Abstract
Renewable energy is increasingly relying on optimized electrolytes and interfaces. In this work, Tween 20 and sodium chloride are selected as a model system to reveal the effects of surfactants on salt crystallization in the context of ionic conductivity and interface optimization. At a varied crystallization speed and mix ratio, it is demonstrated that the resultant solution-grown ionic crystalline complexes can achieve a highly tunable ion transport with a controllable crystalline interface. X-ray diffraction results rule out the possibility of polymorphism in the NaCl/Tween 20 systems, which further supports the importance of an optimized crystalline network for optimizing permittivity or ionic conductivity. Raman mapping and machine learning techniques are used to perform semantic segmentation on highly heterogeneous NaCl/Tween 20 complexes. Furthermore, FTIR measurements demonstrate that inter- and intra-molecular interactions play critical roles in the formation of these crystals. This work lays a foundation toward future optimization of such complex ion systems for a specific salt or crystallization modifier in energy storage or ion transport applications.
Original language | English |
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Article number | 1293801 |
Journal | Frontiers in Nanotechnology |
Volume | 5 |
DOIs | |
State | Published - 2023 |
Bibliographical note
Publisher Copyright:Copyright © 2023 Chen, Keum, Wang, Wang, Lokitz, Yang, Yuan, Kumar and Advincula.
Funding
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Center for Nanophase Materials Sciences (CNMS) at Oak Ridge National Laboratory, a US Department of Energy (DOE) Office of Science User Facility.
Funders | Funder number |
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Center for Nanophase Materials Sciences | |
U.S. Department of Energy | |
Office of Science | |
Oak Ridge National Laboratory |
Keywords
- conductivity
- crystallization
- ion
- machine learning and AI
- permittivity
- salt
- surfactant