Super-bridging fibrous materials for water treatment
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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) | 0.026 | 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 it