Study of support materials for sol-gel immobilized lipase
Bibliographic record
Abstract
A variety of support materials for sol-gel immobilized lipase were considered based on their ability to provide superior sol-gel adhesion, load protein, and synthesize methyl oleate. A standard approach was developed to formulate the supported lipase sol-gels and to allow comparison of the resulting hybrid materials. These supported sol-gels are proposed as an alternative immobilization regime to overcome some challenges associated with enzymatic biodiesel production such as enzyme stability and cost. The support materials considered were 6–12 mesh silica gel, Celite® R633, Celite® R632, Celite® R647, anion-exchange resin AG3-X4, and Quartzel® felt. Each support material exhibited unique properties that would be beneficial for this application including: Quartzel® felt had the highest initial sol-gel capacity (62.5 mL/g) and sol-gel adhesion (1100 mg sol-gel/g material); silica gel had the most uniform coating of deposited sol-gel; the anion-exchange resin AG3-X4 supported sol-gel had the highest protein loading (1060 μg lipase/g) and reaction rate [1.25 mM/(min g-material)]; the Celite® support series were the most thermally stable and had the lowest water content; and the Celite® R632 supported lipase sol-gels had the highest 6 h biodiesel conversion per gram of supported material (68%) and enzymatic activity [9.4 mmol/(min g-lipase)]. The supported sol-gels with the highest enzymatic properties (conversion, activity, and reaction rate) were those supported on Celite® R632, anion-exchange resin AG3-X4, and Quartzel®. These supported sol-gels had superior performance in comparison with the unsupported sol-gels. Based on this study, the lipase sol-gel support material with the most potential for biodiesel production is Celite® R632.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".