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:Fish morphological phenotypes are important resources in artificial breeding, functional gene mapping, and population-based studies in aquaculture and ecology. Traditional morphological measurement of phenotypes is rather expensive in terms of time and labor. More importantly, manual measurement is highly dependent on operational experience, which can lead to subjective phenotyping results. Here, we developed 3DPhenoFish software to extract fish morphological phenotypes from three-dimensional (3D) point cloud data. Algorithms for background elimination, coordinate normalization, image segmentation, key point recognition, and phenotype extraction were developed and integrated into an intuitive user interface. Furthermore, 18 key points and traditional 2D morphological traits, along with 3D phenotypes, including area and volume, can be automatically obtained in a visualized manner. Intuitive fine-tuning of key points and customized definitions of phenotypes are also allowed in the software. Using 3DPhenoFish, we performed high-throughput phenotyping for four endemic Schizothoracinae species, including Schizopygopsis younghusbandi, Oxygymnocypris stewartii, Ptychobarbus dipogon, and Schizothorax oconnori. Results indicated that the morphological phenotypes from 3DPhenoFish exhibited high linear correlation (>0.94) with manual measurements and offered informative traits to discriminate samples of different species and even for different populations of the same species. In summary, we developed an efficient, accurate, and customizable tool, 3DPhenoFish, to extract morphological phenotypes from point cloud data, which should help overcome traditional challenges in manual measurements. 3DPhenoFish can be used for research on morphological phenotypes in fish, including functional gene mapping, artificial selection, and conservation studies. 3DPhenoFish is an open-source software and can be downloaded for free at https://github.com/lyh24k/3DPhenoFish/tree/master.

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.

Similar Datasets

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