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Balancing research and funding using value of information and portfolio tools for nanomaterial risk classification

A Corrigendum to this article was published on 05 April 2016

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Abstract

Risk research for nanomaterials is currently prioritized by means of expert workshops and other deliberative processes. However, analytical techniques that quantify and compare alternative research investments are increasingly recommended. Here, we apply value of information and portfolio decision analysis—methods commonly applied in financial and operations management—to prioritize risk research for multiwalled carbon nanotubes and nanoparticulate silver and titanium dioxide. We modify the widely accepted CB Nanotool hazard evaluation framework, which combines nano- and bulk-material properties into a hazard score, to operate probabilistically with uncertain inputs. Literature is reviewed to develop uncertain estimates for each input parameter, and a Monte Carlo simulation is applied to assess how different research strategies can improve hazard classification. The relative cost of each research experiment is elicited from experts, which enables identification of efficient research portfolios—combinations of experiments that lead to the greatest improvement in hazard classification at the lowest cost. Nanoparticle shape, diameter, solubility and surface reactivity were most frequently identified within efficient portfolios in our results.

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Figure 1: Data and results for VoI analysis, showing cost inputs to and benefit–cost outputs from the VoI portfolio model.
Figure 2: Frequency of the inclusion of each individual research effort in aggregated portfolios on the efficient frontier.

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Change history

  • 04 February 2016

    In the version of this Article originally published, the data in Figs 1b–e and 2 were incorrect because of a coding error in the Monte Carlo analysis; the figures have now been replaced. Supplementary Figs 1–3 and Tables 1–7 also relied on these calculations and have been replaced. Furthermore, in the caption to Fig. 1e inset and in the related sentence in the main text, the values discussed were incorrect and have now been amended. None of these changes affect our conclusions. These errors have been corrected in the online versions of the Article.

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Acknowledgements

This work was funded by the nanotechnology and emerging material risk research programmes of the US Army Engineer Research and Development Center, by the Environmental Protection Agency ‘Science to Achieve Results’ programme through grant no. FP917643, and the National Science Foundation through grants nos. ECCS-1140190 and SES-0937591. Publication of this material has been approved by the authority of the Chief of the US Army Corps of Engineers.

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Contributions

J.M.K., M.E.B. and I.L. conceived the approach. N.P.Z., B.A.W., M.E.B., J.M.K. and I.L. wrote the manuscript text and responded to reviewer comments. N.P.Z. performed the CB Nanotool literature review and material scoring. B.A.W., N.P.Z. and M.E.B. performed the cost-estimating interviews. M.E.B. and K.J.P. performed the VoI analysis. K.J.P. and M.E.B. generated the figures. N.P.Z., B.A.W., M.E.B. and K.J.P. compiled the Supplementary Information.

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Correspondence to Matthew E. Bates or Igor Linkov.

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The authors declare no competing financial interests.

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Bates, M., Keisler, J., Zussblatt, N. et al. Balancing research and funding using value of information and portfolio tools for nanomaterial risk classification. Nature Nanotech 11, 198–203 (2016). https://doi.org/10.1038/nnano.2015.249

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