CAREER: Predicting Nanocrystal Superlattices Based on Ligand Interactions

该职业奖支持纳米粒子自组装基本问题的理论和计算研究和教育。纳米粒子是微小的物质，仅由几百到几千个原子组成，具有非凡的特性。当大量的纳米颗粒被排列成有序的模式（称为“超晶格”）时，它们可以作为高效的太阳能电池，可以作为医学和技术领域的新型传感器，还可以用作净化水和空气的过滤器。由于纳米粒子的体积小，不能一个粒子一个粒子地排列成超晶格。相反，研究人员利用“自组装”，这是一种受大自然启发的过程；自组装构建生物分子结构，如生物细胞。在自组装中，纳米粒子受到有利于有序模式自动形成的条件的影响。理想情况下，由于相互吸引力，纳米颗粒将自己排列到目标超晶格中。然而，这些微小粒子之间的作用力还没有被很好地理解，并且自组装经常导致无序的结构或与期望的不同的模式。在自组装过程中，纳米粒子之间最重要的作用力不是来自粒子本身，而是来自所谓的配体，即在纳米粒子表面形成柔软保护层的短原子链。在自组装过程中，邻近纳米颗粒上的配体以复杂的方式相互作用，这在实验中很难探测到。在这个项目中，PI和他的团队将开发新的理论模型和计算机模拟方法，这将增加对配体相互作用的理解。计算机模拟将用于揭示配体的长度和数量，以及它们与纳米粒子的相互作用，如何决定在自组装过程中形成什么样的超晶格。为此，将开发新的计算方法来模拟大量纳米颗粒及其配体的自组装。本研究结果将为未来的实验提供参考，并有助于为纳米粒子超晶格更有针对性的自组装铺平道路；它将为设计基于纳米颗粒的改进设备的广泛努力做出贡献。该奖项的教育活动侧重于提高学生在犹他大学本科物理化学课上的成功。研究表明，许多学生对物理化学课程的抽象概念和高等数学感到困惑。PI将通过开发一套计算机模拟练习来解决这些挑战，这些模拟练习旨在说明分子波动的关键概念，并帮助学生建立准确的心理模型和可靠的原子和分子行为直觉。该职业奖支持理论和计算研究和教育，使用分子模型和计算机模拟来预测纳米晶体的自组装，并提高学生在犹他大学本科物理化学课程中的成功。研究活动将集中于预测纳米晶体自组装过程中形成的有序超结构。获得对纳米晶体空间组织的精确实验控制对于调整纳米材料的性质至关重要。在自组装过程中，纳米晶体之间的相互作用是由覆盖纳米晶体表面的有机配体决定的。为了揭示配体在超晶格自组装中的复杂作用，研究小组将开发计算效率高的纳米晶体和配体分子模型。有了这些模型，研究小组将利用分子动力学模拟、自由能计算和蒙特卡罗采样来确定超晶格形成的热力学和动力学。该项目的具体目标是：1。建立粗粒度的烷基配体计算模型，通过多体相互作用捕捉不同溶剂环境的影响。计算纳米晶体超晶格的相图，作为纳米晶体形状、配体长度和覆盖、溶剂质量和含量的函数。通过自组装和超晶格转换的分子动力学模拟来确定超晶格的动力学可达性。3. 配体在纳米晶体表面的平衡分布，作为表面结合能和溶剂条件的函数。确定配体重排对自组装结果的影响。本项目旨在回答有关配体的结构和空间分布及其在自组装过程中的相互作用的基本问题，并揭示实验参数（包括纳米晶体尺寸和形状、配体参数和自组装方法）的相对重要性。该奖项支持的教育活动包括引入复杂的分子计算机模拟，由本科生进行和分析，作为犹他大学物理化学课程CHEM 3070的主要教学工具。在物理化学中，学生面临的一个主要挑战是将不熟悉的分子波动与熵等抽象概念联系起来。为了解决这个问题，PI将设计几个综合模拟活动，阐明微观波动和说明物理化学的关键概念。在这些活动中，学生将执行、分析和可视化复杂的计算机实验。课堂上概念的讨论将基于学生生成的模拟结果。此外，学生将使用计算机模拟作为研究和发现工具，这些活动将与纳米晶体自组装的研究活动紧密结合。材料研究部和化学部为该奖项提供资金。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

NONTECHNICAL SUMMARYThis CAREER award supports theoretical and computational research and education on fundamental questions in nanoparticle self-assembly. Nanoparticles are tiny pieces of matter, consisting of only a few hundred to thousands of atoms, and can have extraordinary properties. When large numbers of nanoparticles are arranged in ordered patterns, called "superlattices", they can act as highly efficient solar cells, can function as new types of sensors in medicine and technology, and can be used as filters to purify water and air. Because of their small size arranging nanoparticles into superlattices cannot be performed particle by particle. Instead, researchers utilize "self-assembly", a process inspired by nature; self-assembly builds bio-molecular structures like biological cell. In self-assembly, nanoparticles are subjected to conditions that favor automatic formation of ordered patterns. Ideally, nanoparticles arrange themselves into the targeted superlattice due to mutually attractive forces. However, the forces between these tiny particles are not well understood and self-assembly often results in disordered structures or in patterns that are different from the desired one.The most important forces between nanoparticles during self-assembly do not come from the particles themselves, but from so-called ligands, short chains of atoms that form a soft protective layer on the surface of nanoparticles. During self-assembly, ligands on nearby nanoparticles interact with each other in complicated ways that are difficult to probe in experiments. In this project, the PI and his team will develop new theoretical models and computer simulation methods that will increase understanding of ligand interactions. Computer simulations will be used to reveal how the length and number of ligands, as well as their interactions with the nanoparticles, determine what superlattice will form during self-assembly. To this end, new computational methods will be developed that allow the simulation of the self-assembly of large numbers nanoparticles and their ligands. The results of this research will provide a reference for future experiments and will help pave the way for more targeted self-assembly of nanoparticle superlattices; it will contribute to broad efforts to engineer improved devices based on nanoparticles.Educational activities of this award focus on improving student success in an undergraduate Physical Chemistry class at the University of Utah. Research has shown that many students struggle with the abstract concepts and advanced mathematics of Physical Chemistry courses. The PI will address these challenges by developing a set of computer simulation exercises that are designed to illustrate key concepts of molecular fluctuations and help student build accurate mental models and reliable intuition for the behavior of atoms and molecules. TECHNICAL SUMMARYThis CAREER award supports theoretical and computational research and education that uses molecular models and computer simulations to predict the self-assembly of nanocrystals and to improve student success in an undergraduate physical chemistry course at the University of Utah. Research activities will focus on predicting ordered superstructures that form during the self-assembly of nanocrystals. Gaining precise experimental control over the spatial organization of nanocrystals is paramount for tuning the properties of nanomaterials. Interactions between nanocrystals during self-assembly are determined by organic ligands that cover the surface of nanocrystals. To reveal the complex role of ligands in the self-assembly of superlattices, the research team will develop computationally efficient molecular models of nanocrystals and ligands. With these models, the research team will determine the thermodynamics and kinetics of superlattice formation, using molecular dynamics simulation, free energy calculations, and Monte Carlo sampling. The specific aims of the project are:1. Develop coarse-grained computational models of alkyl ligands that capture effects of different solvent environments through many-body interactions.2. Compute phase diagrams for nanocrystal superlattices as a function of nanocrystal shape, ligand length and coverage, and solvent quality and content. Determine kinetic accessibility of superlattices by molecular dynamics simulations of self-assembly and superlattice transformations. 3. Sample equilibrium distributions of ligands on nanocrystal surfaces, as a function of surface binding energies and solvent conditions. Determine effects of ligand rearrangements on self-assembly outcomes.This project is aimed to answer fundamental questions concerning the structure and spatial distribution of ligands and their interactions during self-assembly and reveal the relative importance of experimental parameters including nanocrystal size and shape, ligand parameters, and self-assembly methods. Educational activities supported under this award include introducing sophisticated molecular computer simulations, performed and analyzed by undergraduate students, as a main teaching tool in the physical chemistry course CHEM 3070 at the University of Utah. A major challenge for students in physical chemistry is to connect unfamiliar molecular fluctuations with abstract concepts like entropy. To address this problem, the PI will design several comprehensive simulation activities that elucidate microscopic fluctuations and illustrate key concepts of physical chemistry. In these activities, students will perform, analyze, and visualize sophisticated computer experiments. Discussion of concepts in class will be based on student-generated simulation results. Furthermore, students will use computer simulations as research and discovery tools, and these activities will be tightly integrated with research activities on nanocrystal self-assembly. The Division of Materials Research and the Division of Chemistry contribute funds to this award.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.