Bacterial cellulose cookbook: A systematic review on sustainable and cost-effective substrates
Bibliographic record
Abstract
Bacterial cellulose is a versatile material with applications in many industries. However, the widespread uptake of bacterial cellulose faces challenges including high production costs and lack of scalability. One approach to address these obstacles is the use of alternative substrates and media, compared to the Hestrin-Schramm (HS) media. By evaluating and selecting appropriate media and substrates, the production of bacterial cellulose can be more efficient: enabling sustainable systems and supply chains where less energy and materials are lost, and the output production is increased. The purpose of this paper is to analyze the current landscape of bacterial cellulose alternative media and substrates (ingredients). Through a systematic review of 198 papers, this review identifies 299 alternative substrates from 12 industries and 101 bacterial cellulose-producing strains, which were systematically compared. This review also finds that there are methodological gaps in this field such as data variability, papers mislabelling the HS media or not using a comparison media, and a lack of strain names. This alternative substrate analysis for bacterial cellulose production demonstrates that overall, for some applications alternative substrates can be taken into consideration that are not only cheaper, but also produce higher yields than HS media.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".