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Alkali-activated materials (AAMs) are candidates for high-strength lunar construction materials via in- situ resource utilization (ISRU) of aluminosilicate lunar regolith. To inform processing strategies for lunar AAMs, the shear-dependent rheological properties of a model AAM comprised of a sodium silicate activated metakaolin are measured from synthesis through gelation along with the compressive strength at longer reaction times. A combination of steady-shear, small amplitude oscillatory shear (SAOS), and Optimally Windowed Chirp (OWCh) techniques characterize the viscosity and shear moduli from initial slurry through the critical gel time. The critical gel point defines the processing time window regardless of the applied shear duration. Shearing prior to the critical gel point does not affect the critical gel time or viscoelastic properties of the material after the set point, or the 7-day compressive strength. However, the dynamic moduli prior to the critical gel time vary significantly based on the shear duration, and the critical gel exponent (n) increases with the duration of applied shear. These results demonstrate how to process without compromising final material properties. Metakaolin geopolymers exposed to low earth orbit (LEO) conditions on the Multi-purpose International Space Station Experiment Flight Facility (MISSE-FF) test station on the International Space Station (ISS) for six months of durability testing retain their compressive strength, furthering the technology readiness of aluminosilicate-derived construction material for future lunar construction. This study provides practical guidance for AAM processing protocols and insight into the effect of shear on the binder structural network valuable for both terrestrial and lunar ISRU
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Machine learning enabled closed‐form models to predict strength of alkali‐activated systems
Abstract Alkali‐activated mortar (AAM) is an emerging eco‐friendly construction material, which can complement ordinary Portland cement (OPC) mortars. Prediction of properties of AAMs—albeit much needed to complement experiments—is difficult, owing to substantive batch‐to‐batch variations in physicochemical properties of their precursors (i.e., aluminosilicate and activator solution). In this study, a machine learning (ML) model is employed; and it is shown that the model—once trained and optimized—can reliably predict compressive strength of AAMs solely from their initial physicochemical attributes. Prediction performance of the model improves when multiple compositional descriptors of the aluminosilicate are combined into a singular, composite chemostructural descriptor (i.e.,network ratioandnumber of constraints); thus, reducing the degrees of freedom. Through interpretation of the ML model's outcomes—specifically the variable importance for the AAMs’ compressive strength—a simple, easy‐to‐use, closed‐form analytical model is developed. Results demonstrate that the analytical model yields predictions of compressive strength of AAMs without scarifying much accuracy compared to the ML model. Overall, this study's outcomes demonstrate a roadmap—incorporates composite chemostructural descriptors in ML models—that can be employed to design AAMs to achieve targeted compressive strength.
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- PAR ID:
- 10367901
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of the American Ceramic Society
- Volume:
- 105
- Issue:
- 6
- ISSN:
- 0002-7820
- Format(s):
- Medium: X Size: p. 4414-4425
- Size(s):
- p. 4414-4425
- Sponsoring Org:
- National Science Foundation
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