A Review of the Effect of Plasticizers on the Physical and Mechanical Properties of Alginate-Based Films
Bibliographic record
Abstract
In recent years, there has been a growing attempt to manipulate various properties of biodegradable materials to use them as alternatives to their synthetic plastic counterparts. Alginate is a polysaccharide extracted from seaweed or soil bacteria that is considered one of the most promising materials for numerous applications. However, alginate potential for various applications is relatively limited due to brittleness, poor mechanical properties, scaling-up difficulties, and high water vapor permeability (WVP). Choosing an appropriate plasticizer can alleviate the situation by providing higher flexibility, workability, processability, and in some cases, higher hydrophobicity. This review paper discusses the main results and developments regarding the effects of various plasticizers on the properties of alginate-based films during the last decades. The plasticizers used for plasticizing alginate were classified into different categories, and their behavior under different concentrations and conditions was studied. Moreover, the drawback effects of plasticizers on the mechanical properties and WVP of the films are discussed. Finally, the role of plasticizers in the improved processing of alginate and the lack of knowledge on some aspects of plasticized alginate films is clarified, and accordingly, some recommendations for more classical studies of the plasticized alginate films in the future are offered.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".