Phosphorylated cellulose nanofibers exhibit exceptional capacity for uranium capture
Bibliographic record
Abstract
Abstract We investigate the adsorption of hexavalent uranium, U(VI), on phosphorylated cellulose nanofibers (PHO-CNF) and compare the results with those for native and TEMPO-oxidized nanocelluloses. Batch adsorption experiments in aqueous media show that PHO-CNF is highly efficient in removing U(VI) in the pH range between 3 and 6. Gelling of nanofiber hydrogels is observed at U(VI) concentration of 500 mg/L. Structural changes in the nanofiber network (scanning and transmission electron microscopies) and the surface chemical composition (X-ray photoelectron spectroscopy) gave insights on the mechanism of adsorption. The results from batch adsorption experiments are fitted to Langmuir, Freundlich, and Sips isotherm models, which indicate a maximum adsorption capacity of 1550 mg/g, the highest value reported so far for any bioadsorbent. Compared to other metals (Zn, Mn, and Cu) and typical ions present in natural aqueous matrices the phosphorylated nanofibers are shown to be remarkably selective to U(VI). The results suggest a solution for the capture of uranium, which is of interest given its health and toxic impacts when present in aqueous matrices.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".