Protein extraction from secondary sludge of paper mill wastewater and its utilization as a wood adhesive
Bibliographic record
Abstract
In this study, secondary sludge (SS) from a kraft paper mill was used as a source of biomass to recover protein and investigate its potential use as a wood adhesive. The process of protein recovery involved disruption of the floc structure in alkaline medium to disintegrate and release intercellular contents into the aqueous phase followed by separation of soluble protein. Finally, the soluble protein was subjected to low pH precipitation and the pelletized sludge protein, referred to as recovered sludge protein (RSP) was tested for crude protein, moisture, and other contents. A significant process yield of 90% in terms of precipitation of soluble protein from disintegrated sludge was estimated through calorimetric studies, whereas an overall material balance confirmed a RSP yield of up to 23% based on total suspended solids of raw sludge. The RSP containing 30% crude protein was used as a wood adhesive and its adhesion performance was compared with soy protein isolate (SPI) and phenol formaldehyde (PF) resin. The testing of plywood lap joints has shown up to 41% shear strength level of RSP adhesive compared to PF. This work demonstrates the technical feasibility and potential of SS as a biomass resource to develop eco-friendly adhesives for wood composite applications.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Bench or experimental | low |
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".