Unlocking the Potential of Biosurfactants in Agriculture: Novel Applications and Future Directions
Bibliographic record
Abstract
With rising environmental concerns and the urgent need for sustainable agricultural practices, biosurfactants have garnered significant attention. These naturally occurring, surface-active compounds produced by microorganisms offer eco-friendly alternatives to synthetic chemicals. This review explores the multifaceted role of biosurfactants in agriculture, highlighting their applications in soil nutrient enhancement, plant growth promotion, pest and pathogen control, and bioremediation. The inherent versatility and biodegradability of biosurfactants position them as pivotal agents in improving soil health and advancing sustainable farming. Cutting-edge biotechnological approaches, such as synthetic biology and metabolic engineering, are critical for optimizing biosurfactant production. Integrating these bioactive molecules into smart agricultural systems promises to enhance resource utilization and crop management. Despite challenges like high production costs and limited ecological impact studies, innovative production techniques and comprehensive ecological assessments are essential for broader applications. This review underscores the transformative potential of biosurfactants in driving sustainable agricultural practices and environmental remediation, paving the way for future research and innovation in this field.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".