用激光诱导击穿光谱(LIBS)鉴别方解石是否有生物标志物,新墨西哥州瓜达卢佩山脉

B. Jackson, N. McMillan
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摘要

在洞穴环境中,洞穴石可能是由方解石的无机沉淀或微生物作用的沉淀形成的。激光诱导击穿光谱(LIBS)是一种分析技术,可用于区分这两种类型的洞穴。LIBS是一种原子发射光谱,它使用高功率激光在样品表面产生烧蚀等离子体。然后可以分析烧蚀等离子体的辐射,以确定样品中存在的元素的类型和丰度。与传统的分析技术相比,LIBS具有几个优点,因为它提供了一种快速、便携和现场分析样品的方法。这项研究使用LIBS分析了从新墨西哥州东南部瓜达卢佩山脉的两个洞穴中收集的总共40个样本。在美国林务局的许可下,从棉木洞采集了20个样本,从黑洞采集了20个样本。样本包括怀疑是生物和非生物来源的洞穴,包括钟乳石、苏打吸管、方解石砾石、洞穴爆米花、池指和u形环。样品被运送到新墨西哥州立大学,并使用日立T-1000扫描电子显微镜(SEM)检查微生物结构,如棒状、细丝状、球菌状和生物膜。每个样本使用LIBS进行100次注射。利用LIBS采集的光谱数据构建并训练了多元偏最小回归二乘(PLSR)模型,并利用SEM数据对模型进行了验证。利用这些技术,我们能够成功地区分微生物纹理与无机纹理的样品,其中棉洞的成功率为78%,黑洞的成功率为90%。
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Distinguishing Calcite With and Without Biomarkers Using Laser-Induced Breakdown Spectroscopy (LIBS), Guadalupe Mountains, New Mexico
In a cave environment, speleothems may form by inorganic precipitation of calcite or by precipitation through microbial action. Laser-induced breakdown spectroscopy (LIBS) is an analytical technique that may be used to differentiate between these two types of speleothems. LIBS is a type of atomic emission spectroscopy that uses a high-powered laser to generate an ablation plasma on a sample surface. The emissions from the ablation plasma can then be analyzed to determine both the type and abundance of elements present in that sample. LIBS has several advantages over traditional analytical techniques in that it offers a rapid, portable, and in-situ method of analyzing samples in the field. This study uses LIBS to analyze a total of 40 samples collected from two caves in the Guadalupe Mountains of southeastern New Mexico. Under permit from the U.S. Forest Service 20 samples were collected from Cottonwood Cave and another 20 samples from Black Cave. Samples were selected to include speleothems suspected of being both biologic and non-biologic in origin and include stalactites, soda straws, calcite gravels, cave popcorn, pool fingers, and u-loops. Samples were transported to New Mexico State University and examined using a Hitachi T-1000 scanning electron microscope (SEM) to look for microbial textures such as rods, filaments, cocci, and biofilms. One-hundred shots per sample were taken using LIBS. The spectra collected using LIBS were used to construct and train a model using the multivariate technique of partial least regressive squares (PLSR) and the SEM data were used to validate this model. Using these techniques, we were able to successfully discriminate samples with microbial textures vs. inorganic textures with a 78% success rate for Cottonwood Cave and a 90% success rate for Black Cave.
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