拉曼光谱
材料科学
分光计
分析化学(期刊)
计算机科学
光学
化学
物理
色谱法
作者
Stipe Lukin,Krunoslav Užarević,Ivan Halász
出处
期刊:Nature Protocols
[Springer Nature]
日期:2021-06-04
卷期号:16 (7): 3492-3521
被引量:54
标识
DOI:10.1038/s41596-021-00545-x
摘要
Solid-state milling has emerged as an alternative, sustainable approach for preparing virtually all classes of compounds and materials. In situ reaction monitoring is essential to understanding the kinetics and mechanisms of these reactions, but it has proved difficult to use standard analytical techniques to analyze the contents of the closed, rapidly moving reaction chamber (jar). Monitoring by Raman spectroscopy is an attractive choice, because it allows uninterrupted data collection from the outside of a translucent milling jar. It complements the already established in situ monitoring based on powder X-ray diffraction, which has limited accessibility to the wider research community, because it requires a synchrotron X-ray source. The Raman spectroscopy monitoring setup used in this protocol consists of an affordable, small portable spectrometer, a laser source and a Raman probe. Translucent reaction jars, most commonly made from a plastic material, enable interaction of the laser beam with the solid sample residing inside the closed reaction jar and collection of Raman-scattered photons while the ball mill is in operation. Acquired Raman spectra are analyzed using commercial or open-source software for data analysis (e.g., MATLAB, Octave, Python, R). Plotting the Raman spectra versus time enables qualitative analysis of reaction paths. This is demonstrated for an example reaction: the formation in the solid state of a cocrystal between nicotinamide and salicylic acid. A more rigorous data analysis can be achieved using multivariate analysis. This protocol describes how to set up and use Raman spectroscopy for monitoring the course of solid-state reactions in vibratory ball mills, which will help increase our understanding of the mechanisms and kinetics of mechanochemical reactions.
科研通智能强力驱动
Strongly Powered by AbleSci AI