Lactose electroisomerization into lactulose: Effect of the electrode material, active membrane surface area-to-electrode surface area ratio, and interelectrode-membrane distance
Bibliographic record
Abstract
The aim of the present work was to study and develop an innovative, clean, and environmentally friendly process for lactulose synthesis by electroactivation of lactose. In this work, the electrode material (type 304 stainless steel, titanium, and copper), dimensionless interelectrode-membrane distance at the cathodic compartment (0.36, 0.68, and 1), and the membrane:electrode surface area ratio (0.23, 0.06, and 0.015) were considered to be the factors that could affect the kinetic conversion of lactose into lactulose. The reactions were conducted under an initial lactose concentration of 0.15mol/L at 10°C, Froude number (mixing speed) of 2.05×10(-2), and electric current intensity of 300mA for 30min. The highest lactulose formation yield of 32.50% (0.05mol/L) was obtained by using a copper electrode, interelectrode-membrane distance of 0.36, and membrane:electrode surface area ratio of 0.23. The 2-parameter Langmuir, Freundlich, and Temkin isotherm models were used for the prediction of the lactose isomerization kinetics as well as the 3-parameter Langmuir-Freundlich isotherm model. It was shown that the lactose isomerization kinetics into lactulose followed the Temkin and Langmuir-Freundlich models with coefficients of determination of 0.99 and 0.90 and a relative error of 1.42 to 1.56% and 4.27 to 4.37%, respectively.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".