Sequestration of U(VI) from Acidic, Alkaline, and High Ionic-Strength Aqueous Media by Functionalized Magnetic Mesoporous Silica Nanoparticles: Capacity and Binding Mechanisms
Bibliographic record
Abstract
Uranium(VI) exhibits little adsorption onto sediment minerals in acidic, alkaline or high ionic-strength aqueous media that often occur in U mining or contaminated sites, which makes U(VI) very mobile and difficult to sequester. In this work, magnetic mesoporous silica nanoparticles (MMSNs) were functionalized with several organic ligands. The functionalized MMSNs were highly effective and had large binding capacity for U sequestration from high salt water (HSW) simulant (54 mg U/g sorbent). The functionalized MMSNs, after U exposure in HSW simulant, pH 3.5 and 9.6 artificial groundwater (AGW), were characterized by a host of spectroscopic methods. Among the key novel findings in this work was that in the HSW simulant or high pH AGW, the dominant U species bound to the functionalized MMSNs were uranyl or uranyl hydroxide, rather than uranyl carbonates as expected. The surface functional groups appear to be out-competing the carbonate ligands associated with the aqueous U species. The uranyl-like species were bound with N ligand as η 2 bound motifs or phosphonate ligand as a monodentate, as well as on tetrahedral Si sites as an edge-sharing bidentate. The N and phosphonate ligand-functionalized MMSNs hold promise as effective sorbents for sequestering U from acidic, alkaline or high ionic-strength contaminated aqueous media.
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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".