Efficient Immobilization of <i>Streptomyces gobitricini</i> Lipase for Sustainable Lipid Degradation and Wastewater Bioremediation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide Immobilized lipases are emerging as highly effective biocatalysts due to their enhanced operational stability, reusability, and promising environmental applications, particularly in the bioremediation of lipid-contaminated wastewater. In this study, the lipase from Streptomyces gobitricini (Lip S.g ) was immobilized on various supports, with calcium carbonate demonstrating the highest immobilization efficiency (82.66 ± 5.5%). Optimal conditions were achieved using a lipase concentration of 3500 U·g –1 support, resulting in a retained activity of 92.67 ± 3.05%. The immobilized Lip S.g showed significantly improved storage stability, maintaining 68.33% of its initial activity after 120 days at 4 °C, compared to only 29.7% for the free enzyme. It also exhibited greater tolerance to alkaline pH and high temperatures, with maximum activity at pH 9.0 and thermal stability up to 70 °C. Substrate specificity tests on oil-based substrates revealed improved catalytic performance in the immobilized form, likely due to enhanced substrate accessibility. In practical wastewater treatment trials, the immobilized enzyme achieved complete lipid removal by day 9, in contrast to the free enzyme, which achieved only 50% removal. Moreover, marked reductions in chemical oxygen demand and residual lipid levels further validated its bioremediation efficacy. These results position immobilized Lip S.g as a robust, eco-friendly biocatalyst with strong potential for industrial applications in the treatment of oil-laden wastewater.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| 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 teacher head, 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".