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Next Generation Electrical Energy Storage

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REPORT OF THE BASIC RESEARCH NEEDS WORKSHOP fundamentally reinvent engineered membranes beyond simple diffusion or Newtonian fluid flow limitations. Next generation membranes may emerge from the strategic and precise placement of active chemical functionality onto robust material systems, intelligent pore design with signal chemistry, and mechanisms for fast fluid flow and mass transport. The growing research interest in this area in recent years has produced redox active materials other than traditional transition metal ions, such as organic and organometallic redox couples, polymers, and metal ionic liquid complexes.32,33,41,42 Meanwhile, various charge carrier ions other than protons have been enlisted in many storage systems with new redox chemistries. With the continuous invention of new redox chemistries, there is currently an urgent need to develop new membranes tailored to specific redox chemistries, a variety of charge carrier ions, and various fouling characteristics. Past membrane research has often focused on the compromise between membrane selectivity and conductivity.43 New membrane materials with high selectivity and fast transport mechanism should be developed and investigated. 2.4.3 REFERENCES 1. McDowell, M.T.; Xia; S.; Zhu, T., The mechanics of large-volume-change transformations in high-capacity battery materials, Extreme Mech. Lett., 2016, 9, 480-494. 2. McDowell, M.T.; Lee, S.W.; Cui, Y., 25th Anniversary article: Understanding the lithiation of silicon and other alloying anodes for lithium-ion batteries, Adv. Mater., 2013, 25 (36), 4966-4985. 3. Grzelczak, M.; Vermant, J.; Furst, E.M.; Liz-Marzán, L.M., Directed self-assembly of nanoparticles, ACS Nano, 2010, 4 (7), 3591-3605. 4. Xu, R.; Scalco de Vasconcelos, L., Zhao, K., Computational analysis of chemomechanical behaviors of composite electrodes in Li-ion batteries, J. Mater. Res., 2016, 31 (18), 2715-2727. 5. 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Raccuglia, P.; Elbert, K.C.; Adler, P.D.F.; Falk, C.; Wenny, M.B.; Mollo, A.; Zeller, M.; Friedler, S.A.; Schrier, J.; Norquist, A.J., Machine-learning- assisted materials discovery using failed experiments, Nature, 2016, 533, 73-77, DOI:10.1038/nature17439. 15. Le, T.C.; Winkler, D.A., Discovery and optimization of materials using evolutionary approaches, Chem. Rev., 2016, 116, 6107−6132, DOI: 10.1021/acs.chemrev.5b00691. 16. Wicker, G.P.; Cooper, R.I., Will it crystallise? Predicting crystallinity of molecular materials, CrystEngComm, 2015, 17, 1927-1934, DOI: 10.1039/c4ce01912a. 17. Goldsmith, B.R.; Bryan, R.G.; Boley, M.; Vreeken, J.; Scheffler, M.; Ghiringhelli, L.M., Uncovering structure-property relationships of materials by subgroup discovery, New J. Phys., 2017, 19 ( 1), 013031; DOI: 10.1088/1367-2630/aa57c2. 18. U.S. Department of Energy, BESAC Subcommittee on Mesoscale Science, From Quanta to the Continuum: Opportunies for Mesoscale Science, http://science.energy.gov/~/media/bes/pdf/reports/files/OFMS_rpt.pdf, September 2012. 19. Collins, S.P.; Daff, T.D.; Piotrkowski, S.S.; Woo, T.K., Materials design by evolutionary optimization of functional groups in metal-organic frameworks, Sci. Adv., 2016, 2, e1600954, DOI: 10.1126/sciadv.1600954. 20. Pulido, A.; Chen, L.; Kaczorowski, T.; Holden, D.; MaLittle, M.A.; Chong, S.Y; Slater, B.J.; McMahon, D.P.; Bonillo, B.; Stackhouse, C.J., Functional materials discovery using energy–structure–function maps, Nature, 2017, 543, 657-666; DOI: 10.1038/nature21419. 21. Nystrom, G.; Marais, A.; Karabulut, E.; Wågberg, L.; Cui, Y.; Hamedi, M.M., Self-assembled three-dimensional and compressible interdigitated thin-film supercapacitors and batteries, Nature Commun., 2015, 6, 8. 22. 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DuBois, D.L., Development of molecular electrocatalysts for energy storage, Inorg. Chem., 2014, 53 (8), 3935-3960. 62 PRIORITY RESEARCH DIRECTION – 4

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