Advanced Uranium Removal with Pure, Nonfluorinated Ionic Liquids: Fine-Tuning Hydrophilic and Hydrophobic Properties for Enhanced Selectivity
Bibliographic record
Abstract
Ionic liquids (ILs) have significant potential for eco-friendly extraction of uranium from aqueous solutions, which is critical for nuclear technology, fuel cycle management, and environmental protection. This study examines the impact of the adjustable hydrophobic/hydrophilic properties of ILs on the removal of uranium(VI) (UO 2 2+ ) from aqueous solutions utilizing both a novel hydrophilic IL (1-butoxyethyl-1-methylmorpholinium butoxyethylphosphite – Mor 1–2O4 -BOEP) and 1-heptyl-1-methylmorpholinium heptylphosphite (Mor 1–7 -HP) as an example of a hydrophobic IL with a similar structure. The transfer mechanism of uranyl ions from water to organic or solid phases closely depends on the physicochemical properties of ILs, especially their hydrophobicity. The hydrophobic Mor 1–7 -HP extracts uranyl via neutral complex formation as UO 2 (NO 3 ) 2 -(Mor 1–7 -HP) 2 . Conversely, hydrophilic Mor 1–2O4 -BOEP induced selective precipitation as UO 2 (NO 3 )-(BOEP), transferring uranyl to the solid phase. Optimization of the working parameters, in terms of acidity of the aqueous solution and amount of ILs used, allowed the extraction of over 98% of U(VI). The stoichiometry of the organic complex and the precipitate was determined using physicochemical techniques. These tunable H-phosphonate-based ILs have advantages over traditional solvent extraction and conventional ILs, allowing easier handling, improved selectivity, and lower environmental impact. This work advances uranium separation techniques with applications in hydrometallurgy, particularly in the treatment of wastewater and radioactive waste for sustainable uranium recovery.
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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) | 0.001 | 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".