Bigels and multi-component organogels: An overview from rheological perspective
Bibliographic record
Abstract
Polymeric gels, an important class of soft matter systems, are widely used for commercial applications particularly in food products (i.e., saturated fat replacement). The efficiency of the product for a particular application can be directly linked to its rheological signature. However, the rheological imprint of these structured systems is significantly dependent on the interactions between their individual components. Therefore, the rational understanding of interacting components, their structural arrangements and the resultant characteristics of a hybrid system is vital, in order to speed-up our progress in recognizing the suitable combination of gelling agents and also to tune the technological and functional properties of final product. The main aim of this narrative review article is to look at the synergistic interactions (i.e., interpenetrating polymer structures) between different components of the hybrid systems (i.e., multi-component organogels or bigels), in order to unveil the resultant rheological characteristics for different commercial applications including food systems. However, multi-component hydrogel systems have not been discussed in this review. The existence of synergistic interactions between different gelators in multi-component organogels (i.e., higher values of hardness and moduli (G'and G'') of multi-component organogel than the corresponding mono-component system) is clearly evident from the literature. This synergy also helps in optimizing the rheological signature of final system by exploiting the concept of formulation engineering. However, this exciting concept of synergistic enhancement in properties has not yet been explored in the field of bigels. The incorporation of colloidal particles in the gelled system resulted in increased stability and higher moduli values, as compared to the gels without particles. This exciting approach can also be quite useful for designing food products by exploiting the useful properties of gelators and (nano)particles, in a single system.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".