Efficient Immobilization of <i>Streptomyces gobitricini</i> Lipase for Sustainable Lipid Degradation and Wastewater Bioremediation
Bibliographic record
Abstract
Abstract 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 (LipS.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 LipS.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 LipS.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 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) | 0.000 | 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 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".