- Michiel
G. Wessels
- Colburn
Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Colburn
Laboratory, 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, Newark, Delaware 19716, United States,
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17
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Wessels MG, Jayaraman A. Computational Reverse-Engineering Analysis of Scattering Experiments (CREASE) on Amphiphilic Block Polymer Solutions: Cylindrical and Fibrillar Assembly. Macromolecules 2021. [DOI: 10.1021/acs.macromol.0c02265] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Affiliation(s)
- Michiel G. Wessels
- Colburn Laboratory, Department of Chemical and Biomolecular Engineering, University of Delaware, 150 Academy Street, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Colburn Laboratory, 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, Newark, Delaware 19716, United States
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18
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Lee JY, Song Y, Wessels MG, Jayaraman A, Wooley KL, Pochan DJ. Hierarchical Self-Assembly of Poly(d-glucose carbonate) Amphiphilic Block Copolymers in Mixed Solvents. Macromolecules 2020. [DOI: 10.1021/acs.macromol.0c01575] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/02/2023]
Affiliation(s)
- Jee Young Lee
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Yue Song
- Departments of Chemistry, Chemical Engineering, and Materials Science & Engineering, Texas A&M University, College Station, Texas 77842, United States
| | - Michiel G. Wessels
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Arthi Jayaraman
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States
- Department of Chemical and Biomolecular Engineering, University of Delaware, Newark, Delaware 19716, United States
| | - Karen L. Wooley
- Departments of Chemistry, Chemical Engineering, and Materials Science & Engineering, Texas A&M University, College Station, Texas 77842, United States
| | - Darrin J. Pochan
- Department of Materials Science and Engineering, University of Delaware, Newark, Delaware 19716, United States
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19
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Koochaki A, Moghbeli MR, Nikkhah SJ, Ianiro A, Tuinier R. Dual responsive PMEEECL–PAE block copolymers: a computational self-assembly and doxorubicin uptake study. RSC Adv 2020; 10:3233-3245. [PMID: 35497759 PMCID: PMC9048636 DOI: 10.1039/c9ra09066e] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2019] [Accepted: 01/08/2020] [Indexed: 11/21/2022] Open
Abstract
The self-assembly behaviour of dual-responsive block copolymers and their ability to solubilize the anticancer drug doxorubicin (DOX) has been investigated using all-atom molecular dynamics (MD) simulations, MARTINI coarse-grained (CG) force field simulation and Scheutjens–Fleer self-consistent field (SCF) computations. These diblock copolymers, composed of poly{γ-2-[2-(2-methoxyethoxy)ethoxy]ethoxy-ε-caprolactone} (PMEEECL) and poly(β-amino ester) (PAE) are dual-responsive: the PMEEECL block is thermoresponsive (becomes insoluble above a certain temperature), while the PAE block is pH-responsive (becomes soluble below a certain pH). Three MEEECL20–AEM compositions with M = 5, 10, and 15, have been studied. All-atom MD simulations have been performed to calculate the coil-to-globule transition temperature (Tcg) of these copolymers and finding appropriate CG mapping for both PMEEECL–PAE and DOX. The output of the MARTINI CG simulations is in agreement with SCF predictions. The results show that DOX is solubilized with high efficiency (75–80%) at different concentrations inside the PMEEECL–PAE micelles, although, interestingly, the loading efficiency is reduced by increasing the drug concentration. The non-bonded interaction energy and the RDF between DOX and water beads confirm this result. Finally, MD simulations and SCF computations reveal that the responsive behaviour of PMEEECL–PAE self-assembled structures take place at temperature and pH ranges appropriate for drug delivery. The self-assembly behaviour of dual-responsive block copolymers and their ability to solubilize the drug doxorubicin is demonstrated using molecular dynamics simulations, coarse-grained force field simulations and self-consistent field theory.![]()
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Affiliation(s)
- Amin Koochaki
- Smart Polymers and Nanocomposites Research Group
- School of Chemical Engineering
- Iran University of Science and Technology
- Tehran 16846-13114
- Iran
| | - Mohammad Reza Moghbeli
- Smart Polymers and Nanocomposites Research Group
- School of Chemical Engineering
- Iran University of Science and Technology
- Tehran 16846-13114
- Iran
| | - Sousa Javan Nikkhah
- Smart Polymers and Nanocomposites Research Group
- School of Chemical Engineering
- Iran University of Science and Technology
- Tehran 16846-13114
- Iran
| | - Alessandro Ianiro
- Laboratory of Physical Chemistry
- Department of Chemical Engineering and Chemistry
- Eindhoven University of Technology
- 5600 MB Eindhoven
- The Netherlands
| | - Remco Tuinier
- Laboratory of Physical Chemistry
- Department of Chemical Engineering and Chemistry
- Eindhoven University of Technology
- 5600 MB Eindhoven
- The Netherlands
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