Phosphorus removal and recovery from wastewater via hybrid ion exchange nanotechnology: a study on sustainable regeneration chemistries
Bibliographic record
Abstract
Abstract Technologies that allow for removal and subsequent recovery and reuse of phosphorus from polluted streams are imperative. One such technology is hybrid ion exchange nanotechnology (HIX-Nano), which may allow to produce a valuable nutrient solution following phosphorus desorption of the saturated media. This study evaluated the potential of four regeneration chemistries to desorb phosphorus from a commercially available HIX-Nano resin hybridized with iron oxide nanoparticles using a design of experiments (DoE) approach. More sustainable and less harmful regeneration solutions using a KOH/K 2 SO 4 blend or a recovered NH 4 OH alkaline solution, along with tap water instead of synthetic acid, were compared to a control solution of KOH and H 2 SO 4 . Among the four regeneration methods studied, using the combination of recovered NH 4 OH and tap water shows the highest phosphorus recovery potential because: (i) it involves low cost and sustainable products, (ii) it showed a relatively high recovery efficiency (75 ± 15% as compared to the control at 89 ± 13%), and (iii) it did not demonstrate any significant dampening of the resin longevity after five adsorption and desorption cycles. Based on the DoE data, a series of regression models was developed to generate understanding of the effect of important operational parameters (volume of the regenerant solution, rinse speed, strength of the alkaline solution) on the phosphorus concentration in the recovered nutrient solution. Overall, this study indicates that HIX-Nano may contribute to providing a cost-effective and sustainable technological solution to tackle the phosphorus problem in wastewater treatment applications across the globe.
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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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".