电子级氮气中超痕量杂质的质谱分析方法及测量不确定度评估
Determination of Ultra-Trace Impurities in Electronic Grade Nitrogen by Mass Spectrometry with Evaluation of Measurement Uncertainty
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摘要: 采用大气压电离质谱（APIMS）技术及标准添加法建立了一种电子级氮气中超痕量杂质的分析方法。以高效纯化后氮气中的5种杂质（CO、CH4、H2、O2和CO2）作为检测对象，以动态稀释后的标准气体作为参考进行回归分析，标准气体依据ISO 6142及ISO导则34规定的重量法在实验室制备，同时采用蒙特卡洛（MCM）法及《不确定度表示指南》描述的GUM法对本方法的不确定度进行评定。结果表明：在0~1 nmol/mol范围内，5种杂质的校准曲线呈现出良好的线性关系，线性相关系数R2均大于0.998；方法的灵敏度高，检出限可达几至几十pmol/mol水平，扩展不确定度为37%（k=2），通过与文献中报道的数据相比，CH4和CO的检出限分别改善了1个和2个数量级，H2、O2、CO2则与文献的报道值基本一致。本方法能够满足对相关电子气体中杂质的分析要求，并可为量值溯源提供参考依据。
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关键词:
- 电子级氮气 /
- 纯化 /
- 超痕量杂质 /
- 大气压电离质谱（APIMS） /
- 不确定度
Abstract: A determination method for the ultra-trace impurities in electronic grade nitrogen was developed by using atmospheric pressure ionization mass spectrometry (APIMS) with standard addition method. 5 impurities (CO, CH4, H2, O2, CO2) in high purity nitrogen were detected after purifying via a high-efficiency purifier. The standard gas used for calibrating APIMS was gravimetrically prepared in the lab according to the rules in ISO 6142 & ISO Guide 34, and it was added into the ionization source with different concentrations by dynamic dilution, then the fitting curve was built based on the data from different dilution points. Furtherly, the measurement uncertainty was evaluated through the method described in Guide to the expression of uncertainty in measurement (GUM) and Monte Carlo Method (MCM). The results show that all the 5 impurities have good linearity (R2>0.998) in the dynamic range of 0.1 nmol/mol, and the high sensitivity is achieved, the detection limits (LOD) reached to the low level of several to tens of pmol/mol with the expanded uncertainty of 37% (k=2). By compared with the previously reported data in the articles, the LOD values of magnitude for CH4 and CO are improved 1 and 2 orders, and the LOD values for other three impurities H2, O2, CO2 are the same levels as the reported data. It indicates that the method can be used for the relevant electronic gases for ultra-trace analysis with the traceability supporting. -
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