权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

Collaborative Research: Design of Optimal Bimetallic Nanoparticles

合作研究：最佳双金属纳米粒子的设计

基本信息

批准号：
1634880
负责人：
Ioannis Bourmpakis
金额：
$ 35.04万
依托单位：
University of Pittsburgh
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2016
资助国家：
美国
起止时间：
2016-09-01 至 2020-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1634880&HistoricalAwards=false
关键词：
Collaborative Research Design Optimal Bimetallic

项目摘要

The scientific field of computational chemistry uses computer simulations to calculate the structures, properties, as well as, reactions of molecules and materials. These simulations can be envisioned as virtual experiments that generate rich information about the materials behavior with a great level of accuracy. Elucidating the mathematical functions that describe how the materials properties depend on their structural characteristics allows us to design optimal materials for targeted applications (structures that maximize a desired property). This methodology significantly reduces the need to perform numerous, time-consuming, and costly experiments in the lab, which are often based on extensive trial and error. This Design of Engineering Material Systems (DEMS) award supports fundamental research to design nanoparticles that consist of two different metals and are able to capture carbon dioxide, a molecule contributing to the greenhouse effect. The predictions from the computational research will be validated with targeted experiments in the lab. Since metal nanoparticles find a wide range of applications, it is expected that results from this research will affect the U.S. economy, society and the environment. A website will be developed to allow free access to a library of simulated structures. The multidisciplinary nature of this research, involving computational chemistry, materials design, optimization, scientific computation, materials synthesis and catalysis, will help broaden engineering education and attract underrepresented students to research. In addition, animation modules will be generated for incorporation in high school classes.This project creatively integrates first-principles calculations with rigorous engineering design methods, in order to develop a systematic framework to optimize nanoparticles in light of a performance metric (demonstrated via carbon dioxide adsorption), while also taking into account nanoparticle stability aspects. A novel design of experiments approach, tailored to the intricacies of this specific materials class, will be developed, while the computational predictions will be validated experimentally through targeted nanoparticle synthesis, characterization, and carbon dioxide adsorption experiments. Developing the capability to computationally identify nanoparticles that maximize their performance for a given application in a multi-dimensional composition-morphology space is crucial to guide future research efforts and accelerate nanomaterials discovery. However, efforts to-date have been focused entirely on one-dimensional optimizations (almost exclusively focused on metal composition). The present project will demonstrate the first strategy that truly explores the vast parameter space and hence enables true design of functional nanoparticles. In addition, this research advances the state-of-the-art in the study of bimetallic nanoparticles by developing structure-property relationships that will be applicable to any nanoparticle morphology. Finally, this project advances environmental science by designing bimetallic nanostructures for capturing and activating carbon dioxide, a key greenhouse gas.

计算化学的科学领域使用计算机模拟来计算分子和材料的结构，性质以及反应。这些模拟可以被设想为虚拟实验，以很高的准确度生成有关材料行为的丰富信息。阐明描述材料特性如何取决于其结构特征的数学函数，使我们能够为目标应用设计最佳材料（最大化所需特性的结构）。这种方法大大减少了在实验室中进行大量耗时且昂贵的实验的需要，这些实验通常基于大量的试验和错误。该工程材料系统设计（DEMS）奖支持基础研究，以设计由两种不同金属组成的纳米颗粒，并能够捕获二氧化碳，一种导致温室效应的分子。计算研究的预测将在实验室中通过有针对性的实验进行验证。由于金属纳米粒子的应用范围广泛，预计这项研究的结果将影响美国的经济，社会和环境。将开发一个网站，允许免费访问模拟结构库。这项研究的多学科性质，涉及计算化学，材料设计，优化，科学计算，材料合成和催化，将有助于拓宽工程教育，吸引代表性不足的学生进行研究。该项目创造性地将第一原理计算与严格的工程设计方法相结合，以开发一个系统框架，根据性能指标（通过二氧化碳吸附证明）优化纳米颗粒，同时考虑纳米颗粒的稳定性。将开发一种新的实验设计方法，针对这种特定材料类的复杂性，而计算预测将通过有针对性的纳米颗粒合成，表征和二氧化碳吸附实验进行实验验证。开发计算识别纳米颗粒的能力，以最大限度地提高其在多维组成-形态空间中特定应用的性能，对于指导未来的研究工作和加速纳米材料的发现至关重要。然而，迄今为止的努力完全集中在一维优化上（几乎完全集中在金属成分上）。本项目将展示第一个真正探索巨大参数空间的策略，从而实现功能纳米颗粒的真正设计。此外，本研究通过开发适用于任何纳米颗粒形态的结构-性质关系，推进了纳米颗粒研究的最新进展。最后，该项目通过设计用于捕获和激活二氧化碳（一种关键温室气体）的纳米结构来推进环境科学。

项目成果

期刊论文数量（10）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Identification of optimally stable nanocluster geometries via mathematical optimization and density-functional theory

通过数学优化和密度泛函理论识别最佳稳定的纳米团簇几何形状

DOI：
10.1039/c9me00108e
发表时间：
2020
期刊：
Molecular Systems Design & Engineering
影响因子：
3.6
作者：
Isenberg, Natalie M.;Taylor, Michael G.;Yan, Zihao;Hanselman, Christopher L.;Mpourmpakis, Giannis;Gounaris, Chrysanthos E.
通讯作者：
Gounaris, Chrysanthos E.

Predicting Metal–Support Interactions in Oxide-Supported Single-Atom Catalysts