Spherical Alginate Granules Formulated for Quick-Release Active Subtilisin
Bibliographic record
Abstract
Novel attrition-resistant and spherical enzyme granules encapsulating active subtilisin were formed by emulsification of 2% alginate sol loaded with active enzyme, instantaneous gelation triggered through in situ release of Ca(2+) (internal gelation), particle separation, and finally acetone extractive drying. Granular subtilisin was highly active, readily dispersible, and mechanically robust. This technique serves as a new and attractive alternative to established enzyme granulation processes, such as fluid bed coating, extrusion followed by marumerization, drum granulation, or prilling, for use in industrial enzyme applications such as detergents, textile manufacturing, and food processing. The formulation and encapsulation conditions were optimized to maximize the resistance of the granule to compression and impact forces, consistent with enzyme release and particle dispersion in detergent solutions. Well characterized alginates, with specified guluronic/mannuronic acid (G/M) content and molecular weight, were used in the formulation. The characteristics of the resulting microspheres, including their size and distribution, morphology, shrinkage, compression resistance, impact strength, solubility and encapsulation yield, were examined. Spherical dry granules were formulated with a mean diameter of 500 microm with particle sizes ranging from 300 to 800 microm. Dry alginate granules were discrete, spherical, and glossy white and exhibited impact strength, compression resistance, and solubility difference dependent on composition. Reduced starch levels, high alginate concentration, low alginate molecular weight, and use of high guluronate alginates resulted in the lowest dust level and highest compression resistance. Subtilisin mass yields were approximately 50%, and specific activity yields ranged from 60% to 100%. A formulation consisting of 3% SG150 alginate, 10% starch, 10% TiO(2), and 1% CaCO(3) provided granules appropriate for use in detergent application.
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How this classification was reachedexpand
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".