Interactive effects between components in binary mixtures of zinc sulfate and iron oxide nanoparticles on Daphnia magna

Park, Chang-Beom; Jung, Jae-Woong; Yeom, Dong‐Hyuk; Jang, Jae Yool; Park, Jin-Woo; Kim, Young Jun

doi:10.1007/s13273-019-0035-7

Cited by 3 publications

(2 citation statements)

References 25 publications

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“…As verified in Figure S7, more than 50% ions were adsorbed on SLMoS 2 in BG-11. Many ENMs (e.g., Zn and Ag NPs) and their ionic counterpart showed the similar antagonism phenomenon. , However, the toxicological mechanisms are very complicated, involving the free radicals and the cell structure interacting with nanosheets (Figures and ). The metabolomics was used to further understand the impact of material nanoholes on algal toxicity, as provided in the next section.…”

Section: Resultsmentioning

confidence: 99%

Nanoholes Regulate the Phytotoxicity of Single-Layer Molybdenum Disulfide

Tong

Feng

Hou

et al. 2019

Environ. Sci. Technol.

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Single-layer molybdenum disulfide (SLMoS2) are applied as a hot 2D nanosheet in various fields involving water treatments. Both intentional design and environmental or biological processes induce many nanoholes in SLMoS2. However, the effects of nanoholes on the environmental stability and ecotoxicity of SLMoS2 remain largely unknown. The present work discovered that visible-light irradiation induced nanoholes (diameters, approximately 20 nm) in the plane of SLMoS2, with irregular edges and increased interplanar crystal spacing. The ratios of Mo to S in pristine and transformed SLMoS2 were 0.53 and 0.33, respectively. After 96 h exposure at concentrations from 0.1 to 1 mg/L, the above nanoholes promoted algal division, induced a stress-response hormesis, decreased the generation of •OH, and mitigated the cell shrinkage and wall rupture of Chlorella vulgaris induced by SLMoS2. In terms of stress response, the nanohole-bearing SLMoS2 induced fewer vacuoles and polyphosphate bodies of Chlorella vulgaris than the pristine form. Metabolomic analysis revealed that nanoholes perturbed the metabolisms of energy, carbohydrates, and fatty acids. This work proposes that nanoholes cause obvious effects on the environmental fate and ecotoxicity of SLMoS2 and that the environmental risks of engineered nanomaterials should be reevaluated using nanohole-bearing rather than pristine forms for testing.

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Section: Resultsmentioning

confidence: 99%

Nanoholes Regulate the Phytotoxicity of Single-Layer Molybdenum Disulfide

Tong

Feng

Hou

et al. 2019

Environ. Sci. Technol.

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“…There are sixteen studies investigating the bioaccumulation of D. magna ( Figure 3 A) of six nanomaterials (TiO 2 , CeO 2 , Cu, MWCNT, C 60 , and SWCNTs), six inorganic compounds (Cu(NO 3 ) 2 , CuCl 2 , CdCl 2 , ZnCl 2 , AgNO 3 , and Na 2 HAsO 4 ), and five organic compounds (atrazine, methylparathione, pentachlorophenol, phenanthrene, and tributyltin). Previous studies suggested a mechanism by which higher bioaccumulation induced by the absorption of metal ions/dissolved organic matter and a low level of agglomeration might cause higher immobilization/mortality of D. magna [ 16 , 59 , 81 , 117 , 153 , 184 ]. For example, Martin-de-Lucia and colleagues [ 16 ] found that at low concentrations, the binary mixtures of graphite–diamond nanoparticles and fungicide thiabendazole expressed synergistic toxic interactions, which could be attributed to the increased bioavailability of fungicide thiabendazole adsorbed on graphite–diamond nanoparticles.…”

Section: Current Available Data Of Nano-mixture Toxicitymentioning

confidence: 99%

Status Quo in Data Availability and Predictive Models of Nano-Mixture Toxicity

Trinh

Kim

2021

Nanomaterials

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Co-exposure of nanomaterials and chemicals can cause mixture toxicity effects to living organisms. Predictive models might help to reduce the intensive laboratory experiments required for determining the toxicity of the mixtures. Previously, concentration addition (CA), independent action (IA), and quantitative structure–activity relationship (QSAR)-based models were successfully applied to mixtures of organic chemicals. However, there were few studies concerning predictive models for toxicity of nano-mixtures before June 2020. Previous reviews provided comprehensive knowledge of computational models and mechanisms for chemical mixture toxicity. There is a gap in the reviewing of datasets and predictive models, which might cause obstacles in the toxicity assessment of nano-mixtures by using in silico approach. In this review, we collected 183 studies of nano-mixture toxicity and curated data to investigate the current data and model availability and gap and to derive research challenges to facilitate further experimental studies for data gap filling and the development of predictive models.

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