Aerated Geopolymers for Building Decarbonization: Mix-Design, Chemistry, Microstructure, and Hygrothermal Performance
Bibliographic record
Abstract
The carbon footprint of buildings is mainly attributed to the embodied carbon of building materials and energy consumed by building services systems. In recent years, the search for materials with low embodied energy and better hygrothermal performance has increased. Geopolymers are among the low-carbon materials that are attracting attention. These are one category of alkali-activated materials. The fact that they can be synthesized from abundant earth elements at ambient hygrothermal conditions and their superior thermal resistance when aerated are the main reasons for their growing popularity. The hygroscopic nature geopolymers give them the capacity to regulate indoor relative humidity levels. This will decrease the energy consumed by building service systems. Aerated geopolymers can also be produced from commonly available industrial wastes such as fly ash and slag providing new opportunities for the creation of a circular economy. A proper understanding of the impact of manufacturing parameters on the hygrothermal performance of aerated geopolymers is a working progress. This dissertation aims to contribute to the efforts to understand the influence of various manufacturing parameters on the thermal and hydric performance of aerated geopolymers. The effect of the NaOH/Na ₂SiO₃,Si/Al, alkaline molarity, water, surfactant, and porogen on the thermal conductivity, porosity, moisture buffer capacity, vapor permeability, and adsorption-desorption isotherms of geopolymers is investigated. The results showed that the degree of polycondensation, pore statistics, and carbonation are the main features that should be paid attention to apply geopolymers as a building material. The SiO₂ and Al₂O₃ amount in the raw material and their rate of leaching as well as the concentrations of Na₂O, surfactant, and porogen have huge influence on the hygrothermal performance of aerated geopolymers. All the samples studied in this research program showed moisture buffer capacity well above 2 g/(m²%RH), which is excellent according to the NORDTEST categorization. They also satisfy RILEM (Réunion Internationale des Laboratoires et Experts des Matériaux, systèmes de construction et ouvrages)’s requirements to be classified as thermal insulating lightweight concrete. This indicates aerated geopolymers have the potential to be used as building material with a passive capacity to regulate indoor microclimate.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".