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Detecting Trace Gases at Ppm Levels with Low Temperature Plasma Optical Emission Spectroscopy

机译:用低温等离子体光发射光谱检测PPM水平的痕量气体

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The detection of trace levels of molecular gases has gained increasing attention in many fields from atmospheric pollution and climate change monitoring to industrial safety and breath analysis for clinical diagnosis. Established techniques e.g. mass spectrometry, gas chromatography, electrochemical offer accuracy but are bulky and expensive. Apart from improving limits of detection (LOD) and increasing the number of target species, there is a major drive towards system miniaturisation and cost reduction in order to enhance field deployment e.g. for rapid continuous environmental monitoring via autonomous distributed networks or point of care clinical breath screening. Tuneable diode laser IR absorption spectroscopy (TLDAS) and atomic emission spectroscopy ICP-AES are routine laboratory spectroscopic techniques where future miniaturisation research includes, for example, microwave, photoacoustic-MEMS, broadband tuneable quantum cascade lasers or supercontinuum IR lasers, high sensitivity nanomaterials, among others. The advent of non-equilibrium low-temperature (NELT) atmospheric pressure plasmas opens up the possibility of using plasma optical emission spectroscopy (OES) for portable and/or low-cost detection in a diverse range of applications. However the poor quality of the spectra from these plasmas requires additional machine learning techniques to develop accurate models based on optical emission training samples. Our original work involving unsupervised principal component analysis indicated significant cluster separation even at sub-ppm levels of e.g. NO impurity gases. Recently we have investigated supervised learning using Partial Least Squares Discriminant Analysis (PLS-DA) using a dataset of He-CH_4 spectra where the CH_4 concentration varies from 0 - 100 ppm. Methane is a important hydrocarbon gas found in a number of fields from breath analysis to natural gas production and research is ongoing into accurate environmental CH_4 detectors in the ppm range.
机译:在大气污染和气候变化监测到临床诊断的工业安全和呼气分析,许多领域的痕量分子气体的检测增加了许多领域。建立了技术。质谱,气相色谱,电化学提供精度,但庞大且昂贵。除了改善检测限的限制并增加目标物种的数量,还有一个主要的驱动,朝着系统小型化和成本降低,以提高现场部署。通过自主分布式网络或护理点临床呼吸筛选的快速连续环境监测。可调谐二极管激光IR吸收光谱(TLDAS)和原子发射光谱ICP-AES是常规的实验室光谱技术,其中未来的小型化研究包括例如微波,光声 - MEMS,宽带可调量子级联激光器或超连续IR激光器,高灵敏度纳米材料,其中。非平衡低温(NELT)大气压等离子体的出现开辟了使用等离子体光发射光谱(OES)在各种应用范围内使用等离子体发射光谱(OES)的可能性。然而,来自这些等离子体的光谱的质量差需要额外的机器学习技术来开发基于光学发射训练样本的准确模型。我们涉及无监督的主成分分析的原创作品表明即使在例如次PPM水平下也表明了显着的聚类分离。没有杂质气体。最近,我们使用部分最小二乘判别分析(PLS-DA)使用HE-CH_4光谱的数据集来调查监督学习,其中CH_4浓度从0-100ppm变化。甲烷是一种重要的碳氢化合物气体,其在许多领域中发现,从呼吸分析到天然气生产,研究在PPM范围内持续到精确的环境CH_4探测器中。

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