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相关概念视频

Raman Spectroscopy: Overview01:20

Raman Spectroscopy: Overview

295
The underlying principle of Raman spectroscopy is based on the interaction between light and matter, specifically molecules' inelastic scattering of photons. When a monochromatic beam of light, typically from a laser source, interacts with a sample, most scattered light has the same frequency as the incident light. This is known as Rayleigh scattering.
However, a small fraction of the scattered light exhibits a frequency shift due to the exchange of energy between the incident photons and...
295
Raman Spectroscopy Instrumentation: Overview01:26

Raman Spectroscopy Instrumentation: Overview

286
A conventional Raman spectrophotometer includes a laser source, a sample holding system, a wavelength selector, and a detector.
The monochromatic laser source, typically using visible or near-infrared radiation, generates a highly focused beam of light. This light interacts with the molecules of the sample, scattering some of the light. Liquid and gaseous samples are usually tested in ordinary glass capillaries, while solids can be analyzed as powders packed in capillaries or as potassium...
286
Olefin Metathesis Polymerization: Ring-Opening Metathesis Polymerization (ROMP)01:16

Olefin Metathesis Polymerization: Ring-Opening Metathesis Polymerization (ROMP)

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Ring-opening metathesis polymerization or ROMP involves strained cycloalkenes as starting materials. The mechanism of ROMP proceeds by reacting cycloalkene with Grubbs catalyst to give metallacyclobutane intermediate which undergoes a ring-opening reaction to form new carbene. The new carbene reacts with another molecule of cycloalkene. Repetition of these steps leads to the formation of an unsaturated open-chain polymer product. All these steps are reversible, however, relieving the ring...
2.5K
Olefin Metathesis Polymerization: Overview01:13

Olefin Metathesis Polymerization: Overview

2.0K
Recently, the development of olefin metathesis polymerization advanced the field of polymer synthesis. Simply put, the reorganization of substituents on their double bonds between two olefins in the presence of a catalyst is known as the olefin metathesis reaction. The use of metathesis reaction for polymer synthesis is called olefin metathesis polymerization.
Ruthenium-based Grubbs catalyst is the most commonly used catalyst for olefin metathesis polymerization. Grubbs catalyst consists...
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Free-Radical Chain Reaction and Polymerization of Alkenes02:35

Free-Radical Chain Reaction and Polymerization of Alkenes

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The conversion of alkenes to macromolecules called polymers is a reaction of high commercial importance. The structure of the polymer is defined by a repeating unit, while the terminal groups are considered insignificant. The average degree of polymerization represents the number of repeating units in the polymer molecule and is denoted by the subscript n.
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Mass Spectrometry: Branched Alkane Fragmentation01:29

Mass Spectrometry: Branched Alkane Fragmentation

892
This lesson delves into the mass spectrometry of branched alkane fragmentation. Branched alkanes possess secondary or tertiary carbon atoms, which generate relatively stable carbocations if the cleavage occurs at the branching point. The high stability of carbocations drives the instant fragmentation of branched alkanes. Accordingly, the branched alkane's molecular ion peak is very weak or invisible in the mass spectra, especially in comparison to a linear alkane.
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相关实验视频

Updated: May 27, 2025

Raman and IR Spectroelectrochemical Methods as Tools to Analyze Conjugated Organic Compounds
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基于机器学习技术的olefin聚合和裂变的拉曼光谱特征提取和分析方法.

Yaolan Yang1, Jijiang Hu1, Shaojie Zheng1

  • 1State Key Laboratory of Chemical Engineering, College of Chemical and Biological Engineering, Zhejiang University, Hangzhou 310030, Zhejiang, China. yaozhen@zju.edu.cn.

Analytical methods : advancing methods and applications
|February 17, 2025
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概括

这项研究优化了XGBoost的机器学习,用于拉曼光谱气体分析. 它提高了识别气体混合物的准确性,使其成为实时化学过程监测的理想选择.

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相关实验视频

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科学领域:

  • 分析化学 分析化学
  • 频谱学是一种光谱学.
  • 机器学习 机器学习

背景情况:

  • 拉曼光谱技术提供快速,灵敏和成本效益的实时气体监测.
  • 拉曼数据中的高维度,光谱重叠和噪声挑战了传统的混合物组成分析.
  • 精确的气体成分确定对于复杂的化学过程监测至关重要.

研究的目的:

  • 优化 XGBoost 机器学习模型,使用拉曼光谱数据进行增强的气体成分预测.
  • 评估不同特征提取和选择方法在提高预测准确性的有效性.
  • 为了比较XGBoost与其他机器学习模型的性能,用于拉曼光谱分析.

主要方法:

  • 利用了包括,乙烯,烯和丁烯在内的气体混合物的拉曼光谱数据.
  • 实施并比较了三种不同的特征提取和选择技术.
  • 培训和评估了XGBoost,决策树,随机森林,支持矢量机器和神经网络.

主要成果:

  • 优化的XGBoost模型在从拉曼光谱中预测气体成分方面表现出卓越的准确性和概括能力.
  • 在定量分析中,XGBoost的表现优于决策树,随机森林,支持矢量机器和神经网络.
  • 特征提取和选择方法显著提高了XGBoost模型的预测性能.

结论:

  • XGBoost 是一种高效的机器学习模型,用于对复杂的拉曼光谱数据进行定量分析.
  • 优化的XGBoost方法为化学过程中的实时气体成分监测提供了强大的解决方案.
  • 这项工作突出了先进机器学习技术的潜力,以克服光谱数据分析的局限性.