Radiation tolerance and biodegradation performance of a marine bacterium <i>Acinetobacter</i> sp. Y9 in radioactive composite oil‐contaminated wastewater
Bibliographic record
Abstract
Abstract The tolerance and degradation characteristics of a marine oil‐degrading strain Acinetobacter sp. Y9 were investigated in the presence of diesel oil and simulated radioactive nuclides (Mn 2+ , Co 2+ , Ni 2+ , Sr 2+ , Cs + ) at varying concentrations, as well as exposure to γ‐ray radiation (Co‐60). The maximum tolerable concentrations for Co 2+ and Ni 2+ were found to be 5 mg/l and 25 mg/l, respectively, while the tolerable concentrations for Mn 2+ , Sr 2+ , and Cs + exceeded 400 mg/l, 1000 mg/l, and 1000 mg/l, respectively. A total of 0.4 Gy/h of γ‐ray radiation (Co‐60) did not significantly affect the growth of strain Y9. The presence of metal nuclides and γ‐ray radiation primarily inhibited the production of outer membrane proteins while promoting the secretion of polysaccharides in strain Y9. Strain Y9 exhibited a notable capacity to degrade diesel oil under radiative conditions when exposed to the five individual radionuclides used in this study. Furthermore, the introduction of the radiation‐resistant strain R1 significantly enhanced the diesel oil degradation efficiency of strain Y9 in the presence of a mixture of five nuclides, with the degradation efficiency increasing from 26.7% to 46.75%. Strain R1 demonstrated the ability to absorb a substantial amount of free nuclides, thereby creating favorable environmental conditions for the growth and degradation activity of strain Y9. Practitioner Points Investigate the tolerance mechanisms of strain Y9 to different nuclides and γ‐ray irradiation. Examine the degradation characteristics of strain Y9 on diesel oil under the influence of nuclides and irradiation. In a single nuclide medium, strain Y9 exhibited a high degradation rate of 90.64% toward 1% diesel oil concentration. The addition of a radiation‐resistant strain R1 can enhance the degradation efficiency of Y9 toward diesel oil.
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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".