Surface functionalized nanostructured ceramic sorbents for the effective collection and recovery of uranium from seawater
Bibliographic record
Abstract
The ability to collect uranium from seawater offers the potential for a nearly limitless fuel supply for nuclear energy. We evaluated the use of functionalized nanostructured sorbents for the collection and recovery of uranium from seawater. Extraction of trace minerals from seawater and brines is challenging due to the high ionic strength of seawater, low mineral concentrations, and fouling of surfaces over time. We demonstrate that rationally assembled sorbent materials that integrate high affinity surface chemistry and high surface area nanostructures into an application relevant micro/macro structure enables collection performance that far exceeds typical sorbent materials. High surface area nanostructured silica with surface chemistries composed of phosphonic acid, phosphonates, 3,4 hydroxypyridinone, and EDTA showed superior performance for uranium collection. A few phosphorous-based commercial resins, specifically Diphonix and Ln Resin, also performed well. We demonstrate an effective and environmentally benign method of stripping the uranium from the high affinity sorbents using inexpensive nontoxic carbonate solutions. The cyclic use of preferred sorbents and acidic reconditioning of materials was shown to improve performance. Composite thin films composed of the nanostructured sorbents and a porous polymer binder are shown to have excellent kinetics and good capacity while providing an effective processing configuration for trace mineral recovery from solutions. Initial work using the composite thin films shows significant improvements in processing capacity over the previously reported sorbent materials.
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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) | 0.001 | 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".