Title:
MEMBRANE-BASED TECHNOLOGIES FOR SALINITY GRADIENT POWER HARVESTING: ADVANCED MEMBRANES DEVELOPMENT

dc.contributor.advisor Chen, Yongsheng
dc.contributor.author Gao, Haiping
dc.contributor.committeeMember Crittenden, John
dc.contributor.committeeMember Huang, Ching-Hua
dc.contributor.committeeMember Xie, Xing
dc.contributor.committeeMember Xia, Shuman
dc.contributor.committeeMember Tong, Zhaohui
dc.contributor.department Civil and Environmental Engineering
dc.date.accessioned 2021-01-11T17:05:01Z
dc.date.available 2021-01-11T17:05:01Z
dc.date.created 2019-12
dc.date.issued 2019-09-11
dc.date.submitted December 2019
dc.date.updated 2021-01-11T17:05:01Z
dc.description.abstract Salinity gradients have emerged as a potential sustainable source for renewable energy. The free energy released from mixing two solutions of different salinities can be harvested by controlled mass transport through membrane - based technologies, reverse electrodialysis (RED) and pressure retarded osmosis (PRO). Salinity gradient power (SGP) has made remarkable progress in the past decade. However, it remains challenging to realize the practical application feasibility of the RED and PRO technologies. Since membranes are the heart of these technologies, it is therefore critical to develop membranes with highly anticipated properties for advancement of SGP as economically attractive sustainable energy. This study primarily focused on developing high - performance membranes for efficiently capturing SGP in RED and PRO systems. Additionally, a better understanding on the correlation of leading membrane properties, including permselectivity and ionic conductivity, with the underlying characteristics of the membrane was investigated through statistical simulation and experimental validation. The modeling and simulation results can potentially offer fine - tuning approaches for ion exchange membranes (IEMs) - based electrochemical systems like RED but not limited to RED system. With respect to the RED system, monovalent - ion selective anion exchange membranes (AEMs) were fabricated by layer-by-layer (LBL) modification. The optimized membranes exhibited simultaneously enhanced monovalent-ion selectivity and anti-fouling potential due to the formation of a negatively charged surface layer. Consequently, higher energy efficiency and power density were achieved with monovalent-ion selective membrane than that achieved with standard commercial AEM. In terms of the PRO system, the freestanding hybrid thin membranes were developed using two - dimensional (2D) materials, MXene and graphene oxide (GO), as building blocks. The “limiting factor” internal concentration polarization (ICP) was remarkably mitigated in the support free MXene/GO hybrid membranes. Thus, the effective osmotic driving force for water permeation was largely increased, which contributed to a larger water flux and anticipating PRO power output. The finds in this work highlight a new platform on the fabrication of advanced 2D materials assembled hybrid membranes and its promise in osmotic energy harvesting.
dc.description.degree Ph.D.
dc.format.mimetype application/pdf
dc.identifier.uri http://hdl.handle.net/1853/64020
dc.language.iso en_US
dc.publisher Georgia Institute of Technology
dc.subject Salinity gradients, Reverse electrodialysis (RED), Pressure retarded osmosis (PRO), Salinity gradient power (SGP), Ion exchange membranes (IEMs), Internal concentration polarization (ICP), two-dimensional (2D) materials
dc.title MEMBRANE-BASED TECHNOLOGIES FOR SALINITY GRADIENT POWER HARVESTING: ADVANCED MEMBRANES DEVELOPMENT
dc.type Text
dc.type.genre Dissertation
dspace.entity.type Publication
local.contributor.advisor Chen, Yongsheng
local.contributor.corporatename School of Civil and Environmental Engineering
local.contributor.corporatename College of Engineering
relation.isAdvisorOfPublication 07477536-9f7c-4580-988f-d21ea0e72e97
relation.isOrgUnitOfPublication 88639fad-d3ae-4867-9e7a-7c9e6d2ecc7c
relation.isOrgUnitOfPublication 7c022d60-21d5-497c-b552-95e489a06569
thesis.degree.level Doctoral
Files
Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
GAO-DISSERTATION-2019.pdf
Size:
4.41 MB
Format:
Adobe Portable Document Format
Description:
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
LICENSE.txt
Size:
3.86 KB
Format:
Plain Text
Description: