A Study of the Synergistic Interaction of Konjac Glucomannan/Curdlan Blend Systems under Alkaline Conditions
Bibliographic record
Abstract
To improve the gelation performance of konjac glucomannan (KGM) thermo-irreversible gel in the condition of alkaline, this study investigated the interactions between KGM and curdlan (CUD) in terms of the sol state and gelation process. The apparent viscosity, rheological properties during heating and cooling, thermodynamic properties, gelation properties and water holding capacity of KGM/CUD blend systems in an alkaline environment were studied using physical property testing instruments and methods. The results showed that the viscosity of the KGM/CUD blended solution was greater than the value calculated from the ideal mixing rules in the condition of alkaline (pH = 10.58). As the proportion of CUD in the system increased, the intersection of storage modulus (G') and loss modulus (G") shifted to low frequencies, the relaxation time gradually increased, and the degree of entanglement of molecular chains between these two components gradually increased. The addition of CUD helped decrease the gelation temperature of KGM, increased the gelation rate and inhibited the thinning phenomenon of KGM gels at low temperatures (2-20 °C). The addition of CUD increased the hardness and gel strength of KGM but did not significantly improve the water holding capacity of the KGM/CUD blend gel. The process of mixing KGM and CUD improved the thermal stability of the gel. In summary, KGM/CUD exhibited excellent compatibility under alkaline conditions, and the blend systems produced a "viscosifying effect". KC8 and KC5 show better thermal stability, low temperature resistance and gel strength compared to KGM. This blended gel can be used as a structural support material to provide reference for the development of konjac bionic vegetarian products.
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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) | 0.000 | 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) | 0.001 | 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".