Effect of konjac glucomannan aerogel‐immobilized <scp><i>Chlorella vulgaris</i></scp> LH‐1 on oil‐contaminated seawater remediation and endogenous bacterial community diversity
Bibliographic record
Abstract
Ocean oil spills can severely impact ecosystems and disrupt marine biodiversity and habitats. Microbial remediation is an effective method for removing thin oil slick contamination. In this study, the adsorption and degradation of low-concentration oil spills by Chlorella vulgaris LH-1 immobilized in konjac glucomannan (KGM) aerogel were investigated. The effect of the KGM aerogel-immobilized C. vulgaris on the bacterial community structure in seawater environments was analyzed through bacterial diversity sequencing. In seawater containing 0.01 and 1.00 g/L of crude oil, after 14 days of remediation with the KGM aerogel-immobilized C. vulgaris, crude oil removal rates of 98.73% and 95.13% were achieved, respectively. The FDA hydrolytic enzyme activity curve indicated that the microbial growth activity in the immobilized C. vulgaris group was significantly higher than that in other groups. After remediation, the top three dominant bacterial genera in the seawater were found to be Vitellibacter, Roseitalea, and Methylophaga. Vitellibacter, a genus known for its ability to degrade polycyclic aromatic hydrocarbons (PAHs) in marine environments, showed increased abundance in seawater treated with the KGM aerogel-immobilized C. vulgaris, suggesting enhanced PAH degradation capability in the presence of the immobilized C. vulgaris. Functional prediction using PICRUSt indicated that the oil metabolism capability of bacteria was promoted by the KGM aerogel-immobilized C. vulgaris. PRACTITIONER POINTS: High degradation efficiency across various oil concentrations is exhibited by KGM-immobilized microalgae. KGM aerogels effectively confine C.vulgaris, reducing loss in marine systems. The impact of KGM aerogel-immobilized C. vulgaris on bacterial community structure in marine environments was analyzed. Immobilized C. vulgaris enhanced the growth of polycyclic aromatic hydrocarbon-degrading bacteria, such as Vitellibacter, in seawater.
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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".