Sustainable granular materials improve removal of natural organic matter, turbidity and microplastics during adsorption, ballasted flocculation and granular filtration
Bibliographic record
Abstract
Single-use metal-based coagulants, high molecular weight flocculants, engineered adsorbents, and porous media are key materials for several processes in water treatment. However, many of these materials are often expensive, unsustainable, and can increase the amount of sludge produced during the water treatment process. Moreover, the accumulation of toxic metals and synthetic flocculants limits sludge reusability as a fertilizer in agriculture. Herein, we propose reusable, low-cost and sustainable modified grains that can be simultaneously used as positively charged adsorbents to improve removal of soluble matter (natural organic matter), and ballast media to improve removal of particulate matter (turbidity or total suspended solids). Three different starting materials were used to synthesize these modified grains: pristine sand, recycled crushed glass, and grit extracted from a wastewater treatment plant, all of which were grafted with iron (hydr)oxides (Fe surface coverage of 13 and 81%, synthesized with 0.02 and 2 M Fe, respectively). The modified grains were then employed as (i) adsorbents to remove natural organic matter, (ii) ballast media during flocculation to increase floc size and density, and (iii) filtration media for simultaneous removal of natural organic matter and turbidity. Compared to conventional treatment alone (coagulation and flocculation), the incorporation of modified grains simultaneously used as adsorbents and ballast media increased removal of natural organic matter (up to 16%), microplastics (up to 92%), and turbidity (< 1 NTU, with settling rate 18 times faster). Ultimately, modified grains could be used in existing ballasted flocculation processes – replacing conventional sand – to provide an additional NOM removal. Iron-grafted sand, glass, and grit – simultaneously used as adsorbent and ballast media – reduced the settling duration by 18 times compared to conventional treatment (without iron-grafted media) and increased removal of natural organic matter (up to 23%) and microplastics (up to 92%). • Three different starting materials were used to synthesize Fe-coated grains. • Fe surface area coverage of 81% was obtained. • Modified grains were employed as adsorbents and ballast media • Modified grains media increased removal of microplastics (up to 92%). • Modified grains could be used in flocculation processes to provide NOM removal.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".