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Portfolio Optimization of Nanomaterial Use in Clean Energy Technologies

  • Elizabeth A. Moore
    Elizabeth A. Moore
    Golisano Institute for Sustainability Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, New York 14623, United States
  • Callie W. Babbitt*
    Callie W. Babbitt
    Golisano Institute for Sustainability Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, New York 14623, United States
    *E-mail: [email protected]. Phone: (585) 475-6277.
  • Gabrielle Gaustad
    Gabrielle Gaustad
    Golisano Institute for Sustainability Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, New York 14623, United States
  • , and 
  • Sean T. Moore
    Sean T. Moore
    Golisano Institute for Sustainability Rochester Institute of Technology, 190 Lomb Memorial Drive, Rochester, New York 14623, United States
Cite this: Environ. Sci. Technol. 2018, 52, 7, 4440–4448
Publication Date (Web):March 26, 2018
https://doi.org/10.1021/acs.est.7b04912
Copyright © 2018 American Chemical Society

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    Abstract

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    While engineered nanomaterials (ENMs) are increasingly incorporated in diverse applications, risks of ENM adoption remain difficult to predict and mitigate proactively. Current decision-making tools do not adequately account for ENM uncertainties including varying functional forms, unique environmental behavior, economic costs, unknown supply and demand, and upstream emissions. The complexity of the ENM system necessitates a novel approach: in this study, the adaptation of an investment portfolio optimization model is demonstrated for optimization of ENM use in renewable energy technologies. Where a traditional investment portfolio optimization model maximizes return on investment through optimal selection of stock, ENM portfolio optimization maximizes the performance of energy technology systems by optimizing selective use of ENMs. Cumulative impacts of multiple ENM material portfolios are evaluated in two case studies: organic photovoltaic cells (OPVs) for renewable energy and lithium-ion batteries (LIBs) for electric vehicles. Results indicate ENM adoption is dependent on overall performance and variance of the material, resource use, environmental impact, and economic trade-offs. From a sustainability perspective, improved clean energy applications can help extend product lifespans, reduce fossil energy consumption, and substitute ENMs for scarce incumbent materials.

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    The Supporting Information is available free of charge on the ACS Publications website at DOI: 10.1021/acs.est.7b04912.

    • Additional results and data, ENM production, cost, and performance data, and sensitivity analyses (PDF)

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    Cited By

    This article is cited by 14 publications.

    1. Hui Wang, Weili Wang, Qianqian Xie, Di Wu, Jiayu Cao, Huilin Chen, Meng Gao, Huizhen Zheng, Xi Liu, Jie Jiang, Wenjie Li, Xiaoming Cai, Sergey V. Gudkov, Ruibin Li. Using Chicken Embryos to Identify the Key Determinants of Nanoparticles for the Crossing of Air–Blood Barriers. Analytical Chemistry 2023, 95 (14) , 6009-6019. https://doi.org/10.1021/acs.analchem.3c00034
    2. P. Sreeramana Aithal, Shubhrajyotsna Aithal. Opportunities and Challenges for Green and Eco‐Friendly Nanotechnology in Twenty‐First Century. 2022, 31-50. https://doi.org/10.1002/9781119650294.ch3
    3. Erinn G. Ryen, Callie W. Babbitt, Komal Kooduvalli. Circular Economy Research: From Theory to Practice. 2022, 25-46. https://doi.org/10.1007/978-3-030-94293-9_2
    4. P. S. Aithal, Shubhrajyotsna Aithal. Opportunities and Challenges for Green and Eco‐Friendly Nanotechnology in Twenty‐First Century. SSRN Electronic Journal 2022, 28 https://doi.org/10.2139/ssrn.4161033
    5. Shahana Althaf, Callie W. Babbitt. Disruption risks to material supply chains in the electronics sector. Resources, Conservation and Recycling 2021, 167 , 105248. https://doi.org/10.1016/j.resconrec.2020.105248
    6. Callie W. Babbitt, Shahana Althaf, Fernanda Cruz Rios, Melissa M. Bilec, T.E. Graedel. The role of design in circular economy solutions for critical materials. One Earth 2021, 4 (3) , 353-362. https://doi.org/10.1016/j.oneear.2021.02.014
    7. Shubhrajyotsna Aithal, P. S. Aithal. Green and Eco-friendly Nanotechnology – Concepts and Industrial Prospects. International Journal of Management, Technology, and Social Sciences 2021, , 1-31. https://doi.org/10.47992/IJMTS.2581.6012.0127
    8. Mohammad Hossein Karimi, Emran Mohammadi. A Multinomial Goal Programming Model Using Markowitz Model Based on Energy Portfolio Under Uncertainty. 2021, 297-309. https://doi.org/10.1007/978-3-030-66501-2_24
    9. Elizabeth A. Moore, Callie W. Babbitt, Brian Tomaszewski, Anna Christina Tyler. Spatial perspectives enhance modeling of nanomaterial risks. Journal of Industrial Ecology 2020, 24 (4) , 855-870. https://doi.org/10.1111/jiec.12976
    10. Xiaojing Zhu. Energy optimization of the configurable service portfolio for IoT systems. Computer Communications 2020, 154 , 491-500. https://doi.org/10.1016/j.comcom.2020.03.008
    11. Andrew Livingston, Bernhardt L. Trout, Istvan T. Horvath, Martin D. Johnson, Luigi Vaccaro, Joaquin Coronas, Callie W. Babbitt, Xiangliang Zhang, Thalappil Pradeep, Enrico Drioli, John D. Hayler, Kam C. Tam, C. Oliver Kappe, Anthony G. Fane, Gyorgy Szekely. Challenges and Directions for Green Chemical Engineering—Role of Nanoscale Materials. 2020, 1-18. https://doi.org/10.1016/B978-0-12-814681-1.00001-1
    12. Sarah Ponte, Elizabeth A. Moore, Charles T. Border, Callie W. Babbitt, Anna Christina Tyler. Fullerene toxicity in the benthos with implications for freshwater ecosystem services. Science of The Total Environment 2019, 687 , 451-459. https://doi.org/10.1016/j.scitotenv.2019.05.362
    13. Therese Garvey, Elizabeth A. Moore, Callie W. Babbitt, Gabrielle Gaustad. Comparing ecotoxicity risks for nanomaterial production and release under uncertainty. Clean Technologies and Environmental Policy 2019, 21 (2) , 229-242. https://doi.org/10.1007/s10098-018-1648-6
    14. Rong Wang, Chuangang Yu, Jia Wang. Construction of Supply Chain Financial Risk Management Mode Based on Internet of Things. IEEE Access 2019, 7 , 110323-110332. https://doi.org/10.1109/ACCESS.2019.2932475

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