Acidic and thermal pre-treatments for anaerobic digestion inoculum to improve hydrogen and volatile fatty acid production using xylose as the substrate
Bibliographic record
Abstract
Xylose is a by-product of lignocellulosic biomass processing for production of second-generation biofuels and could be suitable for bioproduct manufacturing. This paper describes an innovative approach that enables the system to achieve high yielding for hydrogen production. The study compared 4 physicochemical pre-treatments performed in an anaerobic mixed culture (acidic, thermal, acidic-thermal and thermal acidic) to achieve an inoculum with a high-efficiency xylose to hydrogen conversion under mesophilic conditions (30 °C). The acidic pre-treatment was the most efficient to select microorganisms able to produce hydrogen and volatile acid from xylose. Kinetics has shown that acidic pre-treatment had a hydrogen/xylose molar yielding factor of 1.57 (molar base) and a hydrogen maximum production rate of 253 mL H2 h−1. Mass balance considered all possible metabolic pathways using xylose as a substrate. Anaerobic degradation of ethanol was the most active pathway for hydrogen production in all experiments, except for the control. Each pre-treatment performed for the original inoculum resulted in different microbiological profiles, but the genus Clostridium was the most abundant in all assays. Acidic pre-treatment stimulated the growth of organisms from the genera Peptostreptococcaceae, Truepera and Kurthia, which could be related to the better results in hydrogen production found in this condition.
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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) | 0.001 | 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 | 0.001 |
| 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".