The current understanding of the carbonation and the prediction of the carbonation rate of alkali-activated concretes is complicated
Efforts to reduce the carbon footprint associated with cement and concrete production have resulted in a number of promising lower‐emission alternatives. Still, research has emphasized a small subset of potentially useful precursor materials. With the goal of expanding the precursor pool, this work presents results of parallel literature mining and rate modeling activities. As a result of literature mining, materials with appropriate SiO2, Al2O3, and CaO concentrations were assembled into a comprehensive, representative ternary diagram. 23 000+ materials were extracted from 7000 journal articles, and 7500 materials from 6000 articles with 80 ≤ SiO2 + Al2O3 + CaO ≤105 wt% automatically classified. Both supervised and semi‐supervised models were used for dissolution rate prediction of glassy materials with all models pulling from a single data set (n = 802 reported dissolution rates from 105 different glasses). Supervised modeling utilized linear and decision tree regressions to determine features most predictive of dissolution rate, resulting in log‐linear relationships between rate and pH, inverse temperature (1/
- Award ID(s):
- 1922311
- NSF-PAR ID:
- 10442892
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of the American Ceramic Society
- Volume:
- 104
- Issue:
- 7
- ISSN:
- 0002-7820
- Page Range / eLocation ID:
- p. 3042-3057
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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