Production and Formulation of <i>Alcanivorax borkumensis SK2</i> Cell Powders for Marine Oil Spill Bioremediation
Bibliographic record
Abstract
Oil spills pose severe threats to marine ecosystems and coastal communities. Alcanivorax borkumensis SK2, a marine bacterium with superior hydrocarbon-degrading capabilities, emerges as a promising agent for bioremediation. This study identified an economical growth substrate for A. borkumensis SK2 and led to highly viable cell powder formulations for effective applications in contaminated marine environments. Various non-hydrocarbon substrates were evaluated to replace the costly sodium pyruvate, revealing that canola oil and sunflower oil gave biomass levels (optical density) four times higher than sodium pyruvate (20 ± 2 and 20 ± 1, compared to 4.6 ± 0.4, respectively). Freeze-drying and spray-drying approaches were investigated to produce a viable cell formulation. Two screening campaigns of potential freeze-drying cryoprotectants showed that the proprietary blend of Proventus Bioscience Inc. (Proventus) and 0.5 M glutamate ensured the highest viability, with 2 ± 1×10¹⁰ and 1.1 ± 0.3 × 10¹⁰ CFU/g, after the first screening, and 1.0 ± 0.5 × 10¹⁰ and 6 ± 2 × 10⁹ CFU/g after the second screening. Differential scanning calorimetry (DSC) analysis demonstrated a 9%-15% reduction in ice formation with cooling rates from 5 to 10°C/min. Glutamate reduced ice formation by 5%-9% compared to Proventus' solution. To promote cell viability during A. borkumensis SK2 freezing and freeze-drying, the best product temperatures were determined to be -65°C with 0.5 M glutamate and -59°C with Proventus' blend. Spray-drying resulted in cell powders with a viability up to 1.0 ± 0.7 × 10⁵ CFU/g, considerably lower than the levels obtained by freeze-drying, indicating some potential but also the need for further research and optimization.
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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) | 0.001 | 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".