Developing and assessing geopolymers from seochun pond ash with a range of compositional ratios SCOPUS KCI

Title
Developing and assessing geopolymers from seochun pond ash with a range of compositional ratios
Author(s)
Lee, S.; Jou, H.-T.; Chon, C.-M.; Kang, N.-H.; Cho, S.-B.
KIOST Author(s)
Jou, Hyeong Tae(주형태)
Publication Year
2013
Abstract
Pond ash produced from Seochun Power Station was quantitatively characterized to manufacture geopolymers with a range of Si/ Al compositional ratios. Mix consistency was kept nearly constant for comparing the compressive strengths of geopolymers. The amorphous composition of coal ash was determined using XRF and quantitative X-ray diffraction. Different mix compositions were used in order to achieve Si/Al ratios of 2.0, 2.5 and 3.0 in the geopolymer binder. Geopolymers synthesized from coal ash with a Si/ Al ratio of 3.0 exhibited the highest compressive strength in this study. It was found that geopolymers activated with aluminate produced different microstructure from that of geopolymers activated with silicate. High silica in alkali activators produced the finegrained microstructure of geopolymer gel. It was also found that high compressive strength was related to low porosity and a dense, connected microstructure. The outcome of the reported experiment indicates that quantitative formulation method made it possible to choose suitable activators for achieving targeted compositions of geopolymers and to avoid efflorescence.
ISSN
1229-7801
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/3297
DOI
10.4191/kcers.2013.50.2.134
Bibliographic Citation
Journal of the Korean Ceramic Society, v.50, no.2, pp.134 - 141, 2013
Subject
Alkali activators; Compositional ratio; Fine-grained microstructure; Geopolymer; Geopolymer binders; Pond ash; Quantitative formulations; Quantitative x ray diffraction; Coal ash; Compressive strength; Drying; Inorganic polymers; Lakes; Microstructure; Silica; Silicates; Surface chemistry; X ray diffraction; Geopolymers
Keywords
Compositional ratios; Compressive strength; Geopolymer; Pond ash
Type
Article
Language
Korean
Document Type
Article
Related Researcher
Research Interests

Machine Learning,Marine Active Fault Study,Exploration Seismology,기계학습,해저단층연구,탄성파탐사

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