Emerging technologies for selenium separation and recovery from aqueous systems: A review for sustainable management strategy
Bibliographic record
Abstract
Abstract Selenium (Se) is known both as an essential micronutrient for human health as well as a toxic element when consumed in excess. This work endeavours to critically review the existing selenium literature over the period 1980–2021, following a systematic sub‐classification into the domains of occurrence, speciation, and existing conventional and advanced technologies, while directing further research towards emerging integrated sustainable recovery strategies. The review reveals that the majority of the conventional and advanced separation techniques eventually lead to secondary pollution rather than offering a complete solution, whereas resource recovery studies from water are very scant. Some of the techniques, being miserably slow, cannot be applied in the situations of the occurrence of excess selenium in drinking water, posing a serious threat to human health. Research efforts have so far been directed mainly at the removal of selenium rather than recovery through processes that have the potential of turning selenium abatement technology into a sustainable one with value addition. Emerging integrated technologies involving resource recovery from waste streams or generated sludge such as physicochemical technologies, biochemical reduction, electro‐biological reduction, and membrane‐based hybrid technologies are presently at a very early development stage. Moreover, the application of studies where Se nanoparticles (SeNPs) ormetal selenides are obtained is very limited, which may be attributed to the particle size, purity, morphology, and challenges involved in their separation. This review evaluates the challenges in separation and resource recovery from contaminated streams and points future research in a new direction.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".