Journal of Materials Science

, Volume 47, Issue 16, pp 5933–5945 | Cite as

Effects of activator type/concentration and curing temperature on alkali-activated binder based on copper mine tailings



This article investigates the effects of activator type/concentration and curing temperature on alkali-activated binder based on copper mine tailings (MT). Different alkaline activators including sodium hydroxide (NaOH), sodium silicate (SS), and sodium aluminate (SA) at different compositions and concentrations were used and four different curing temperatures, 60, 75, 90, and 120 °C, were considered. Scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM/EDX), and X-ray diffraction (XRD) were conducted to investigate the effect of these factors on the unconfined compressive strength (UCS), microstructure, and phase composition of the binder. The results indicate that NaOH concentration and curing temperature are two important factors that affect the UCS and micro-structural properties of the alkali-activated MT binder. The optimum curing temperature, i.e., the curing temperature at the maximum UCS, depends on the NaOH concentration, lower optimum curing temperature at smaller NaOH concentration. Addition of aqueous SS to the NaOH solution can lead to strength improvement, with the highest UCS obtained at a SiO2/Na2O ratio of 1.0–1.26. Addition of powder SA to the NaOH solution profoundly delays the setting at 60 °C but improves the UCS at 90 °C. The SEM/EDX results show highly heterogeneous microstructure for the alkali-activated MT binder as evidenced by the variable Si/Al ratios in different phases. The XRD patterns indicate a newly formed crystalline phase, zeolite, in the 90 °C-cured specimens. The results of this study provide useful information for recycling and utilization of copper MT as construction material through the geopolymerization technology.


Geopolymer Unconfined Compressive Strength Ordinary Portland Cement Mine Tailing Unconfined Compressive Strength 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.



This work is partially supported by the National Science Foundation under Grant No. CMMI-0969385, the University of Arizona Faculty Seed Grants Program, and a local mine company in Tucson, AZ.


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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  1. 1.Department of Civil Engineering and Engineering MechanicsUniversity of ArizonaTucsonUSA
  2. 2.Department of Mining and Geological EngineeringUniversity of ArizonaTucsonUSA

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