Rheological Properties of Aluminium Oxide Nanoparticle-Modified Cemented Paste Tailings Materials
Bibliographic record
Abstract
There is currently no research examining the rheological properties of cementitious paste backfill (CPB) materials containing aluminium oxide nanoparticles (nAlO). Knowing the yield stress and viscosity of CPB containing nAlO is crucial, especially when applying nano-CPB technology in underground mines. The purpose of this work is to thoroughly examine how nAlO affects the rheological characteristics of CPB and how those characteristics change over time. Yield stress and viscosity measurements are performed on CPB samples with different compositions (e.g., nAlO content, binder type, and superplasticizer content) at intervals of 0 min, 20 min, 1 h, 2 h, and 4 h. The study also includes measurements of the pH and zeta potential of the materials, microstructural studies (TG/DTG and XRD), and electrical conductivity (EC). The findings show that adding nAlO to CPB significantly changes its rheological properties, which in turn affects flowability. The yield stress and viscosity of CPB samples are greatly increased by the incorporation of nAlO, with the degree of influence varying based on variables including water content, curing duration, and type of binder. Because of the nAlO-induced microstructural changes in the CPB material, the interaction of nAlO and a larger fraction of nAlO, along with an increase in curing time, raises rheological characteristics and decreases paste flowability. The results of EC, DTG, and XRD, which show that binder hydration improves with nAlO dosage, corroborate this. Moreover, as nAlO content increases, the zeta potential decreases in magnitude, resulting in stronger repulsion forces and reduced flowability. However, EC, XRD, and DTG analyses suggest that the addition of 0.125% superplasticizer counteracts the flowability reduction caused by nAlO, as the superplasticizer slows down the cement hydration rate at very early curing stages. Moreover, the increase in the slag percentage from 0% to 50% and 75% of the binder content slightly decreases viscosity but greatly increases yield stress. The study’s fresh perspectives contribute to the advancement of nano-CPB technology and have important ramifications for the practical use of this technology in underground mine backfill operations.
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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) | 0.000 | 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".