Transforming drinking water treatment residuals into efficient adsorbents: A review of activation and modification methods
Bibliographic record
Abstract
The management of drinking water treatment residuals (DWTRs) poses significant environmental and economic challenges for water treatment facilities; however, these residues have considerable potential as effective adsorbents for pollutant removal. The objectives of this review are to evaluate research conducted from 2015 to 2024 on treatment and modification techniques aimed at enhancing DWTRs' efficacy as adsorbents, analyze the influence of preparation methods on DWTRs performance, evaluate DWTRs adsorbents for different pollutants, and discuss the limitations and challenges in DWTRs applications. The review addresses the knowledge gap by detailed analysis of these advanced modification methods, which have not been extensively reviewed before, and their direct impact on the physicochemical properties and adsorption performance of DWTRs. The review explores various methods including thermal treatment, chemical activation, granulation, pelletization, and the development of composite materials. Key findings indicate that thermal treatment significantly increases surface area and porosity, while chemical activation introduces functional groups that enhance adsorption capacity. Composite DWTRs, incorporating metals, organic compounds, or magnetic properties, demonstrate superior performance in adsorbing diverse contaminants such as dyes and heavy metals. Despite these advancements, challenges remain, particularly in reporting the life cycles and costs of the treated and modified DWTRs and the regeneration of spent adsorbents. The review highlights the importance of optimizing preparation techniques to enhance the physicochemical properties and adsorption performance of DWTRs. By synthesizing existing knowledge and identifying key areas for future research, this review aims to advance sustainable practices in water treatment and resource recovery, aligning with global sustainability goals.
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 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".