Advancement of biosurfactant production and biosurfactant-aided pollution remediation
Bibliographic record
Abstract
Biosurfactant enhanced soil washing and/or bioremediation have been proven as promising technologies for cleaning up petroleum hydrocarbon contaminants (PHCs)- and heavy metals- contaminated soil and groundwater. As environmentally friendly amphiphiles, biosurfactants display promising wetting, solubilization, and emulsification properties. Biosurfactant addition can enhance the mobility and bioavailability of entrapped PHCs in porous media, and finally improve their removal. Biosurfactants can also reduce the heavy metal toxicity and assist their removal through acting as metal complexing agents. The availability of economic biosurfactants, however, has become a major obstacle to their applications. In addition, little research has been conducted to investigate the role of biosurfactants, especially lipopeptides, in contaminated subsurface cleanup process and their impacts on oil degrading microbes. To fill the knowledge gaps, a number of methodologies and mechanisms aimed at economical biosurfactant production and advanced biosurfactant enhanced subsurface co-contamination control have been investigated. Economical lipopeptide production by Bacillus Substilis N3-1P using fish waste as an unconventional medium was achieved. The lipopeptide production was further enhanced using immobilized robust biocatalysts on porous fly ash by Bacillus Substilis N3-1P, and the associated mechanisms were explored. The lipopeptide production by Bacillus Substilis N3-4P was optimized and its application for crude oil removal was examined. The impact of the generated biosurfactant on the biodegradation of PHCs in presence of heavy metals was finally evaluated. The newly developed lipopeptide production methodologies and the associated mechanisms helped to break down the barriers impeding economical biosurfactant production. The research outcomes (e.g., fish-waste-based hydrolysate, fly ash (FA) - based robust biocatalyst and optimized growth medium) could contribute to a cost-efficient biosurfactant production through proper selection of waste materials, advanced bioreactor design and medium optimization. This dissertation research was also a first attempt to identify the role of lipopeptides in cell surface associated biodegradation mechanisms in a co-contaminated environment. This research could help implement effective soil and groundwater remediation practices and bring short/long-term benefits to the governments, industries and communities at regional, national and international levels.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".