Michaelis-Menten Kinetic Parameters of Coconut Coir Enzymatic Hydrolysis
Bibliographic record
Abstract
The limitation of fossil oil reserves and environmental pollution has been current problems that need to be solved. Biofuels such as ethanol can be alternative energy source that can reduce demand on fossil fuel and environmental problem. Food wastes such as coconut coirs are abundant and contain carbohydrate which can be used as the substrate for biofuel production. Pretreatment and hydrolysis are important stages which have to be applied on such lignocellulose materials before fermentation process to produce biofuel. This article presents Michaelis-Menten kinetic parameters for coconut coir enzymatic hydrolysis. Coconut coirs collected from several local markets in Surabaya were subjected to alkaline pretreatment using 11% sodium hydroxide solution at 121oC. Hydrolysis steps were carried out using commercial enzymes at various initial substrate concentrations. The hydrolysis conditions were at 50oC and pH 4.8. The concentrations of reducing sugar produced in the reactions were measured at certain time intervals. Initial rate of reactions of each reaction batch were then determined. Finally, kinetic parameters of Michaelis-Menten model for enzymatic reaction were determined by fitting the initial rate of reactions and initial substrate concentration data. From nonlinear fitting, the maximum reaction rate (Vm) is 4.9´104 1/h and the value of KM is 4,195 mg/L.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".