Super-bridging fibrous materials for water treatment
Bibliographic record
Abstract
Abstract To deal with issues of process sustainability, cost, and efficiency, we developed materials reengineered from fibers to serve as super-bridging agents, adsorbents, and ballast media. These sustainable fiber-based materials considerably increased the floc size (~6,630 µm) compared to conventional physicochemical treatment using a coagulant and a flocculant (~520 µm). The materials also reduced coagulant usage (up to 40%) and flocculant usage (up to 60%). These materials could be used in synergy with coagulants and flocculants to improve settling in existing water treatment processes and allow facilities to reduce their capital and operating costs as well as their environmental footprint. Moreover, the super-sized flocs produced using fiber-based materials (up to ~13 times larger compared to conventional treatment) enabled easy floc removal by screening, eliminating the need for a settling tank, a large and costly process unit. The materials can be effective solutions at removing classical (e.g., natural organic matter (NOM) and phosphorus) and emerging contaminants (e.g., microplastics and nanoplastics). Due to their large size, Si- and Fe-grafted fiber-based materials can be easily recovered from sludge and reused multiple times.
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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) | 0.002 | 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".