Dataset Information

Investigation of cloud point extraction for the analysis of metallic nanoparticles in a soil matrix.

ABSTRACT: The characterization of manufactured nanoparticles (MNPs) in environmental samples is necessary to assess their behavior, fate and potential toxicity. Several techniques are available, but the limit of detection (LOD) is often too high for environmentally relevant concentrations. Therefore, pre-concentration of MNPs is an important component in the sample preparation step, in order to apply analytical tools with a LOD higher than the ng kg-1 level. The objective of this study was to explore cloud point extraction (CPE) as a viable method to pre-concentrate gold nanoparticles (AuNPs), as a model MNP, spiked into a soil extract matrix. To that end, different extraction conditions and surface coatings were evaluated in a simple matrix. The CPE method was then applied to soil extract samples spiked with AuNPs. Total gold, determined by inductively coupled plasma mass spectrometry (ICP-MS) following acid digestion, yielded a recovery greater than 90 %. The first known application of single particle ICP-MS and asymmetric flow field-flow fractionation to evaluate the preservation of the AuNP physical state following CPE extraction is demonstrated.

SUBMITTER: Hadri HE

PROVIDER: S-EPMC5427641 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Investigation of cloud point extraction for the analysis of metallic nanoparticles in a soil matrix.

Hadri Hind El HE Hackley Vincent A VA

Environmental science. Nano 20161019 1

The characterization of manufactured nanoparticles (MNPs) in environmental samples is necessary to assess their behavior, fate and potential toxicity. Several techniques are available, but the limit of detection (LOD) is often too high for environmentally relevant concentrations. Therefore, pre-concentration of MNPs is an important component in the sample preparation step, in order to apply analytical tools with a LOD higher than the ng kg<sup>-1</sup> level. The objective of this study was to e ...[more]

PMID: 28507763

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Project description:ReasonPedestrian recognition has great practical value and is a vital step toward applying path planning and intelligent obstacle avoidance in autonomous driving. In recent years, laser radar has played an essential role in pedestrian detection and recognition in unmanned driving. More accurate high spatial dimension and high-resolution data could be obtained by building a three-dimensional point cloud. However, the point cloud data collected by laser radar is often massive and contains a lot of redundancy, which is not conducive to transmission and storage. So, the processing speed grows slow when the original point cloud data is used for recognition. On the other hand, the compression processing of many laser radar point clouds could save computing power and speed up the recognition processing.MethodologyThe article utilizes the fusion point cloud data from laser radar to investigate the fast pedestrian recognition algorithm. The focus is to compress the collected point cloud data based on the boundary and feature value extraction and then use the point cloud pedestrian recognition algorithm based on image mapping to detect pedestrians. This article proposes a point cloud data compression method based on feature point extraction and reduced voxel grid.ResultsThe Karlsruhe Institute of Technology and Toyota Technological Institute data set is used to investigate the proposed algorithm experimentally. The outcomes indicate that the peak signal-to-noise ratio of the compression algorithm is improved by 6.02%. The recognition accuracy is improved by 16.93%, 17.2%, and 16.12%, corresponding to simple, medium, and difficult scenes, respectively, when compared with the point cloud pedestrian recognition method based on image mapping, which uses the random sampling method to compress the point cloud data.ConclusionThe proposed method could achieve data compression better and ensure that many feature points are retained in the compressed Point Cloud Data (PCD). Thus, the compressed PCD achieves pedestrian recognition through an image-based mapping recognition algorithm.

Dataset Information

Investigation of cloud point extraction for the analysis of metallic nanoparticles in a soil matrix.

Publications

Investigation of cloud point extraction for the analysis of metallic nanoparticles in a soil matrix.

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