Innovative method for improving fermentability of hemicellulosic hydrolysate by ozonation for sustainable xylitol production
Why this work is in the frame
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Bibliographic record
Abstract
Biomanufacturing xylitol, a sugar alcohol from surplus rice straw (RS) offers an opportunity for its green production alongside fostering a sustainable bioeconomy. However, RS pretreatment for hemicellulose extraction (majorly xylose), creates several toxic compounds as side products that impede microbial metabolism. So, the yeast mediated production of xylitol is a big challenge due to low fermentability of RS hydrolysate. The present work explored the efficacy of ozone and hydrogen peroxide, O 3 -H 2 O 2 (O-HP), in integration with activated charcoal (AC) for detoxifying and decolorizing RS hydrolysate. The effect on fermentability of RS hydrolysate was studied with the yeast, M. caribbica CP02 for xylitol production. O-HP/AC process was able to remove ∼88.86 % of the aromatics (furan and phenolic compounds) and color (ΔE = 46.2) without substantial xylose loss, which is a key drawback of conventional detoxification treatments. In 3 L batch bioreactor study, O-HP treated hydrolysate produced 43.14 ± 0.84 gL −1 xylitol titre and 0.74 gg −1 yield after 72 h of fermentation, comparable to semi-synthetic medium (44.6 ± 0.70 gL −1 , 0.77 gg −1 ). Simultaneously, a xylitol yield of 0.69 gg −1 and a productivity of 0.56 gL −1 h −1 at 72 h was observed in a 14 L bioreactor. Conclusively, the O-HP process is a promising intervention that can be used as a significant step during detoxification/decolorization for bioprocessing of xylitol. • Significant reduction in total phenols, furans, and color with O 3 -H 2 O 2 process. • No significant loss in xylose level in O-HP treated hydrolysate as compared to control. • Improved fermentability and xylitol yield of O 3 -H 2 O 2 treated hydrolysate. • CCD and ANN predicted responses for O 3 -H 2 O 2 process optimization. • Xylitol yield of 0.69 gg −1 and productivity of 0.56 gL −1 h −1 observed in 14 L bioreactor.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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 it