Water-mediated adhesion of oil sands on solid surfaces at low temperature
Bibliographic record
Abstract
Adhesion of frozen granular materials on solid surfaces creates various problems for surface cleaning, reduces the carrying capacity of vehicles, and increases energy consumption for in-land transportation. Here we report that water content determines the adhesion strength of oil sands on solid surfaces at temperature of -2.5 ◦C to -20 ◦C. Our measurements by X-ray micro-computed tomography revealed that water forms capillary bridges between the sand particles and the solid substrate and more air gaps at the interface between oil sands and the substrate are filled with interstitial water at a higher content. We experimentally measured the minimal force required to push the frozen oil sands off the substrate and identified that the adhesion strength increased linearly with water content from 4% to 14% on both rubber and steel substrate. For short freezing time at a fixed water content, lowering the temperature increased the adhesion strength on the steel substrate. Fouling from a layer of bitumen or asphaltenes aggravated the adhesion of oil sands on steel. A theoretical model was proposed to rationalize the linear relationship between water content and the adhesion strength, based on the contact area between ice and the substrate. We also found an effective method to reduce the adhesion of oil sands by spraying a little amount of anti-freezing liquid on the substrate. Our approach may reduce the energy consumption in transport and processing of wet granular materials, and potentially save manpower and the cost from cleaning in industrial operations. The insight from our work may have wide applicability to many natural/industrial processes, such as soil formation, food processing, and porous structures in ice crystal-templating nanomaterials synthesis by freezing-drying.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".