A Superfast Sorbent Based on Textile-Grade Poly(acrylonitrile) Fiber/Fabric. Rapid Removal of Uranium from Mildly Acidic Aqueous Solutions of Low Concentration
Bibliographic record
Abstract
Poly(ethyleneimine) cross-linked with glutaraldehyde has been coated on partially hydrolyzed poly(acrylonitrile) (textile grade), used in fiber as well as fabric form, by a copper-mediated gel-coating process. For uranium sorption from aqueous solutions at low substrate concentrations (≤1 mmol/L), the gel-coated fiber or fabric, containing 15% resin (w/w on a dry basis), exhibits an order of magnitude higher sorption capacity and 2 orders of magnitude higher initial rate of sorption, as compared to conventional bead-form anionic resin Dowex 21K. For example, in a 1 mM solution of UO 2 SO 4 at pH 4.0, the sorption capacities of the gel-coated fiber and Dowex 21K resin beads are respectively 106 and 12 mg of U/g of dry sorbent, while the initial rates of sorption are 3.5 and 0.02 mg of U/(g of dry sorbent)(s), respectively. With the sorption on the gel-coated fiber being by both ion exchange and chelation, it shows a significantly greater salt (NaCl) tolerance than Dowex 21K on which the sorption takes place only by ion exchange. Following fast stripping with dilute mineral acid, the gel-coated fiber is regenerated by a simple wash with dilute alkali to its original capacity. The sorption rate data on the fiber, in the initial stage, fit to a shell−core model equation derived for a cylindrical-shaped sorbent of a very large length/diameter ratio.
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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) | 0.002 | 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".