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一种通过追踪布朗运动来估计海水中亚微米颗粒尺寸分布的方法。

A method for tracking the Brownian motion to estimate the size distribution of submicron particles in seawater.

作者信息

Xiong Yuanheng, Zhang Xiaodong, Hu Lianbo

机构信息

Department of Earth System Science and Policy University of North Dakota Grand Forks North Dakota USA.

Division of Marine Science School of Ocean Science and Engineering, The University of Southern Mississippi, Stennis Space Center Mississippi USA.

出版信息

Limnol Oceanogr Methods. 2022 Jul;20(7):373-386. doi: 10.1002/lom3.10494. Epub 2022 May 24.

Abstract

Because the diffusivity of particles undergoing the Brownian motion is inversely proportional to their sizes, the size distribution of submicron particles can be estimated by tracking their movement. This particle tracking analysis (PTA) has been applied in various fields, but mainly focused on resolving monodispersed particle populations and is rarely used for measuring oceanic particles that are naturally polydispersed. We demonstrated using Monte Carlo simulation that, in principle, PTA can be used to size natural, oceanic particles. We conducted a series of lab experiments using microbeads of NIST-traceable sizes to evaluate the performance of ViewSizer 3000, a PTA-based commercial instrument, and found two major uncertainties: (1) the sample volume varies with the size of particles and (2) the signal-to-noise ratio for particles of sizes < 200-250 nm was reduced and hence their concentration was underestimated with the presence of larger particles. After applying the volume correction, we found the instrument can resolve oceanic submicron particles of sizes greater than 250 nm with a mean absolute error of 3.9% in size and 38% in concentration.

摘要

由于做布朗运动的粒子的扩散率与其大小成反比,因此可以通过追踪亚微米粒子的运动来估算其尺寸分布。这种粒子追踪分析(PTA)已应用于各个领域,但主要集中于解析单分散粒子群体,很少用于测量天然多分散的海洋粒子。我们通过蒙特卡洛模拟证明,原则上PTA可用于确定天然海洋粒子的大小。我们使用具有NIST可溯源尺寸的微珠进行了一系列实验室实验,以评估基于PTA的商业仪器ViewSizer 3000的性能,发现了两个主要的不确定因素:(1)样品体积随粒子大小而变化;(2)尺寸<200-250 nm的粒子的信噪比降低,因此在存在较大粒子的情况下其浓度被低估。应用体积校正后,我们发现该仪器可以分辨尺寸大于250 nm的海洋亚微米粒子,尺寸的平均绝对误差为3.9%,浓度的平均绝对误差为38%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/070e/9543390/09c3ca11e844/LOM3-20-373-g013.jpg

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