Phosphorus pollution control using waste-based adsorbents: Material synthesis, modification, and sustainability
Bibliographic record
Abstract
The utilization of waste materials to control phosphorus (P) pollution has been intensively studied as a promising strategy to achieve sustainable wastewater treatment. Although many methods are proposed and investigated to develop modified waste-based adsorbents, a core yet still-debated issue is their effectiveness and viability in real-world applications. Therefore, this critical review summarizes the current research state on the use of waste materials and their modified forms as adsorbents for dissolved reactive P removal from wastewater. Various performance improvement methods are compiled into the research outcomes to highlight three significant efforts that scientists have contributed to promoting the application of waste-based adsorbents: (i) how to enhance the P removal efficiency; (ii) how to scale up implementation; and (iii) how to achieve sustainable management. Furthermore, this review proposes a paradigm of waste-based absorbent in the P removal process to systematically formulate a complete sustainable management strategy for practical application. Overall, this review offers a guide for the development and application of waste-based adsorbents for P removal from wastewater.
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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".