Computational Methods for Ensemble Averaged Surface-Enhanced Raman Scattering

系综平均表面增强拉曼散射的计算方法

• 批准号: 2312222
• 负责人: Lasse Jensen
• 金额: $ 52.58万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-06-01 至 2026-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2312222&HistoricalAwards=false
• 关键词: Computational; Methods; Ensemble; Averaged; Surface

With support from the Chemical Theory, Models and Computational Methods program in the Division of Chemistry, Professor Lasse Jensen of Pennsylvania State University will develop new theoretical methods to describe ensemble averaged surface-enhanced Raman scattering (SERS). SERS is a powerful technique that can be used to obtain detailed information about chemical reactions and the orientation of molecules on surfaces. Modeling is important to obtain this information but remains difficult due to the complicated interface and the need to average over many different molecular orientations. Professor Lasse Jensen and his team will develop new computational tools to describe this spectroscopy that will be disseminated to the broad scientific community through incorporation into available computational chemistry software packages. Using interactive simulators, Dr. Jensen will educate students on nano-concepts, machine learning and fundamental theory; and emphasize the interplay between theory and experiments. In this project, the Jensen team at Pennsylvania State University will develop a multiscale model that goes beyond the harmonic approximation and allows for sampling over molecular configurations such that ensemble averaged surface-enhanced Raman scattering (SERS) can be modeled. The proposed research will provide new theoretical tools for fundamental understanding of dynamics and orientation of molecules on nanoparticle surfaces for characterizing of interfaces of relevance for heterogeneous catalysis, photovoltaics, and molecular sensing. The combination of a machine-learning approach with an efficient multiscale model is expected to enable simulations of ensemble-averaged SERS spectra that include both electromagnetic and chemical enhancement mechanisms. The developed computational tools will be disseminated to a broad scientific community through incorporation into software packages.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.

在化学理论，模型和计算方法的支持下，宾夕法尼亚州立大学的Lasse Jensen教授将开发新的理论方法来描述集合平均表面增强的拉曼散射（SERS）。 SER是一种强大的技术，可用于获取有关化学反应和表面上分子方向的详细信息。建模对于获取此信息很重要，但是由于复杂的界面以及在许多不同的分子方向上平均的需要，因此仍然很困难。 Lasse Jensen教授及其团队将开发新的计算工具来描述这种光谱，该光谱将通过将可用的计算化学软件包纳入到广泛的科学界。詹森博士使用互动模拟器将教育学生有关纳米概念，机器学习和基本理论的教育。并强调理论与实验之间的相互作用。在这个项目中，宾夕法尼亚州立大学的Jensen团队将开发一个多尺度模型，该模型超出了谐波近似，并允许对分子构型进行采样，以便可以对整体平均表面增强的拉曼散射（SERS）进行建模。拟议的研究将为纳米颗粒表面上动力学和分子方向的基本理解提供新的理论工具，以表征与异质催化，光伏和分子传感相关的接口。机器学习方法与有效的多尺度模型的组合有望实现包括电磁和化学增强机制在内的合奏平均SERS光谱的模拟。开发的计算工具将通过纳入软件包将开发的计算工具传播到一个广泛的科学界。该奖项反映了NSF的法定任务，并认为使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估。