红外光谱差减技术扣除水汽吸收干扰的局限性

发布时间：2019-03-08 10:17

【摘要】：红外光谱差减技术在扣除背景组份干扰方面得到广泛应用,但其扣除空气中水汽效果却不尽如人意。研究了不同湿度的水汽光谱与光谱差减效果的关系,以探究光谱差减技术在水汽扣除领域局限性的原因。结果表明:(1)相对湿度改变,水汽的红外光谱也发生变化,不管如何小心地选择比例系数f,从相对湿度x%的水汽光谱Ax%,也不能完全扣除相对湿度为y%的水汽光谱Ay%,即fAx%≠Ay%。(2)相对湿度改变,水分子团簇(H_2O)n的相对组成也会发生变化,这是导致光谱差减技术局限性的主要原因。(3)将湿度为x%和y%的两水汽光谱Ax%和Ay%进行线性组合,则可以高度近似地模拟出介于两者之间的湿度的水汽光谱。比如用40%水汽谱和30%水汽谱,可以模拟得到32%或35%或37%的水汽谱。实验结果表明这是扣除水汽干扰效果更好的路径。(4)论证了水汽补偿湿度滴定法具有高效性的原因。
[Abstract]:The infrared spectrum subtraction technique has been widely used to deduct the interference of background components, but the effect of deducting water vapor in air is not satisfactory. The relationship between the spectrum of water vapor with different humidity and the effect of spectral subtraction is studied in order to explore the reason of the limitation of spectral subtraction technique in the field of water vapor deduction. The results show that: (1) the infrared spectrum of water vapor also changes with the change of relative humidity. No matter how carefully choose the ratio coefficient f, Ax%, from the relative humidity x% spectrum of water vapor. It is also not possible to completely deduct the water vapor spectrum (Ay%,) with y% relative humidity, that is, fAx% Aye%. (2) the relative composition of water molecular clusters (H _ 2O) _ n will also change with the change of relative humidity. This is the main reason leading to the limitation of spectral difference reduction technique. (3) the moisture spectrum of water vapor with humidity of x% and y% can be simulated by linear combination of Ax% and Ay% with humidity of x% and y%, and the moisture spectrum between them can be simulated in a highly approximate manner. For example, using 40% water vapor spectrum and 30% water vapor spectrum, 32% or 35% or 37% water vapor spectrum can be simulated. The experimental results show that this is a better way to deduct water vapor interference. (4) the reason for the high efficiency of moisture compensation titration is demonstrated.
【作者单位】：华南理工大学化学与化工学院;北京理工大学化学学院化学物理研究所;
【基金】：国家自然科学基金项目(21473009,20873136)资助
【分类号】：O657.33

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