Stripping Uranium from Seawater-Loaded Sorbents with the Ionic Liquid Hydroxylammonium Acetate in Acetic Acid for Efficient Reuse
Bibliographic record
Abstract
A new stripping and recovery process was developed to harvest the uranium recovered from seawater with amidoxime-functionalized polyethylene fiber sorbents and allow reuse of the sorbent without loss of capacity and without the need to recondition the sorbent before reuse. Hydroxylammonium acetate ([NH 3 OH][OAc])/aqueous acetic acid (AcOH) solutions were used as weakly acidic stripping agents and the stripped uranium as a soluble acetate was further immobilized on shrimp shells. These solutions also stripped the vanadium and other metal ions coadsorbed, which reduce capacity through competition with uranium for sorbent binding and can resist stripping even by strong acids. [NH 3 OH][OAc]/AcOH was found to allow recovery of more than 85% of the uranium although at a substantially longer time than the current 0.5 M HCl-stripping solutions, (less than 12 h vs 48–72 h, respectively); however, the use of HCl severely compromises the capacity of the sorbent in subsequent reuse (a 50% lost was observed on the third reuse of the fiber). Both [NH 3 OH][OAc] and the acetic acid were necessary to achieve high uranium recovery without sacrificing the sorbent’s capacity on reuse. The ability to reuse the sorbent without pretreatment and with minimal capacity loss could be an important step toward making the extraction of uranium from seawater energy-efficient and economically viable.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".