Preparation and optimization of a lignin-based pressure-sensitive adhesive
Bibliographic record
Abstract
D-optimal designs were applied to find the best parameters for the preparation of lignin-based pressure-sensitive adhesives (PSA) for sticky notes. Organosolv lignin was directly incorporated into a polycarboxylate polyether (PCE)/water matrix. The independent variables considered in the experimental design were the ratio between PCE, lignin, and water and the curing parameters. The distance traveled by the ball (tack), the peel-off losses and the final water content were the analyzed responses that allowed the optimization of the PSA formulation. The accuracy, the precision and the efficiency of the model were evaluated during the first experimental design for the formulation of the lignin-based adhesive named DES-OL-ADH. This formulation was optimized during the second experimental design abbreviated DES-OL-OPT. The coefficients of determination of the tack, the peel-off losses and the final water content were 0.98, 0.99 and 0.99, respectively. The model was satisfactory which allows the optimization of the PSA formulation. The DES-OL-OPT suggests that lignin-based PSA can be prepared as a sticky note application with 5 wt% of lignin, 84 wt% of PCE and 11 wt% of added water in the oven at 130 °C for 60 min, which shows a higher tackiness and similar peel-off losses than the commercial sticky notes PSAs.•Protocol optimization for the preparation of a green pressure sensitive adhesive (PSA) from PCE polymer, lignin, and water.•Influence of 5 compositional or processing parameters on adhesive performance through a 2-steps d-optimal experimental design.•Development of a new method, based on peel-off losses, to assess the performance of a PSA.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".