pH-depended behaviors of electrolytes in nanofluidic salinity gradient energy harvesting

Xi Chen, Lu Wang, Ruhong Zhou, Rui Long, Zhichun Liu and Wei Liu

Renewable Energy, 2023, vol. 211, issue C, 31-41

Abstract: Transmembrane ion transportation in the nanofluidic salinity gradient energy conversion process is significantly regulated by the ion characteristics and concentration-depended physical and chemical properties of the electrolyte solution. In this paper, considering the Born and dielectrophoretic forces and nonhomogeneous electrolyte solution, impacts of various electrolytes on the nanofluidic energy conversion performance are systematically investigated under various solution pHs. When the solution pH is less than the isoelectric point (IEP), with BeCl2 solution employed, where the anion diffusion and concentration coefficient are much larger than those of the anion, significant transmembrane anion diffusion exists, leading to the highest osmotic current and maximum power output, even when the solution pH > IEP where the nanochannel is negatively charged, the ion selectivity is still not altered. At pH < IEP, 2:1 electrolytes, where the cation has small ion diffusion coefficient and the anion has larger diffusion coefficient and hydrated radius could result in upgraded energy conversion performance; At pH > IEP, 1:1 electrolytes where the cation has large ion diffusion coefficient and the anion has small diffusion coefficient and large hydrated radius are more appealing. In addition, the relationships between ion characteristics, power extracted, and energy conversion efficiency are further obtained via machine learning.

Keywords: Nanofluidic reverse electrodialysis; pH; Electrolytes; Machine learning (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:211:y:2023:i:c:p:31-41

DOI: 10.1016/j.renene.2023.04.056

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