Advanced molecular separations and data interpretation tools

先进的分子分离和数据解释工具

• 批准号: RGPIN-2016-05445
• 负责人: Harynuk, James
• 金额: $ 3.35万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=689202
• 关键词: Advanced; molecular; separations; data; interpretation

This research program focuses on improving the quantity and quality of information that can be gleaned from gas chromatographic (GC) analyses. GC and multidimensional GC are tools that are routinely used to separate, quantify, and identify small organic molecules (in silico expansion of our library using techniques for predicting properties based on molecular structures. The ultimate goal is the ability to draw a molecule and predict its behaviour in a given GC experiment. The marriage of these two aspects of the program will lead to an intelligent search algorithm to find important molecules in samples and postulate their molecular structures. This will be the first major advancement in this area of GC in decades. It will change how people use the technology. We will apply these methods to problems of identifying emerging designer steroids, identifying structures for synthetic cannabinoids, and detailed molecular characterization of petroleum samples.***HQP will also study fundamentals of separation mechanisms and chemical reactions of some analytes with the GC column (which is supposed to separate molecules without undergoing a chemical reaction) as well as studies of the pyrolysis of household materials. The latter is in support of our ongoing work into automated tools for the detection of ignitable liquids in fire debris. Finally, assuming that an equipment grant is successful, we will also explore a new spectroscopic detector for GC.**

该研究计划的重点是提高可以从气相色谱（GC）分析中收集的信息的数量和质量。GC和多维GC是常规用于分离、定量和鉴定小有机分子的工具（使用基于分子结构预测性质的技术对我们的文库进行计算机模拟扩展。最终目标是能够绘制分子并预测其在给定GC实验中的行为。这两个方面的结合将导致一个智能搜索算法，以找到样品中的重要分子，并假设它们的分子结构。这将是几十年来GC在这一领域的第一个重大进展。它将改变人们使用这项技术的方式。我们将应用这些方法来识别新兴的设计师类固醇，识别合成大麻素的结构，以及石油样品的详细分子表征。HQP还将研究一些分析物与GC柱的分离机制和化学反应的基本原理（该柱应该在不发生化学反应的情况下分离分子）以及家用材料的热解研究。后者是为了支持我们正在进行的自动化工具的工作，用于检测火灾碎片中的易燃液体。最后，假设设备赠款是成功的，我们还将探索一种新的GC光谱检测器。