High‐efficiency separation of oil sands using <scp>ChCl‐based</scp> deep eutectic solvents
Bibliographic record
Abstract
Abstract Deep eutectic solvents (DESs) are a kind of potential lixiviant for extraction processing. Unlike conventional ionic liquids (ILs), DESs are relatively cheap and environmentally friendly. Herein, three different ChCl‐based DESs, namely, choline chloride/urea, choline chloride/ethylene glycol, as well as choline chloride/propandioic acid, were synthesized and used to enhance bitumen recovery from oil sand by petroleum ether extraction. The results showed a multiphase system formed after mixing the components at ~25°C, consisting of sands and clays, a DES layer, and a petroleum ether layer containing the bitumen. These DESs were immiscible with bitumen or petroleum ether. Coupled with a density difference, a clear phase separation was presented between the bitumen–petroleum ether mixture and DES. The DES functioned as a separating agent, keeping the petroleum ether–bitumen mixture and spent sand apart from each other. The results showed that the bitumen recovery was increased by ~12% compared with that without the DESs. We deduced that the enhancement in the separation may result from the reduction of adhesion between bitumen and sand by the DESs. The ChCl‐based DESs and petroleum ether could be readily recycled to reduce industrial costs. After 10 cycles, the bitumen recovery remained above 86%.
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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) | 0.000 | 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 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".