DMREF: Design of Nanoscale Alloy Catalysts from First Principles

约翰霍普金斯大学的Tim Mueller和Chao Wang获得了由化学部资助的设计材料革命和工程未来（DMREF）计划的奖项，以开发合理设计新合金纳米颗粒催化剂的方法。 合金纳米颗粒是用作先进催化剂的有前景的材料，其以相对低的成本提高化学过程的能量效率。 可以通过调整合金纳米颗粒催化剂的尺寸、形状、原子结构和化学组成来改变其性能。 Mueller和Wang使用的研究方法利用了这种灵活性。 他们创建的计算模型能够准确预测纳米颗粒的制造条件如何影响其催化性能。 通过与实验结果的迭代比较，对这些模型进行了改进和验证。 作为概念验证，这种方法被用于设计促进二氧化碳转化为碳氢化合物燃料的催化剂。 将二氧化碳转化为燃料可以同时增加全球燃料供应和减少大气中的二氧化碳含量;但由于缺乏合适的催化剂，这在经济上尚不可行。 这项研究与一个全面的教育推广计划相结合，该计划包括为一名女高中生提供研究机会，举办能源技术研讨会，以及在约翰霍普金斯大学开设可再生能源技术新课程。该项目有两个主要目标。 首先，研究人员正在开发一种预测模型，将合成条件与纳米颗粒结构联系起来。为了实现这一点，基于簇展开的计算模型在从头算数据上进行训练，并用于预测合金纳米颗粒的原子结构。然后将预测的结构与实验确定的由有机溶液合成产生的单分散和均匀的合金纳米颗粒的结构特征进行比较。计算和实验方法被迭代地改进，直到它们是一致的，从而产生具有强大预测能力的模型。 在第二个方面，研究人员正在开发和验证一种将纳米颗粒结构与催化性能联系起来的预测模型。采用密度泛函理论计算了纳米颗粒表面关键中间吸附物的结合能，并将这些结合能用于计算氢电极模型，计算合金纳米颗粒的催化性能。为了迭代地改进计算模型，将不同尺寸、组成和表面结构的纳米颗粒的计算预测与实验催化和光谱研究进行比较。 为了证明和验证这种方法的有效性，研究人员正在设计和测试用于电化学CO2还原的铜合金纳米颗粒催化剂。

Tim Mueller and Chao Wang of Johns Hopkins University are supported by an award funded by the Division of Chemistry in the Designing Materials to Revolutionize and Engineer our Future (DMREF) program to develop a way to rationally design new alloy nanoparticle catalysts. Alloy nanoparticles are promising materials for use as advanced catalysts that increase the energy efficiency of chemical processes at relatively low cost. It is possible to modify the performance of alloy nanoparticle catalysts by adjusting their size, shape, atomic structure, and chemical composition. The research approach used by Mueller and Wang takes advantage of this flexibility. They create computational models that are able to accurately predict how the conditions under which nanoparticles are made affect their catalytic performance. These models are refined and validated by iterative comparisons with experimental results. As a proof of concept, this approach is being used to design catalysts that facilitate the conversion of carbon dioxide into hydrocarbon fuels. The conversion of carbon dioxide into fuels could simultaneously increase global fuel supply and reduce the amount of carbon dioxide in the atmosphere; but it is not yet economically viable due to the lack of suitable catalysts. This research is integrated with a comprehensive educational outreach program that includes research opportunities for a female high school student, a workshop on energy technologies, and a new course on renewable energy technologies at Johns Hopkins University. This project has two primary thrusts. In the first, the researchers are developing a predictive model that relates synthesis conditions to nanoparticle structure. To accomplish this, computational models based on cluster expansions are trained on ab-initio data and used to predict the atomic structures of alloy nanoparticles. The predicted structures are then compared to the experimentally-determined structural characteristics of monodisperse and homogeneous alloy nanoparticles created from organic solution synthesis. The computational and experimental approaches are iteratively refined until they are consistent, resulting in a model with strong predictive power. In the second thrust, the researchers are developing and validating a predictive model that relates nanoparticle structure to catalytic properties. Density functional theory is used to calculate the binding energies of key intermediate adsorbates on the surfaces of nanoparticles, and these energies are used in the computational hydrogen electrode model to calculate catalytic properties of the alloy nanoparticles. To iteratively refine the computational models, computational predictions for nanoparticles of different sizes, compositions, and surface structures are compared to experimental catalytic and spectroscopic studies. To demonstrate and validate the effectiveness of this approach, the researchers are designing and testing Cu-alloy nanoparticle catalysts for electrochemical CO2 reduction.