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Biotoxicity of nanoparticles: effect of natural organic matter

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Abstract
Various natural organic matters (NOM) with different characteristics in aquatic environment may affect toxicity of leased nanoparticles, owing to interactions between NOM and nanoparticles. This study investigated the effect of NOM and physical characteristics of the effluent organic matter (EfOM) on the ecotoxicity of quantum dots (QD) using Daphnia magna. Organic matter samples were obtained from: Yeongsan River (YR-NOM), Dongbuk Lake (DL-NOM), Damyang wastewater treatment plant (EfOM), and Suwannee River NOM (SR-NOM). The QD was composed of a CdSe core, ZnS shell, and polyethylene glycol coating. The average size of the investigated QD was 4.8, 56.5, and 25.0 nm determined by transmission electron microscopy, dynamic light scattering, and asymmetric flow field-flow fractionation, respectively. The relative hydrophobicity of NOM was investigated using both specific UV absorbance at 254 nm and XAD-8/4 resins. The sorption of NOM on the QD was measured using a fluorescence quenching method. The highest hydrophobicity was exhibited by the SR-NOM, while the lowest was recorded for the DL-NOM. All tested NOMs significantly reduced the acute toxicity of D. magna when adsorbed to QD, and the order of effectiveness for each NOM was as follows: SR-NOM > EfOM > YS-NOM > DL-NOM. The sorption of NOM on the QD surface caused a decrease in the fluorescence intensity of QD at increasing NOM concentration. This suggests that the NOM coating influenced the physicochemical characteristics of QD in the internal organs of D. magna by inducing a reduced bioavailability. Results from this study revealed that NOM with relatively high hydrophobicity had a greater capability of inducing toxicity mitigation.
Author(s)
Lee, SungyunKim, KitaeShon, H. K.Kim, Sang DonCho, Jae Weon
Issued Date
2011-07
Type
Article
DOI
10.1007/s11051-010-0204-z
URI
https://scholar.gist.ac.kr/handle/local/16258
Publisher
Kluwer Academic Publishers
Citation
Journal of Nanoparticle Research, v.13, no.7, pp.3051 - 3061
ISSN
1388-0764
Appears in Collections:
Department of Environment and Energy Engineering > 1. Journal Articles
Graduate School of AI Policy and Strategy > 1. Journal Articles
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