ITR/AP(CHE):Characterization of Complex Polymeric Materials using Visualization of Multidimensional Data Sets from Multiple Analytical Technique Fusion

ITR/AP(CHE)：利用多种分析技术融合的多维数据集可视化表征复杂聚合物材料

• 批准号: 0350666
• 负责人: Julia Fulghum
• 金额: $ 30.59万
• 依托单位: University of New Mexico
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2003
• 资助国家: 美国
• 起止时间: 2003-09-01 至 2005-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0350666&HistoricalAwards=false
• 关键词: ITR; AP; CHE; Characterization; Complex

Julia Fulghum and Javed Khan of Kent State University are supported by the Division of Chemistry under the Information Technology Research (ITR) program to create new methods for the non-destructive, three-dimensional characterization of heterogeneous, solid polymeric materials. Data will be acquired from polymer samples of increasing complexity using a variety of analytical techniques, including x-ray photoelectron spectroscopy, time-of-flight secondary ion mass spectrometry, and Fourier transform infrared spectroscopy. All of these methods can be used to obtain chemical information from different sample volumes, and to generate multidimensional data sets. Multivariate statistical analysis and classification methods will be used to enhance interpretation of the acquired data sets, ultimately enabling creation of a unified polymer framework, called an active knowledge mesh. The combined system will create synergy between the computer and the user in large and complex information space exploration, posing interesting challenges for multi-modal data analysis and its multi-perspective visualization.Polymers are widely used in a variety of high technology applications. For example, many current and proposed biomaterials are polymers, and polymers are contained as well in composites used for a light aircraft and automobiles. Commercially important materials often involve molecularly complex polymer blends whose surface chemistry and structure are particularly challenging to analyze. Outcomes from this project are expected to improve the understanding and evaluation of complex polymer blend characteristics, and ultimately enable enhanced control of technologically important materials properties.

肯特州立大学的 Julia Fulghum 和 Javed Khan 在信息技术研究 (ITR) 计划化学系的支持下，创建了非破坏性、三维表征异质固体聚合物材料的新方法。 将使用各种分析技术从日益复杂的聚合物样品中获取数据，包括 X 射线光电子能谱、飞行时间二次离子质谱和傅里叶变换红外光谱。 所有这些方法都可用于从不同的样品体积中获取化学信息，并生成多维数据集。 多元统计分析和分类方法将用于增强对所获取数据集的解释，最终能够创建一个统一的聚合物框架，称为主动知识网格。 该组合系统将在大型复杂信息空间探索中在​​计算机和用户之间产生协同作用，为多模态数据分析及其多视角可视化提出有趣的挑战。聚合物广泛应用于各种高科技应用。 例如，许多当前和提议的生物材料是聚合物，并且聚合物也包含在用于轻型飞机和汽车的复合材料中。 商业上重要的材料通常涉及分子复杂的聚合物共混物，其表面化学和结构分析起来特别困难。 该项目的成果预计将提高对复杂聚合物共混物特性的理解和评估，并最终增强对技术上重要的材料性能的控制。