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基于碳核磁共振分析的热解油碳、氢、氧含量的定量碳核磁共振表征及模拟方法的开发。

Development of quantitative C NMR characterization and simulation of C, H, and O content for pyrolysis oils based on C NMR analysis.

作者信息

Wang Rui, Luo Ying, Jia Hang, Ferrell Jack R, Ben Haoxi

机构信息

Southeast University Nanjing 210096 China

Key Laboratory of Energy Thermal Conversion and Control of Ministry of Education Nanjing 210096 China.

出版信息

RSC Adv. 2020 Jul 9;10(43):25918-25928. doi: 10.1039/d0ra02376k. eCollection 2020 Jul 3.

Abstract

Bio-oil is a valuable liquid product obtained from pyrolysis of biomass and it contains tens of hundreds of compounds, which brings about difficulties for characterization with various analytical methods. C NMR has advantages over other detection methods as it can characterize the entire composition of bio-oil and distinguish different types of carbon. But various shortcomings limit the application of C NMR. This study was carried out to develop a quantitative C NMR method to determine different functional groups in pyrolysis bio-oils with short NMR time and good accuracy, and propose a simulation of C, H, and O content for pyrolysis oils based on C NMR analysis. In order to solve long-term NMR problems, relax reagent has been added and the results show that it is an effective way to shorten the NMR time. Moreover, the aging problem is not obvious in the short-term NMR test, so the effect of aging on the test results can be neglected. Three types of substances with different oxygen content have been employed to verify the feasibility of the C, H, and O calculation methods and the result errors of all elements are small, which shows it is reliable for the simulation data of C, H and O content.

摘要

生物油是一种通过生物质热解获得的有价值的液体产品,它含有成百上千种化合物,这给用各种分析方法进行表征带来了困难。碳核磁共振(¹³C NMR)相对于其他检测方法具有优势,因为它可以表征生物油的整体组成并区分不同类型的碳。但¹³C NMR的各种缺点限制了其应用。本研究旨在开发一种定量¹³C NMR方法,以在短核磁共振时间和高精度的情况下测定热解生物油中的不同官能团,并基于¹³C NMR分析对热解油的碳、氢和氧含量进行模拟。为了解决长期核磁共振问题,添加了弛豫试剂,结果表明这是缩短核磁共振时间的有效方法。此外,在短期核磁共振测试中老化问题不明显,因此可以忽略老化对测试结果的影响。使用了三种不同氧含量的物质来验证碳、氢和氧计算方法的可行性,所有元素的结果误差都很小,这表明其对碳、氢和氧含量的模拟数据是可靠的。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49c6/9055335/e56b26cec9ef/d0ra02376k-f1.jpg

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