Excellence in Research: Development of High Resolution Coherent 2D Rovibrational Spectroscopy

Peter Chen教授在Spelman学院的研究小组正在开发一种新的高分辨率光谱技术，用于真实的研究气体混合物中的分子。 陈教授研究的新光谱方法监测了由气体混合物变化引起的非常小的信号偏移。 它可以在许多不同的领域，如研究燃烧系统，材料合成过程和催化。 由此产生的信息可用于发现和识别化学反应过程中产生的新物种，并提供了解反应机制所需的信息。 因此，它可以导致改进的方法，以减少污染，提高效率，并降低成本的制造，汽车运输，能源生产等陈教授密切合作，与一组本科生在斯佩尔曼学院，并准备他们在STEM领域的研究生学习。 他特别鼓励非裔美国女学生的研究，为他们提供一个刺激，前沿的本科研究经验，正在开发的技术是基于一个新的高分辨率形式的相干多维光谱，使用振转共振和非参数四波混频过程。从电子增强到振动增强的转变使得更容易检测样品中存在的所有物种。 使用新的非参数技术的优势进行了探讨。 新的方法也正在开发中，使用由此产生的多维模式来区分来自不同物种的信号，同时还提供有关其结构的信息。 陈教授在招募和参与本科生方面建立了良好的记录，其中许多学生来自代表性不足的少数群体。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

Professor Peter Chen's research team at Spelman College is developing a new high resolution spectroscopic technique to study molecules in gaseous mixtures in real time. The new spectroscopic method Professor Chen works on monitors very small signal shifts caused by the change in a gas mixture. It could be useful in many different areas, such as studying combustion systems, materials synthesis processes, and catalysis. The resulting information could be used to discover and identify new species produced during chemical reactions and provide information needed to understand reaction mechanisms. Consequently, it can lead to improved ways to reduce pollution, improve efficiency, and lower costs for manufacturing, automobile transportation, energy production, etc. Professor Chen works closely with a group of undergraduate students at Spelman College and prepares them for graduate studies in the STEM fields. He in particular encourages African American women students in research by providing them with a stimulating, cutting-edge undergraduate research experience.The technique that is being developed is based upon a new high resolution form of coherent multidimensional spectroscopy that uses rovibrational resonances and nonparametric four wave mixing processes. The shift from electronic enhancements to vibrational enhancements makes it easier to detect all species present in the sample. Advantages for using new nonparametric techniques are explored. New methods are also being developed that use the resulting multidimensional patterns to distinguish among signals from different species while also providing information on their structures. Professor Chen has established a strong record of recruiting and involving undergraduate students, many from underrepresented minority groups, on his project.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.