Nanoparticle Surface Affinity as a Predictor of Trophic Transfer

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• 作者：Nicholas K. Geitner ; Stella M. Marinakos ; Charles Guo ; Niall O’Brien ; Mark R. Wiesner
• 刊名：Environmental Science & Technology
• 出版年：2016
• 出版时间：July 5, 2016
• 年：2016
• 卷：50
• 期：13
• 页码：6663-6669
• 全文大小：308K
• 年卷期：0
• ISSN：1520-5851

文摘

Nanoscale materials, whether natural, engineered, or incidental, are increasingly acknowledged as important components in large, environmental systems with potential implications for environmental impact and human health. Mathematical models are a useful tool for handling the rapidly increasing complexity and diversity of these materials and their exposure routes. Presented here is a mathematical model of trophic transfer driven by nanomaterial surface affinity for environmental and biological surfaces, developed in tandem with an experimental functional assay for determining these surface affinities. We found that nanoparticle surface affinity is a strong predictor of uptake through predation in a simple food web consisting of the algae Chlorella vulgaris and daphnid Daphnia magna. The mass of nanoparticles internalized by D. magna through consuming nanomaterial-contaminated algae varied linearly with surface-attachment efficiency. Internalized quantities of gold nanoparticles in D. magna ranged from 8.3 to 23.6 ng/mg for nanoparticle preparations with surface-attachment efficiencies ranging from 0.07 to 1. This model, coupled with the functional-assay approach, may provide a useful screening tool for existing materials as well as a predictive model for their development.