Probing Silica-Kaolinite Interactions with Sum Frequency Generation Spectroscopy
Bibliographic record
Abstract
Treating the oil sand tailings ponds is a major challenge owing to the vast amounts of tailings and the need for a reliable treatment technique for releasing water and generating the highly consolidated material required for land reclamation. Treatment with chemicals such as lime (calcium (hydr)oxide) is a promising technology for tailings dewatering and consolidation, particularly at higher pH. Given that kaolinite and silica minerals are the main constituents of many oil sands, we have investigated the influence of lime and NaOH addition on the silica/water/kaolinite interface over the pH range 7-12.4 using vibrational sum frequency generation spectroscopy (SFG). With lime addition, we observe a complete disappearance of the vibrational features of the interfacial water molecules for planar silica in contact with an aqueous dispersion of kaolinite particles at pH 12 and above with a concurrent increase in the amount of adsorbed kaolinite based on the observed peak at 3694 cm-1. This suggests that the absence of water features in the SFG spectra is associated with conditions that facilitate dewatering. With NaOH addition, however, the interfacial water SF intensity is still significant even under highly alkaline conditions despite the increase in adsorbed kaolinite at high pH. To better understand the SFG observations, we perform -potential measurements on the planar silica aqueous interface and kaolinite aqueous suspensions under the same pH conditions with NaOH and lime addition.
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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) | 0.002 | 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".