Use of polysaccharides as a rheology modifying admixture for alkali activated materials for 3D printing
Bibliographic record
Abstract
This paper presents a comprehensive rheological study on the incorporation of polysaccharides as a rheology modifying admixture in alkali activated materials (AAMs) for enhancing their suitability in 3D printing applications. AAMs have gained significant attention in the construction industry but challenges with printability and buildability hinder their successful integration into 3D printing processes. Existing rheological modifiers are often included prior to mixing and printing AAMs to allow buildability- but with undesirable consequences on material properties. In this research, Xanthan Gum (XG), is examined as potential modifier to address these challenges. The study focuses on a systematic investigation into the effects of XG concentration on rheological response, printability, and mechanical properties. The goal of the study is to ascertain if the inclusion of XG in AAM mixes improves rheological properties in relation to 3D printed buildability and investigate XG’s impact on mechanical performance and setting time. The findings from this research contribute valuable insights into the development of a rheology modifying admixture for AAMs, paving the way for enhanced 3D printing without a significant negative impact on setting times or compressive strength. The implications of this work extend beyond 3D printing, offering a promising avenue for improved rheological properties of AAMs applied to other construction methods i.e. spraying/shotcreting. • Xanthan gum improves the buildability of 3D printed alkali activated materials. • Xanthan gum causes a gelation and meshing mechanism in alkali activated materials. • A dramatic increase in geopolymer yield stress can be induced with a small volume of xanthan gum.
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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 | 0.001 |
| 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".