Refining Oxide Ratios in N-A-S-H Geopolymers for Optimal Resistance to Sulphuric Acid Attack
Bibliographic record
Abstract
As an alternative to Portland cement systems, geopolymers have been found to display superior acid resistance. However, at present, there exists no strategy to regulate the suitable design of mixtures. Particularly, the mechanisms underlying the effect of principal oxide ratios on the performance of N-A-S-H geopolymers in acid-rich environments are missing. Nor is any information available on the optimal range for SiO2/Al2O3, Na2O/Al2O3, and H2O/Na2O ratios under acid attack. This study investigates N-A-S-H geopolymers incorporating varying compositional oxide ratios to assess their resistance to sulphuric acid attack. The results show that the optimal range for acid-resistant durability is a narrow band within the optimal range for workability and strength. A SiO2/Al2O3 ratio of 3.4 balanced the enhanced degree of geopolymerization with an increase in the amount of permeable voids. At the same time, the Na2O/Al2O3 and H2O/Na2O ratios should be maintained within 0.8~0.9 and 8~10, respectively. Quantitatively, for the mixture designed within these optimal oxide ranges, the associated strength loss after 84 weeks of acid exposure was only about 10~20%, whereas other mix proportions may lead to a maximum strength loss of up to ~58%. Anything higher will offset the polycondensation and instead raise the volume of permeable voids. A sensitivity analysis suggests that the acid resistance depends chiefly on the Na2O/Al2O3 and H2O/Na2O ratios. The proposed multi-factor models predict the acid-induced neutralization efficiently, and the associated output displays a correlation with the loss in compressive strength.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".