Development of a green binder system for paper products
Bibliographic record
Abstract
BACKGROUND: It is important for industries to find green chemistries for manufacturing their products that have utility, are cost-effective and that protect the environment. The paper industry is no exception. Renewable resources derived from plant components could be an excellent substitute for the chemicals that are currently used as paper binders. Air laid pressed paper products that are typically used in wet wipes must be bound together so they can resist mechanical tearing during storage and use. The binders must be strong but cost-effective. Although chemical binders are approved by the Environmental Protection Agency, the public is demanding products with lower carbon footprints and that are derived from renewable sources. RESULTS: In this project, carbohydrates, proteins and phenolic compounds were applied to air laid, pressed paper products in order to identify potential renewable green binders that are as strong as the current commercial binders, while being organic and renewable. Each potential green binder was applied to several filter paper strips and tested for strength in the direction perpendicular to the cellulose fibril orientation. Out of the twenty binders surveyed, soy protein, gelatin, zein protein, pectin and Salix lignin provided comparable strength results to a currently employed chemical binder. CONCLUSIONS: These organic and renewable binders can be purchased in large quantities at low cost, require minimal reaction time and do not form viscous solutions that would clog sprayers, characteristics that make them attractive to the non-woven paper industry. As with any new process, a large-scale trial must be conducted along with an economic analysis of the procedure. However, because multiple examples of "green" binders were found that showed strong cross-linking activity, a candidate for commercial application will likely be found.
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.
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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".