Collaborative Research: Catalyst Structure, Reaction Mechanism, and Roles of Chlorine for Ethylene Epoxidation

环氧乙烷（EO）是生产各种先进材料、工业解决方案和表面活性剂的关键中间体。全球EO的产量超过270亿公斤，主要来源是原料乙烯与氧在银（Ag）催化剂上的选择性反应。Ag催化剂是由五种或五种以上元素的复杂组合来促进的，每一种元素在催化中都有不同的作用。氯（Cl）是提高乙烯转化为环氧乙烷选择性的主要促进剂之一。虽然目前工业工艺技术的EO选择性在90%以上，但在提高工艺效率和减少CO2排放的同时，还存在进一步提高选择性的机会。然而，氯增加选择性的相互作用尚未完全了解，从而限制了选择性的进一步提高。该项目结合了先进的实验、计算和机器学习方法，以更好地了解EO制造的催化机理和Cl助剂的作用。该项目将确定在与EO催化相关的压力和温度下，cl促进银纳米颗粒上的稳态活性相、表面结构和表面中间体。深入了解EO催化剂的操作和设计原理，以及EO选择性的提高，可能会在减少碳排放的同时，为美国化工行业带来革命性的进步。此外，该项目将通过对理论和实验研究小组之间的学生进行交叉培训，通过指导计划进行K-12外展，以及与针对STEM高中女性的教育机会相结合，加强合作机构的研究生和本科生教育。该项目探讨了许多先前研究中活性银催化剂的表面结构不代表催化剂在实际条件下运行的可能性。因此，主要的反应机制和Cl提高选择性的方式仍然不清楚，特别是在工业相关的反应条件和Cl覆盖率下。长期以来，对Cl作为促进剂的要求与表面氧原子的诱导反应性或氧化表面上负责燃烧的氧空位的选择性滴定有关，然而这些解释假设了依赖于催化剂表面结构的特定机制途径。本项目将建立环氧化和燃烧的机理和反应途径，以及这些途径如何涉及特定的氧中间体和氧诱导表面重建的氧空位。将获得更多关于吸附Cl原子如何影响表面重建，活性氧和空位的分布和覆盖，以及反应障碍，动力学和选择性的相关变化的见解。通过实验（时间分辨原位拉曼光谱）和计算方法（采用基于机器学习的分子动力学模拟）的结合来解决反应条件下催化系统的固有复杂性。更广泛地说，该项目将提供创建和验证方法（实验和计算）的机会，以解决催化过程中表面结构的变化，这是该领域的一个主要挑战。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。

Ethylene oxide (EO) is a key intermediate in the production of a wide array of advanced materials, industrial solutions, and surfactants. Worldwide production of EO exceeds 27 billion kilograms, and the primary source involves selective reaction of the raw feedstock, ethylene, with oxygen over silver (Ag) catalysts. The Ag catalysts are promoted with complex combinations of five or more elements, each with different roles in catalysis. Chlorine (Cl) is one of the main promoters used to enhance the selectivity of ethylene conversion to EO. Although the EO selectivity of current industrial process technology is above 90%, opportunity exists to further increase the selectivity while simultaneously improving process efficiency and decreasing CO2 emissions. Yet, the interactions by which Cl increases selectivity are not fully understood, thus limiting further improvements in selectivity. The project combines advanced experimental, computational, and machine learning methods to better understand the catalytic mechanism of EO manufacture and the role played by the Cl promoter. The project will identify the steady-state active phase, surface structure, and surface intermediates present on Cl-promoted Ag nanoparticles at pressures and temperatures relevant for EO catalysis. Deeper understanding of the operation and design principles for EO catalysts, and increases in EO selectivity, could lead to transformative advances in the U.S. chemicals industry while simultaneously decreasing carbon emissions. In addition, the project will enhance graduate and undergraduate student education at the partner institutions, through cross-training of students between theory and experimental research groups, K-12 outreach via mentorship programs, and integration with educational opportunities that target high school women in STEM. The project explores the likelihood that surface structures for active Ag catalysts in many prior studies do not represent catalysts operating at practical conditions. Consequently, the dominant reaction mechanisms and the ways in which Cl improves selectivity remain unclear, particularly at industrially relevant reaction conditions and Cl coverages. The requirement for Cl as a promoter has long been associated with either induced reactivity of surface oxygen atoms or the selective titration of oxygen vacancies responsible for combustion present on oxide surfaces, yet those explanations presume specific mechanistic pathways that are dependent on catalyst surface structure. This project will establish the mechanism and reaction pathways for epoxidation and combustion, and the ways by which those pathways involve specific oxygen intermediates and oxygen vacancies upon oxygen-induced surface reconstructions. Additional insight will be obtained regarding how adsorbed Cl atoms affect surface reconstructions, the distribution and coverages of reactive oxygen species and vacancies, and the associated changes in reaction barriers, kinetics and selectivity. The inherent complexity of the catalytic system under reaction conditions is addressed through a combination of experimental (time-resolved in situ Raman spectroscopy) and computational methodologies (grand canonical Monte Carlo and molecular dynamics simulations employing machine learning-based potentials). More broadly, this project will provide opportunities for creating and validating methods (both experimental and computational) that address changes in surface structure during catalysis, a major challenge in the field.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.