Abstract

Knowledge of alkaline silicate solutions is crucial in order to optimize geopolymer properties. Geopolymers are new binders resulting from the activation of an aluminosilicate by an alkaline solution. It is well established that the solution reactivity strongly affects the geopolymerization and therefore the geopolymer working properties. As a consequence, an evaluation of the reactivity degree of alkaline silicate solutions prior synthesis is of the utmost interest. However, the determination of the solution reactivity is currently tedious, and for geopolymer commercialization, it would be necessary to find an easy way to determine it. Therefore, Raman spectroscopy, combined with chemometric techniques, is proposed as a solution to easily determine the alkaline silicate solution reactivity. To conduct this investigation, 65 silicate solutions were characterized by Raman spectroscopy, and reference values of their reactivity degree were determined. Finally, principal component analysis and partial least squares regression were performed to build a statistical model able to predict the alkaline silicate solution reactivity from Raman spectra.

© 2019 The Author(s)

An Alkaline Solution of Potassium Chromate as a Transmittancy Standard in the Ultraviolet*

Geraldine Walker Haupt
J. Opt. Soc. Am. 42(7) 441-447 (1952)

Investigation of Cr:Tm:Er:YAG laser crystals in a resonator with various degrees of spectral selectivity

Igor V. Mochalov, Gurii T. Petrovskii, Alexander V. Sandulenko, Vitalii A. Sandulenko, Miguel Cervantes, and Vladimir S. Terpugov
Appl. Opt. 36(18) 4090-4093 (1997)

The Effect of Alkali and Alkaline Earth Salts on the Spectrographic Determination of Strontium*

E. Fast and J. Rud Nielsen
J. Opt. Soc. Am. 37(8) 614-622 (1947)

Quantitative analysis of formaldehyde solution based on terahertz attenuated total reflection and chemometric methods

Xuemin Li, Haiqing Huang, Wei Zhou, and Dehua Li
J. Opt. Soc. Am. B 39(1) 292-298 (2022)

Accurate quantification of alkalinity of sintered ore by random forest model based on PCA and variable importance (PCA-VI-RF)

Xinxin Deng, Guang Yang, Hong Zhang, and Guanyu Chen
Appl. Opt. 59(7) 2042-2049 (2020)