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基于选择性光饱和光谱的双物种单波长检测

Two species-one wavelength detection based on selective optical saturation spectroscopy.

作者信息

Sadiek Ibrahim, Friedrichs Gernot

机构信息

Institute of Physical Chemistry, Kiel University, Kiel, Germany.

Leibniz Institute for Plasma Science and Technology (INP), Greifswald, Germany.

出版信息

Sci Rep. 2023 Oct 10;13(1):17098. doi: 10.1038/s41598-023-44195-3.

Abstract

Cross-sensitivity limits accurate quantitative detection of species concentrations in all sensor technologies, including laser-based absorption techniques. Absorption sensors capture a signal that combines contributions from all interfering species at a given detection wavelength. Careful selection of the probed spectral line, broadband detection, or upstream separation can partially mitigate cross-sensitivity, however, weak or unidentified signal interference remains a challenge for accuracy. Here, we present a proof-of-principle study to overcome cross-sensitivity by taking advantage of the distinct optical saturation characteristics of different gas mixture components. By controlling the absorption contribution of a selected species by intentional optical saturation, simultaneous and quantitative detection of two interfering species becomes possible even without the need for spectral scanning, hence offering two species-one wavelength detection (2S1W) capability. Demonstrated with direct absorption and cavity-ringdown setups, the method offers a new, previously unexploited opportunity to further enhance laser-based analyzers for complex gas mixture analysis in environmental, medical, and technical applications.

摘要

交叉敏感性限制了所有传感器技术(包括基于激光的吸收技术)对物种浓度的准确定量检测。吸收式传感器捕获的信号是在给定检测波长下来自所有干扰物种贡献的组合。仔细选择探测谱线、宽带检测或上游分离可以部分减轻交叉敏感性,然而,微弱或未识别的信号干扰仍然是影响准确性的一个挑战。在此,我们展示了一项原理验证研究,通过利用不同气体混合物成分独特的光学饱和特性来克服交叉敏感性。通过有意的光学饱和控制选定物种的吸收贡献,即使无需光谱扫描也能够同时对两种干扰物种进行定量检测,从而实现双物种单波长检测(2S1W)能力。通过直接吸收和光腔衰荡装置进行了演示,该方法为进一步增强基于激光的分析仪以用于环境、医疗和技术应用中的复杂气体混合物分析提供了一个新的、以前未被利用的机会。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e2c3/10564745/d12cb86d29e3/41598_2023_44195_Fig1_HTML.jpg

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