Collaborative Research: The Role of the Substrate Surface State and Molecular Configuration in Porous Honeycomb Networks of Quinones

美国国家科学基金会数学与物理科学理事会化学系的分析表面化学项目将支持马里兰大学帕克分校的西奥多·l·爱因斯坦和加州大学河滨分校的路德维希·巴特尔斯的研究。这项工作的目标是了解特定的有机分子如何组装成直径为5纳米的开放孔的大型规则蜂窝模式。为了探索这种行为的普遍性，巴特尔斯将在不同的分子和衬底材料上进行实验，并结合爱因斯坦的理论计算，试图用氢键来解释短程结构，用衬底的电子结构来解释大孔隙。掌握了相互作用机制后，巴特尔斯和爱因斯坦将设法设计开孔的大小，或许还能设计分子网络的模式。这样的表面可以作为在表面上生长图案薄膜的模板，应用范围从存储技术到多相催化。教育方面的好处包括培养研究生和博士后在原子尺度成像和模拟系统方面的最新技术，在这些系统中，相对较小的能量和微妙的影响决定了整体的几何形状。它也是物理基础理论与化学基础实验之间协同和密切合作的绝佳机会，为参与的学生提供了高度跨学科的培训。

The Analytical & Surface Chemistry Program of the Division of Chemistry, in the Directorate of Mathematical & Physical Sciences of the National Science Foundation, will support research by Theodore L. Einstein of the University of Maryland, College Park and Ludwig Bartels of the University of California, Riverside. The goal of this work is to understand how particular organic molecules assemble into large regular honeycomb patterns with open pores having diameters of 5 nm. To explore how general this behavior is, Bartels will perform experiments on different molecules and substrate materials in conjunction with theoretical calculations by Einstein that seek to explain the short-range structure in terms of hydrogen bonds and the large pores in terms of the electronic structure of the substrate. With a command of the interaction mechanisms, Bartels and Einstein will seek to engineer the size of open pores and perhaps also the pattern of the molecular networks. Such surfaces can serve as templates for growing patterned films on surfaces for applications ranging from storage technology to heterogeneous catalysis. Educational benefits include the training of graduate students and postdoctoral associates in state-of-the-art techniques for imaging at the atomic scale and for simulating systems in which comparatively small energies and subtle effects determine the overall geometry. It is also an outstanding opportunity for synergistic and close collaboration between physics-based theory and chemistry-based experiment, offering the involved students highly interdisciplinary training.