Investigation of Desulfurization Activity, Reusability, and Viability of Magnetite Coated Bacterial Cells
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Background: Magnetic separation using magnetic nanoparticles can be used as a simple method to isolate desulfurizing bacteria from a biphasic oil/water system. Objectives: Magnetite nanoparticles were applied to coat the surface of Rhodococcus erythropolis IGTS8 and Rhodococcus erythropolis FMF desulfurizing bacterial cells, and the viability and reusability of magnetite-coated bacteria evaluated by using various methods. Material and Methods: Magnetite nanoparticles were synthesized through a reverse co-precipitation method. Glycine was added during and after the synthesis of magnetite nanoparticles to modify their surface and to stabilize the dispersion of the nanoparticles. The glycine-modified magnetite nanoparticles were immobilized on the surface of both oil-desulfurizing bacterial strains. Reusability of magnetite-coated bacterial cells was evaluated via assessing the desulfurization activity of bacteria via spectrophotometry using Gibb's assay, after the separation of bacterial cells from 96h-cultures with the application of external magnetic field. In addition, CFU and fluorescence imaging were used to investigate the viability of magnetite-coated and free bacterial cells. Results: TEM micrographs showed that magnetite nanoparticles have the size approximately 5.35±1.13 nm. Reusability results showed that both magnetite-coated bacterial strains maintain their activity even after 5 × 96h-cycles. The viability results revealed glycine-modified magnetite nanoparticles did not negatively affect the viability of two bacterial strains R. erythropolis IGTS8 and R. erythropolis FMF. Conclusions: In conclusion, the glycine-modified magnetite nanoparticles have great capacity for immobilization and separation of desulfurizing bacteria from suspension.
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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 it