Near-Complete Phosphorus Recovery from Challenging Water Matrices Using Multiuse Ceramsite Made from Water Treatment Residual (WTR)
Bibliographic record
Abstract
Water treatment residual (WTR) is a burden for many water treatment plants due to the large volumes and associated management costs. In this study, we transform aluminum-salt WTR (Al-WTR) into ceramsite (ASC) to recover phosphate from challenging waters. ASC showed remarkably higher specific surface area (SSA, 70.53 m 2 /g) and phosphate adsorption capacity (calculated 47.2 mg P/g) compared to previously reported ceramsite materials (< 40 m 2 /g SSA and < 20 mg P/g). ASC recovered over 94.9% of phosphate across a wide pH range (3 – 11) and generally sustained > 90% of its phosphate recovery at high concentrations of competing anions (i.e., Cl - , F - , SO 4 2- , or HCO 3 - ) or humic acid (HA). We challenged the material with real municipal wastewater at 10°C and achieved simultaneous phosphate (>97.1%) and COD removal (71.2%). Once saturated with phosphate, ASC can be repurposed for landscaping or soil amendment. The economic analysis indicates that ASC can be a competitive alternative to natural clay-based ceramsite, biochar, or other useful materials. Therefore, ASC is an eco-friendly, cost-effective adsorbent for phosphate recovery from complex waters, shedding light upon a circular economy in the water sector.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".