Utilization of Pulp and Paper Mill Sludge as Filler in Nylon Biocomposite Production
Bibliographic record
Abstract
The biological treatment of pulp and paper mills effluents results in the production of waste secondary sludge which is hard and costly to dewater and dispose. Secondary sludge, which is structurally comparable to the municipal sewage sludge, is composed of microbial cells, organic woody materials, and ash. In this work, the use of this waste biosolid as renewable and cost-cutting filler in the composite industry is proposed. Moreover, the effect of enzymatic treatment of the waste biosolid on the final properties of the manufactured biocomposite is studied. The high protein content of the secondary sludge (35 ± 5%) and the surface thermodynamics measured by Inverse Gas chromatography (IGC) led us to choose Nylon 11 as the main polymeric matrix. The biocomposites samples produced by compounding and injection molding of different mixtures of dried secondary sludge and Nylon were tested. The results of mechanical strength tests showed that a 10% sludge content does not lead to any significant deterioration of either tensile or flexural strengths. Therefore, it is concluded that the secondary sludge may be used as filler to reduce the cost while maintaining the mechanical properties of Nylon. Enzymatic modification of the waste biosolid to advance its application from cheapening filler to reinforcing filler has also been proposed in this work. Lipase and laccase utilized for the modification of the sludge in order to reduce the hydrophobicity and increase the molecular weight, respectively. Lipase application did not lead to any significant changes in either tensile or flexural strengths. This is attributed to the rather low content of lipids in the sludge. On the other hand, enzymatic modification of the sludge by laccase which increases the molecular weight of the existing lignins, resulted in significant improvement of the flexural strength of the manufactured biocomposite.
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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) | 0.000 | 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) | 0.000 | 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".