Sustainable Fiber-Based Materials as Super-bridging Agents, Adsorbents, and Ballast Media
Bibliographic record
Abstract
Aggregation combined with gravitational separation is the most commonly used method to treat water globally, but it carries a significant economic and environmental burden as the chemicals used in the process (e.g., coagulants) generate ~8 million tons of metal-based sludge waste annually. To simultaneously deal with the issues of process sustainability, cost, and efficiency, we developed materials reengineered from pristine or waste fibers (e.g., cellulose, polyester, cotton, and keratin) to serve as super-bridging agents, adsorbents and ballast media. This study shows that these sustainable materials (fibers, microspheres, and flakes functionalized with Si, Al and/or Fe) considerably increased the floc size (~6630 µm) compared to conventional physicochemical treatment (~520 µm; using alum and polyacrylamide). The fiber-based materials also reduced chemical usage (20–60 %) and improved contaminant removal during settling by increasing floc size and density. Moreover, the unprecedented size of flocs produced using fiber-based materials (13 times larger compared to conventional treatment) enabled easy floc removal by screening, thereby eliminating the need for a settling tank, a large and costly process unit used to treat more than 70% of water globally. Our results show that fiber-based materials can be effective solutions at removing classical (e.g., natural organic matter (NOM) and phosphorus, via electrostatic affinities) and emerging contaminants (e.g., microplastics and nanoplastics). Due to their large size (> 3000 µm), some Si-grafted and Fe-grafted fiber-based materials were easily recovered from settled/screened sludge and reused multiple times for coagulation/flocculation. These reusable materials combined with separation via screening could allow global water treatment facilities to reduce their capital and operating costs as well as their environmental footprint. Finally, our results also show that these materials could be used in synergy with coagulants and flocculants to improve existing water treatment plants for the removal of NOM, phosphorus, turbidity, total suspended solids and microplastics.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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 teacher head, 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".