Mitigating Strength Loss in Geopolymers in Low-Temperature Environments by Sodium Nitrite Addition
Bibliographic record
Abstract
Geopolymer binders are a promising low-carbon substitute for Portland cement, but their behavior in cold climates remains underexplored. This study investigates the influence of sodium nitrite (NaNO2) on geopolymer properties cured at −10 °C for 28 days. The binders were formulated from bauxite residue, fly ash, and waste glass, and NaNO2 was added in various dosages as a chemical admixture. The geopolymer was tested for its setting time, compressive strength, and chemical and morphological characterizations. The addition of the 3 wt% NaNO2 significantly improved the strength retention in the cold environment, with a compressive strength of 40.7 MPa, compared to a geopolymer without an admixture (26.1 MPa). The X-ray diffraction (XRD) analysis confirmed the presence of gismondine, quartz, and FeSiO3, with NaNO2 remaining largely unreacted within the matrix. Fourier Transform Infrared Spectroscopy (FTIR) indicated the presence of Si–O–T bonds in the NaNO2-modified samples, which showed continued geopolymerization at low temperatures. Scanning electron microscopy (SEM) revealed reduced cracking and a denser microstructure with increasing concentrations of NaNO2. The results indicate that NaNO2 not only mitigates the adverse effects of subzero curing but also promotes structure development, and hence it is a viable admixture for enhancing the cold weather durability of geopolymer materials.
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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 | 0.000 | 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 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".