Production of green bacterial cellulose nanofibers by utilizing renewable resources of algae in comparison with agricultural residue
Bibliographic record
Abstract
Bacterial Cellulose (BC) was synthesized through utilizing algae as a sustainable and renewable carbon source in comparison with agriculture residues (i.e., Wheat Straws (WS)). BC was produced in separate hydrolysis and fermentation method (SHF) using Gluconacetobacter xylinum (G.xylinum). Results for the individual and total sugars were analyzed in comparison with corresponding results from WS hydrolysis. Results show that highest total sugars content was obtained with algae samples that were hydrolyzed using enzymes (Cellulase, β-glycosidase, and Xylanase) and produced 27.58 g/L. Similarly, WS hydrolysis under same conditions produced 52.12 g/L. The lowest total sugars production was obtained with algae sample that was hydrolyzed using 1% of acid at 121°C. Produced sugars were utilized in SHF to produce BC, with highest production of 4.86 g/L BC was achieved with algae sample that went through enzymatic hydrolysis. The equivalent production that was obtained from WS hydrolysis was 10.6 g/L Results obtained from individual sugars indicated that among all individual sugars glucose was maximum consumed i.e. 80-85%of glucose sugar was consumed where the lowest was arabinose which was only 50% consumed during fermentation. The lower production of BC using algae compared to WS (approximately half) as algae we used was unprocessed means it had oil content in it. About 30-60% of algae dry weight was utilized for production of oil and rest amount of feedstock was only used for hydrolysis and fermentation.
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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".