Enhancing Uranium Uptake by Amidoxime Adsorbent in Seawater: An Investigation for Optimum Alkaline Conditioning Parameters
Bibliographic record
Abstract
A high-surface-area polyethylene-fiber adsorbent (AF160-2) has been developed at the Oak Ridge National Laboratory by radiation-induced graft polymerization of acrylonitrile and itaconic acid. The grafted nitriles were converted to amidoxime groups by treating with hydroxylamine. The amidoximated adsorbents were then conditioned with potassium hydroxide (KOH) by varying different reaction parameters such as KOH concentration (0.2, 0.44, and 0.6 M), duration (1, 2, and 3 h), and temperature (60, 70, and 80 °C). Adsorbent screening was then performed with simulated seawater solutions containing sodium chloride and sodium bicarbonate, at concentrations found in seawater, and uranium nitrate at a uranium concentration of ∼7–8 ppm and pH 8. Fourier transform infrared spectroscopy and solid-state NMR analyses indicated that a fraction of amidoxime groups was hydrolyzed to carboxylate during KOH conditioning. The uranium adsorption capacity in the simulated seawater screening solution gradually increased with conditioning time and temperature for all KOH concentrations. It was also observed that the adsorption capacity increased with an increase in concentration of KOH for all the conditioning times and temperatures. AF160-2 adsorbent samples were also tested with natural seawater using flow-through experiments to determine uranium adsorption capacity with varying KOH conditioning time and temperature. Based on uranium loading capacity values of several AF160-2 samples, it was observed that changing KOH conditioning time from 3 to 1 h at 60, 70, and 80 °C resulted in an increase of the uranium loading capacity in seawater, which did not follow the trend found in laboratory screening with stimulated solutions. Longer KOH conditioning times lead to significantly higher uptake of divalent metal ions, such as calcium and magnesium, which is a result of amidoxime conversion into less selective carboxylate. Scanning electron microscopy showed that long conditioning times may also lead to adsorbent degradation.
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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".