Rheological properties of iron oxide nanoparticle-modified cemented paste tailings materials
Bibliographic record
Abstract
The rheological characteristics of Cemented Paste Backfill (CPB) materials incorporating iron oxide nanoparticles (nFeO) remain unexplored. Understanding the yield stress and viscosity of CPB containing nFeO is crucial for implementing nano-CPB technology in underground mines. This study investigates the impact of nFeO on CPB rheology over time, considering various compositions (e.g., nFeO content, binder type, superplasticizer content). Rheological properties were measured at 0 min, 20 min, 1 h, 2 h, and 4 h, alongside electrical conductivity (EC), microstructural analyses (TG/DTG, XRD), pH, and Zeta potential assessments. The results show that nFeO significantly increases yield stress and viscosity, reducing flowability. The extent of this effect depends on binder type, curing time, and water content. The interaction between nFeO and the binder accelerates hydration, as confirmed by EC, DTG, and XRD results. Additionally, increasing nFeO reduces Zeta potential magnitude, lowering repulsion forces and further limiting flowability. However, incorporating 0.125% superplasticizer counteracts this effect, slowing cement hydration at later stages. Increasing slag content from 0% to 50% and 75% slightly reduces viscosity while significantly increasing yield stress. These findings provide new insights into nano-CPB technology, enhancing its potential for sustainable underground mine backfilling. By understanding the rheological behavior of nano-CPB, this study contributes to optimizing its application, balancing improved strength development with manageable flowability for effective underground placement.
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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".