CAREER: Cyber-enabled Multiscale Methodology for Hybrid Soft Materials-based Nanoparticle Design

职业：基于网络的混合软材料纳米粒子设计的多尺度方法

• 批准号: 1654325
• 负责人: Meenakshi Dutt
• 金额: $ 44.54万
• 依托单位: Rutgers University New Brunswick
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2025-04-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1654325&HistoricalAwards=false
• 关键词: CAREER; Cyber; enabled; Multiscale; Methodology

NONTECHNICAL SUMMARYThe Division of Materials Research in the Mathematical and Physical Sciences Directorate and the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering contribute funds for this project. This CAREER award supports computational research, cyberinfrastructure development, and education toward the ability to design nanoparticles with desired properties. Nanoparticles are encountered everywhere such as food, drugs, cars and cosmetics. The properties of nanoparticles are determined by the molecules they encompass, and can be precisely adjusted by using different kinds of molecules. Creating nanoparticles with specific properties and molecular constituents requires understanding how the molecules interact with each other and pack together. Given the vast number of molecules available, an efficient method is required to relate the characteristics of a nanoparticle to the properties of its molecular constituents. Of special interest are nanoparticles made of soft materials, such as those in car tires, jello and detergents, which can store various molecules. This CAREER project supports computational research and education on the computational design of mixed soft materials-based nanoparticles with desired characteristics. The PI's approach will use a method that includes essential physics and chemistry at different scales of length and time. The method will be aided by the development and use of advanced cyberinfrastructure. This research will support activities to stimulate the interest of high school students in science, engineering and mathematics. In addition, the research will be used to engage and inform the general public of the role of computation in materials design to society and benefits of University-level education. Finally, the research will enhance and maintain a competitive science and engineering workforce by increasing awareness of advanced computing methods and tools for materials design among undergraduate, graduate students and the scientific community.TECHNICAL SUMMARYThe Division of Materials Research in the Mathematical and Physical Sciences Directorate and the Office of Advanced Cyberinfrastructure in the Directorate for Computer and Information Science and Engineering contribute funds for this project. This CAREER award supports computational research, cyberinfrastructure development, and education toward the ability to design nanoparticles with desired properties. The PI aims to advance the ability to design of hybrid soft materials-based nanoparticles (NPs) with specific structure-property relations aided by the development and use of a cyber-enabled multiscale methodology. The conception of such designs will require a fundamental understanding of the role of molecular conformation, organization and mobility on the collective behavior of the distinct molecular species, and thereby, on material properties. The research lies at the interface of soft materials and advanced computing, and affords an opportunity to increase awareness, interest, recruitment, retention and training of a competitive workforce in science, technology, engineering, and mathematics areas.The PI seeks to design sterically stable hybrid NPs with morphologies optimized to store various molecules. The NP designs will require understanding the links between molecular traits and desired properties of hybrid soft materials. This will be facilitated by the development and use of a multiscale method that can link the compositional details of the NP to its desired attributes by integrating Molecular Dynamics simulations and analysis tools with advanced cyberinfrastructure. This plan will be realized through three objectives: (1) Development of hybrid NP designs; (2) Elucidation of the role of pH on hybrid NP designs, and (3) Prediction and validation of hybrid NP designs encompassing alternate chemical species.The prediction of soft materials-based nanoparticles with desired properties will be significantly accelerated by understanding the relationship between molecular traits and structure-property relations. Both the design rules and the method can be extended to conceive other multicomponent soft material-based systems with targeted structure-property relations. In addition, the use of advanced computing tools in virtual soft materials design can nucleate the adoption of new computational methodologies by the community. This will facilitate the accelerated development of new soft materials-based innovations and technologies.

非技术性总结数学和物理科学理事会材料研究部和计算机和信息科学与工程理事会高级网络基础设施办公室为该项目提供资金。该职业奖支持计算研究，网络基础设施开发和教育，以设计具有所需特性的纳米粒子。纳米颗粒随处可见，如食品、药品、汽车和化妆品。纳米颗粒的性质由它们所包含的分子决定，并且可以通过使用不同种类的分子来精确调节。创造具有特定特性和分子成分的纳米颗粒需要了解分子如何相互作用并聚集在一起。鉴于可用的分子数量巨大，需要一种有效的方法将纳米颗粒的特性与其分子成分的性质联系起来。特别令人感兴趣的是由软材料制成的纳米颗粒，例如汽车轮胎，果冻和洗涤剂中的纳米颗粒，它们可以储存各种分子。这个CAREER项目支持计算研究和教育的计算设计的混合软材料为基础的纳米粒子所需的特性。PI的方法将使用一种包括不同长度和时间尺度的基本物理和化学的方法。该方法将得到先进网络基础设施的开发和使用的帮助。这项研究将支持开展活动，激发高中生对科学、工程和数学的兴趣。此外，该研究将用于参与并告知公众计算在材料设计中对社会的作用以及大学教育的好处。最后，这项研究将通过提高本科生对材料设计先进计算方法和工具的认识，数学和物理科学理事会的材料研究部和计算机和信息科学与工程理事会的高级网络基础设施办公室提供资金为这个项目。该职业奖支持计算研究，网络基础设施开发和教育，以设计具有所需特性的纳米粒子。PI旨在通过开发和使用网络支持的多尺度方法，提高设计具有特定结构-性能关系的基于混合软材料的纳米颗粒（NP）的能力。这种设计的概念将需要对分子构象、组织和流动性对不同分子种类的集体行为的作用以及由此对材料性质的作用有基本的理解。该研究位于软材料和先进计算的接口，并提供了一个机会，以提高认识，兴趣，招聘，保留和培训有竞争力的劳动力在科学，技术，工程和数学area.The PI旨在设计空间稳定的混合纳米粒子的形态优化存储各种分子。NP设计将需要理解分子性状和混合软材料的期望性质之间的联系。这将通过开发和使用多尺度方法来促进，该方法可以通过将分子动力学模拟和分析工具与先进的网络基础设施相结合，将NP的组成细节与其所需的属性联系起来。该计划将通过三个目标来实现：（1）开发混合NP设计;（2）阐明pH对混合NP设计的作用;（3）预测和验证包含替代化学物种的混合NP设计。通过理解分子特征和结构-性质关系之间的关系，将显著加速预测具有所需性质的基于软材料的纳米颗粒。设计规则和方法可以扩展到设想其他多组分软材料为基础的系统有针对性的结构-性能关系。此外，在虚拟软材料设计中使用先进的计算工具可以促进社区采用新的计算方法。这将促进基于新软材料的创新和技术的加速发展。

项目成果

Self-Organization of Mobile, Polyelectrolytic Dendrons on Stable, Amphiphile-Based Spherical Surfaces

稳定的基于两亲物的球表面上移动聚电解树枝的自组织

• DOI: 10.1021/acs.langmuir.2c03386
• 发表时间: 2023
• 期刊: Langmuir
• 影响因子: 3.9
• 作者: Banerjee, Akash;Dutt, Meenakshi
• 通讯作者: Dutt, Meenakshi

A coarse-grained Molecular Dynamics study of phase behavior in Co-assembled lipomimetic oligopeptides

共组装脂质寡肽相行为的粗粒度分子动力学研究

• DOI: 10.1016/j.jmgm.2023.108624
• 发表时间: 2023
• 期刊: Journal of Molecular Graphics and Modelling
• 影响因子: 2.9
• 作者: Mushnoori, Srinivas;Lu, Chien Y.;Schmidt, Kassandra;Dutt, Meenakshi
• 通讯作者: Dutt, Meenakshi

Self-Organization of Alpha Helical Proteins in Bioinspired Membranes and Vesicles

仿生膜和囊泡中α螺旋蛋白的自组织

• DOI: 10.26434/chemrxiv.12746609.v1
• 发表时间: 2020
• 期刊: ChemRxiv
• 作者: Akash Banerjee, Zachary Finkel

Peptide-based vesicles and droplets: a review

基于肽的囊泡和液滴：综述

• DOI: 10.1088/1361-648x/abb995
• 发表时间: 2020
• 期刊: Journal of Physics: Condensed Matter
• 作者: Mushnoori, Srinivas;Lu, Chien Y;Schmidt, Kassandra;Zang, Ethan;Dutt, Meenakshi
• 通讯作者: Dutt, Meenakshi

Symmetry-specific orientational order parameters for complex structures

复杂结构的对称性特定取向顺序参数

• DOI: 10.1063/5.0076915
• 发表时间: 2022
• 期刊: The Journal of Chemical Physics
• 作者: Logan, Jack A.;Mushnoori, Srinivas;Dutt, Meenakshi;Tkachenko, Alexei V.
• 通讯作者: Tkachenko, Alexei V.