Strategies for Increasing the Biohydrogen Yield in Anaerobic Fermentation of Xylose
Bibliographic record
Abstract
The pretreatment of lignocellulosic materials to obtain cellulose generates a residual stream with hemicellulosic composition, mainly containing xylose. This C5 fraction is not directly fermentable by microorganisms traditionally used to produce ethanol. Hence, more promising alternatives for the C5 fraction have been studied, and acidogenic fermentation proves to be an attractive option for the production of biohydrogen, due to the possibility of using hemicellulose fractions and mixed anaerobic cultures. To reduce the activity of hydrogen-consuming microorganisms when mixed cultures are employed as inoculum to produce hydrogen by anaerobic fermentation, thermal pretreatment was selected. However, such pretreatment method also affects the activity of hydrogen-producing acidogenic bacteria, and strategies should be studied to enrich the inoculum for these bacteria and to increase hydrogen yields. Thus, this study evaluated the effect of some strategies on the biohydrogen production from xylose. The strategies adopted were thermal pretreatment of the sludge, maintenance of the incubation temperature at 35 °C, adaptation of the sludge by successive contacts with the xylose solution, and increasing inoculum to substrate ratio (I/S) from 1 to 2. This approach improved hydrogen yield approximately 30 times, from 0.03 to 0.93 mmol H2/mmol xylose. However, this yield was only 56% of the theoretical value and can still be improved.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".