Functionalised hydroxyapatites for heavy metals removal from water: a review
Bibliographic record
Abstract
The emergence of pollutants in the aquatic environment and their impact on living species is alarming and a source of growing concern. Among these concerns, the removal of heavy metal ions from wastewater is a major challenge for the protection of the environment and for human health. Since standard primary and secondary treatment plants fail to eliminate these harmful species, a cost-effective tertiary treatment approach is proposed. The most commonly used methods for heavy metal removal are adsorption, precipitation, membrane, electric and osmosis. According to recent studies, it has been shown that adsorption is the most adopted technique using porous and surface functionalised adsorbents. With the advancement of technology and the requirement of environmental protection, many adsorbents have been used for wastewater treatment, based on to their effectiveness. Hydroxyapatite-based functional materials have attracted significant attention as green and environmentally friendly adsorbents due to their outstanding ability to remove various inorganic and organic pollutants from wastewater. Their open structure and porous surface support suitable adsorption mechanisms including precipitation, ion exchange, surface complexation, and neutralisation reactions. Developing HAp functionalised with various chelating agents has been proven to achieve a synergistic effect to improve the removal of heavy metals from wastewater. This topic has attracted the attention of the scientific community due to the good efficiency of hydroxyapatite-based materials and their ease of preparation as well as their low cost. This review critically examines recently publications on this topic.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".