Preparation of Wyoming bentonite nanoparticles
Bibliographic record
Abstract
Wyoming bentonite having a higher percentage of montmorillonite content shows a high swelling capacity, which enables it to seal itself when saturated, and it is widely used as a construction material of clay barrier system. The main goal of clay barriers is to seal the containment system from liquid intrusion, which could be assessed by the hydraulic conductivity of the barrier materials. The nanoparticles of the bentonite could be used to reduce the hydraulic conductivity. In this study, both the mechanical attrition and the synthesis process were assimilated to prepare nanoparticles of Wyoming bentonite. In the mechanical attrition process, the particles were ground in wet condition using planetary ball mill. Different parameters including types and size of balls, types of solvent, time period and speed of pulverisation for grinding were selected through continuous particle size analysis using the Zetasizer. The finer particles were synthesised using ultrasonication, centrifuging and filtering techniques. The particle size and the chemical composition of the dry particles were confirmed through scanning electron microscopy and energy-dispersive X-ray spectroscopy. In addition, the mineralogical change of the bentonite samples after the grinding process was observed using X-ray powder diffraction analysis. Finally, a particle size range between 30 and 100 nm was confirmed for the Wyoming bentonite nanoparticles.
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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 | 0.000 |
| 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 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".