Bibliographic record
Abstract
This project report is an attempt to find better conditions and reaction parameters for the potential commercialization of the enzymatic production of amoxicillin. A kinetic model from the literature was used to describe a reaction between p-hydroxyphenylglycine methyl ester (PHPGME) and 6-aminopenicillanic acid (6-APA) that is catalyzed by penicillin G acylase immobilized onto glyoxyl-agarose gel beads. A C++ computer program was developed using the Fourth Order Runge-Kutta method to simulate higher substrate and enzyme concentration during the reaction. For model validation, simulation results were compared with experimental data from the literature and fractional errors. This simulation model predicted 24% yield of amoxicillin at high substrate concentration (50mM of 6-APA; 300mM of PHPGME). It also predicted that increasing the enzyme concentration by four fold could produce a similar amoxicillin yield four times faster. The simulation results obtained in this work could be used in the future to aid in optimization and in further modifications of the kinetic model to predict even better yields. This enzymatic process could therefore become an industrial process to substitute the existing chemical route, which contains toxic organic solvents.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".