识别复杂原料的反应网络假设,从光谱测量与最小的人类干预的光谱测量
Karthik Srinivasan1, Anjana Puliyanda1, Vinay Prasad1
1Department of Chemical and Materials Engineering, Donadeo Innovation Centre for Engineering, 9211, 116st NW, Edmonton T6G 1H9, AB, Canada.
The journal of physical chemistry. A
|June 5, 2024
概括
本研究引入了一种自动化协议,用于使用光谱数据从复杂的未知原料生成反应网络假设. 该方法准确地预测了合成和现实生物质液化过程的反应途径.
科学领域:
- 化学工程是化学工程的重要组成部分.
- 计算化学计算化学
- 频谱学是一种光谱学.
背景情况:
- 了解复杂的化学反应,尤其是未知的原料,对于过程优化至关重要.
- 反应网络阐明的传统方法往往是手动的,耗时的,范围有限的.
研究的目的:
- 开发一个自动化的协议,用于生成反应网络假设,用于使用复杂和未知的原料的过程.
- 证明该方法在不同的反应系统中具有过程不可知的适用性.
主要方法:
- 光谱数据分解以隔离单个组件签名.
- 使用1D卷积神经网络进行自动功能组识别.
- 采用原子对原子映射,从化学数据库中获取的候选分子中推导出反应规则.
主要成果:
- 使用合成数据成功生成接近基本真相的反应网络假设.
- 从实验数据中精确回收生物质热解相关的候选分子和反应网络.
- 在不同复杂度的合成和现实世界数据集中证明了准确性.
结论:
- 开发的自动化协议有效地产生复杂原料的反应网络假设.
- 该方法对化学过程分析,特别是生物质转化中的应用具有前景.
- 这种方法提高了反应路径发现在不良特征系统的效率和准确性.
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