Isolation and Identification of Mercury-Tolerant Bacteria LBA119 from Molybdenum-Lead Mining Soils and Their Removal of Hg2+
Bibliographic record
Abstract
Aims: To screen heavy metal-tolerant strains from heavy metal-contaminated soil in mining areas and determine the tolerance of the strains to different heavy metals and their removal rates through experiments. Methods: Mercury-resistant strain LBA119 was isolated from mercury-contaminated soil samples in Luanchuan County, Henan Province, China. The strain was identified by Gram staining, physiological and biochemical tests, and 16S rDNA sequences. The LBA119 strain showed good resistance and removal rates to heavy metals such as Pb2+, Hg2+, Mn2+, Zn2+, and Cd2+ using tolerance tests under optimal growth conditions. The mercury-resistant strain LBA119 was applied to mercury-contaminated soil to determine the ability of the strain to remove mercury from the soil compared to mercury-contaminated soil without bacterial biomass. Results: Mercury-resistant strain LBA119 is a Gram-positive bacterium that appears as a short rod under scanning electron microscopy, with a single bacterium measuring approximately 0.8 × 1.3 μm. The strain was identified as a Bacillus by Gram staining, physiological and biochemical tests, and 16S rDNA sequence analysis. The strain was highly resistant to mercury, with a minimum inhibitory concentration (MIC) of 32 mg/L for mercury. Under a 10 mg/L mercury environment, the optimal inoculation amount, pH, temperature, and salt concentration of the LBA119 strain were 2%, 7, 30 °C, and 20 g/L, respectively. In the 10 mg/L Hg2+ LB medium, the total removal rate, volatilization rate, and adsorption rate at 36 h were 97.32%, 89.08%, and 8.24%, respectively. According to tolerance tests, the strain showed good resistance to Pb2+, Mn2+, Zn2+, Cd2+, and other heavy metals. When the initial mercury concentration was 50 mg/L and 100 mg/L, compared with the mercury-contaminated soil that contained an LB medium without bacterial biomass, LBA119 inoculation increased 15.54–37.67% after 30 days of culture. Conclusion: This strain shows high bioremediation potential for mercury-contaminated soil.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".