A new approach for describing and solving the reversible Briggs‐Haldane mechanism using immobilized enzyme
Bibliographic record
Abstract
Recently immobilized enzymes have been widely used in industrial processes due to their outstanding advantages, such as high stability and recyclability; however, their kinetic behaviour is generally controlled by mass diffusion effects. Thus, in order to improve these enzymatic processes, a clear discernment between the kinetic and diffusion mechanisms that control the production of the metabolite require investigation. In practice, it is typical to establish apparent kinetics for immobilized enzyme operations, and the validity of the apparent kinetics is restricted to the studied cases. In this work, a new approach for mathematically describing the kinetic and diffusion mechanics in an immobilized biocatalyst bead is established, in which the fraction of residual enzymatic activity is included, and is defined as a measure of the active and available enzymes in the bead porous network. In addition, the diffusion and kinetic mechanisms are described by the effective diffusion coefficient and the free enzyme kinetics, since the porous network of the bead is assumed as the bioreaction volume. Therefore, free enzyme kinetics were determined from glucose to fructose bioconversion using a stirred tank reactor with free glucose‐isomerase, in which substrate and enzyme concentrations and temperature were varied. The fraction of residual enzymatic activity ( ) and the effective diffusion coefficient ( ) were obtained from the isomerization of glucose to fructose using a stirred tank reactor with immobilized glucose‐isomerase in calcium alginate beads at different substrate and enzyme concentrations. Finally, simulations were carried out to establish the bioreaction solid‐phase characteristics that most significantly influence productivity.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".