Production of Green Biocellulose Nanofibers Through Utilizing Agricultural Wastes
Bibliographic record
Abstract
In the present study, the green Biocellulose Nanofibers (BC), a vitally emerging biomaterial, was produced by fermentation of wheat straw (WS), as a widely available agricultural waste, using. Two different fermentation methods were used; Separate Hydrolysis and Fermentation (SHF), and Simultaneous Saccharification and Fermentation (SSF). Different acidic and enzymatic WS pretreatment conditions were used to understand the effect of pretreatment conditions on BC production. Afterward, sugar hydrolsates were simultaneously or separately inoculated with Gluconacetobacter Xylinum bacterium (i.e., for SSF and SHF, respectively), at optimum production conditions in shake flasks for 7 days to produce the biocellulose nanofibers. BC productions of 9.7 g/L in SHF and 10.8 g/L in SSF were achieved when WS was pretreated with dilute acids. Enzymatic treatment of WS after acidic pretreatment increased sugars’ concentrations from the hydrolysis, which increased BC production in SHF to 10.6 g/L. However, enzymes in SSF broke cellulose I alpha linkage in BC and decreased its production compared to no enzymatic treatment. Results show that glucose extracted from WS (~55% of total sugars) was found essential for the cellular metabolism, while xylose (~28% of total sugars) was highly consumed during cells growth phase. Generally, increasing thermal treatment, time and temperature, resulted in increasing furfural concentration. This observed to inhibits bacterial cells growth and leads to lower nanofibers yield when exists at concentration higher than 1 g/L threshold. In general, results obtained in the present study demonstrate the ability of utilizing agricultural wastes in the fermentation production of BC. Such a step is expected to eliminate cost of expensive pure sugars as a carbon source in the fermentation. Also the study shows an improved production yield by using effective fermentation techniques as SSF compared to classical methods used in literature.
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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".