Chemocatalytic Oxidation of Lactose to Lactobionic Acid over Pd−Bi/SBA-15: Reaction Kinetics and Modeling
Bibliographic record
Abstract
Lactobionic acid (LBA) was synthesized from the direct aerobic oxidation of lactose under a very low O 2 concentration with high conversion (96%) and 100% selectivity over low loadings (1.02−0.64%) of bimetallic Pd−Bi supported on mesoporous SBA-15 silica material. Under alkaline conditions (pH 9), the catalyst exhibited enhanced activity and stability with unprecedented complete selectivity toward LBA formation. Furthermore, it exhibited pretty good stability toward metal leaching. It was observed that, with a selective deposition of bismuth on palladium, as well as adequate alkaline pH processing, the redox reaction chain performed efficiently and maintained the continuous dehydrogenation of lactose, while avoiding poisoning of the Pd−Bi bimetallic catalyst. Using the Langmuir−Hinshelwood−Hougen−Watson approach, a kinetic model was developed to predict the fates of the lactose and LBA. The rate equation of lactose consumption contains Langmuir adsorption terms, which accounts for the competitive reversible dissociative chemisorption of oxygen and for the reversible associative adsorption of lactose. The kinetic model was verified by comparing the experimental results with those foreseen in the simulation for different experimental conditions. The assessment of the determined Arrhenius parameters led to physically meaningful estimates of activation energy. The Langmuir adsorption isotherms are physically meaningful, where the standard entropy and enthalpy of adsorption were shown to fulfill established guidelines, which assesses the physical sense of their values.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".