Enzymatic modification of secondary sludge by lipase and laccase to improve the nylon/sludge composite properties
Bibliographic record
Abstract
Secondary sludge from pulp and paper mills can be considered as potential filler for composite industry. Enzymatic modifications of the waste secondary sludge from pulp and paper mills to reduce the hydrophobicity and increase the molecular weight have been carried out by lipase and laccase, respectively. The enzymatic modification was performed to enhance the reinforcing capability of the secondary sludge for further composite production. The lipid content of the secondary sludge, which was measured to be 6 ± 0.5%, was hydrolyzed by lipase from Candida rugosa and the structural changes were followed by Fourier transform infrared (FTIR) spectroscopy. Laccase from Trametes versicolor was tested for its activity and reaction rate in the secondary sludge and the alkali-extracted lignin. Characterization of the sludge before and after the laccase treatment was carried out by FTIR spectroscopy. High pressure size exclusion chromatography (HPSEC) was applied to determine the molecular weight distribution of the lignin samples and also as a means for comparing modified and unmodified samples. Biokinetic parameters for the Michaelis-Menten kinetic model as a function of dissolved oxygen concentrations were determined the K m values to be 3.491 and 2.318 g/m 3 for sludge and the alkali extract, respectively. The FTIR results on the laccase-treated secondary sludge showed clear changes in the molecular structure, which was mainly attributed to the crosslinking reactions and generation of new bonds. Moreover, the HPSEC results revealed that laccase modifies the sludge by increasing the molecular weight. The manufactured nylon/sludge composites showed lower tensile strength for the lipase treated sludge/nylon composite. However, the laccase-treated sludge/nylon composite showed a statistically significant increase in the mechanical strength, which is attributed to the increase in the components molecular weight.
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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".