Biodegradation of gasoline by gellan gum‐encapsulated bacterial cells
Bibliographic record
Abstract
Encapsulated cell bioaugmentation is a novel alternative solution to in situ bioremediation of contaminated aquifers. This study was conducted to evaluate the feasibility of such a remediation strategy based on the performance of encapsulated cells in the biodegradation of gasoline, a major groundwater contaminant. An enriched bacterial consortium, isolated from a gasoline-polluted site, was encapsulated in gellan gum microbeads (16-53 microm diameter). The capacity of the encapsulated cells to degrade gasoline under aerobic conditions was evaluated in comparison with free (non-encapsulated) cells. Encapsulated cells (2.6 mg(cells) x g(-1) bead) degraded over 90% gasoline hydrocarbons (initial concentration 50-600 mg x L(-1)) within 5-10 days at 10 degrees C. Equivalent levels of free cells removed comparable amounts of gasoline (initial concentration 50-400 mg x L(-1)) within the same period but required up to 30 days to degrade the highest level of gasoline tested (600 mg x L(-1)). Free cells exhibited a lag phase in biodegradation, which increased from 1 to 5 days with an increase in gasoline concentration (200-600 x mg L(-1)). Encapsulation provided cells with a protective barrier against toxic hydrocarbons, eliminating the adaptation period required by free cells. The reduction of encapsulated cell mass loading from 2.6 to 1.0 mg(cells) x g(-1) bead caused a substantial decrease in the extent of biodegradation within a 30-day incubation period. Encapsulated cells dispersed within the porous soil matrix of saturated soil microcosms demonstrated a reduced performance in the removal of gasoline (initial concentrations of 400 and 600 mg x L(-1)), removing 30-50% gasoline hydrocarbons compared to 40-60% by free cells within 21 days of incubation. The results of this study suggest that gellan gum-encapsulated bacterial cells have the potential to be used for biodegradation of gasoline hydrocarbons in aqueous systems.
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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".