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Short Communication

Predicting toxic potencies of metal oxide nanoparticles by means of nano-QSARs

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Pages 1207-1214 | Received 09 Dec 2015, Accepted 03 May 2016, Published online: 11 Jul 2016

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Felice C. Simeone, Magda Blosi, Simona Ortelli & Anna L. Costa. (2019) Assessing occupational risk in designs of production processes of nano-materials. NanoImpact 14, pages 100149.
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Crossref
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Crossref
Jing Liu, Birendra Dhungana & George P. Cobb. (2017) Environmental behavior, potential phytotoxicity, and accumulation of copper oxide nanoparticles and arsenic in rice plants. Environmental Toxicology and Chemistry 37:1, pages 11-20.
Crossref
Enrico Burello. (2017) Review of (Q)SAR models for regulatory assessment of nanomaterials risks. NanoImpact 8, pages 48-58.
Crossref
Guangchao Chen, Martina Vijver, Yinlong Xiao & Willie Peijnenburg. (2017) A Review of Recent Advances towards the Development of (Quantitative) Structure-Activity Relationships for Metallic Nanomaterials. Materials 10:9, pages 1013.
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Crossref
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