Application of chitosan‐entrapped β‐galactosidase in a packed‐bed reactor system
Bibliographic record
Abstract
Abstract The model enzyme β‐galactosidase was entrapped in chitosan gel beads and tested for hydrolytic activity and its potential for application in a packed‐bed reactor. The chitosan beads had an enzyme entrapment efficiency of 59% and retained 56% of the enzyme activity of the free enzyme. The Michaelis constant (Km) was 0.0086 and 0.011 μmol/mL for the free and immobilized enzymes, respectively. The maximum velocity of the reaction (Vmax) was 285.7 and 55.25 μmol mL−1min−1for the free and immobilized enzymes, respectively. In pH stability tests, the immobilized enzyme exhibited a greater range of pH stability and shifted to include a more acidic pH optimum, compared to that of the free enzyme. A 2.54 × 16.51‐cm tubular reactor was constructed to hold 300 mL of chitosan‐immobilized enzyme. A full‐factorial test design was implemented to test the effect of substrate flow (20 and 100 mL/min), concentration (0.0015 and 0.003M), and repeated use of the test bed on efficiency of the system. Parameters were analyzed using repeated‐measures analysis of variance. Flow (p< 0.05) and concentration (p< 0.05) significantly affected substrate conversion, as did the interaction progressing from Run 1 to Run 2 on a bed (p< 0.05). Reactor stability tests indicated that the packed‐bed reactor continued to convert substrate for more than 12 h with a minimal reduction in conversion efficiency. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 91: 1294–1299, 2004
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".