Improvements in the production of bacterial synthesized biocellulose nanofibres using different culture methods
Bibliographic record
Abstract
Abstract This review summarizes previous work that was done to improve the production of bacterial cellulose nanofibres. Production of biocellulose nanofibres is a subject of interest owing to the wide range of unique properties that makes this product an attractive material for many applications. Bacterial cellulose is a natural nanomaterial that has a native dimension of less than 50 nm in diameter. It is produced in the form of nanofibres, yielding a very pure cellulose product with unique physical properties that distinguish it from plant‐derived cellulose. Its high surface‐to‐volume ratio combined with its unique properties such as poly‐functionality, hydrophilicity and biocompatibility makes it a potential material for applications in the biomedical field. The purpose of this review is to summarize the methods that might help in delivering microbial cellulose to the market at a competitive cost. Different feedstocks in addition to different bioreactor systems that have been previously used are reviewed. The main challenge that exists is the low yield of the cellulosic nanofibres, which can be produced in static and agitated cultures. The static culture method has been used for many years. However, the production cost of this nanomaterial in bioreactor systems is less expensive than the static culture method. Biosynthesis in bioreactors will also be less labour intensive when scaled up. This would improve developing intermediate fermentation scale‐up so that the conversion to an efficient large‐scale fermentation technology will be an easy task. Copyright © 2009 Society of Chemical Industry
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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 | 0.001 | 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".