Utilization of secondary sludge as filler in composites: surface energy and final mechanical properties
Bibliographic record
Abstract
Secondary sludge from pulp and paper mills can be considered as a potential filler for composite industry. The surface thermodynamics of the secondary sludge from two different pulp and paper mills, processing capability, and material characteristics of biocomposites filled by secondary sludge were studied in this study. Inverse gas chromatography (IGC) has been employed to study the surface characteristics of the secondary sludge. Also, the cellular biopolymers were extracted and their surface energy determined by IGC. Based on the surface thermodynamics and the chemical structure of the secondary sludge, Nylon 11 was selected as the polymeric matrix. The dispersive component of surface energy for the secondary sludge samples was obtained in the range 60—42 mJ/m 2 measured at 313—373 K, which is high enough to allow the biosolid to be coupled with conventional polymeric resins. The manufactured nylon/sludge composites showed acceptable, yet not improved, mechanical strength. Also, 10% of dried sludge as filler proved to be an effective amount, which is sufficient to fill but not deteriorate the tensile and flexural strengths of the composite. Sludge-filled composites compounded by a twin-screw extruder exhibit considerably better tensile properties than those compounded by the K-mixer. Maleated polyolefins used as coupling agents also improved the composite’s mechanical properties significantly.
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.
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) | 0.001 | 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".