Emerging Nanomaterials toward Uranium Extraction from Seawater: Recent Advances and Perspectives
Bibliographic record
Abstract
Nuclear energy holds great potential to facilitate the global energy transition and alleviate the increasing environmental issues due to its high energy density, stable energy output, and carbon-free emission merits. Despite being limited by the insufficient terrestrial uranium reserves, uranium extraction from seawater (UES) can offset the gap. However, the low uranium concentration, the complicated uranium speciation, the competitive metal ions, and the inevitable marine interference remarkably affect the kinetics, capacity, selectivity, and sustainability of UES materials. To date, massive efforts have been made with varying degrees of success to pursue a desirable UES performance on various nanomaterials. Nevertheless, comprehensive and systematic coverage and discussion on the emerging UES materials presenting the fast-growing progress of this field is still lacking. This review thus challenges this position and emphatically focuses on this topic covering the current mainstream UES technologies with the emerging UES materials. Specifically, this review elucidates the causality between the physiochemical properties of UES materials induced by the intellectual design strategies and the UES performances and further dissects the relationships of materials-properties-activities and the corresponding mechanisms in depth. This review is envisaged to inspire innovative ideas and bring technical solutions for developing technically and economically viable UES materials.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".