Production and characterization of bacterial cellulose by Rhizobium sp. isolated from bean root
Bibliographic record
Abstract
Bacterial cellulose (BC) is a natural polymer renowned for its unique physicochemical and mechanical attributes, including notable water-holding capacity, crystallinity, and a pristine fiber network structure. While BC has broad applications spanning agriculture, industry, and medicine, its industrial utilization is hindered by production costs and yield limitations. In this study, Rhizobium sp. was isolated from bean roots and systematically assessed for BC synthesis under optimal conditions, with a comparative analysis against BC produced by Komagataeibacter hansenii. The study revealed that Rhizobium sp. exhibited optimal BC synthesis when supplied with a 1.5% glucose carbon source and a 0.15% yeast extract nitrogen source. Under static conditions at 30 °C and pH 6.5, the most favorable conditions for growth and BC production (2.5 g/L) were identified. Modifications were introduced using nisin to enhance BC properties, and the resulting BC-nisin composites were comprehensively characterized through various techniques, including FE-SEM, FTIR, porosity, swelling, filtration, and antibacterial activity assessments. The results demonstrated that BC produced by Rhizobium sp. displayed properties comparable to K. hansenii-produced BC. Furthermore, the BC-nisin composites exhibited remarkable inhibitory activity against Escherichia coli and Pseudomonas aeruginosa. This study contributes valuable insights into BC's production, modification, and characterization utilizing Rhizobium sp., highlighting the exceptional properties that render it efficacious across diverse applications.
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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) | 0.000 | 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".