Preparation and optimization of a lignin-based pressure-sensitive adhesive
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 it