Impact of glucose concentration on productivity and yield of hydrogen production by the new isolate <i>Clostridium beijerinckii</i> Br21
Bibliographic record
Abstract
Abstract This paper presents a detailed analysis of the batch kinetic fermentative assays data used to evaluate how glucose concentration influences fermentative H2 production, including the non‐dissociated acid concentration threshold, with the new isolate Clostridium beijerinckii Br21. Fermentative batch assays for hydrogen (H2) production were conducted with initial glucose concentrations of 5.2, 27.2, 54.4, 97.2, or 154.4 mmol/L without pH control. The dry cell mass (X), glucose (S), and product (H2) concentrations and the pH were monitored for 60 h. Increasing initial glucose concentration raised the maximum specific H2 production rate to values as high as 1.19 mmol H2/mg X · h. However, the H2 production yield (YH2/S) decreased from 2.23 to 0.2 mmol H2/mmol glucose. The initial glucose concentration that provided the best compromise between YH2/S and H2 productivity was 37 mmol/L (6.7 g/L). The lactic acid, acetic acid, and butyric acid concentrations enhanced at higher initial glucose concentrations, leading to the largest pH decrease. Lower pH favoured non‐dissociated acids, which switched the Clostridium metabolism to solventogenesis. The threshold acid concentrations for C. beijerinckii Br21 solventogenesis were non‐dissociated butyric acid at 8 mmol/L and total non‐dissociated acetic and butyric acids at 13 mmol/L. Glucose supply and pH control in continuous or fed‐batch biorreactors could culminate in higher H2 productivity and yield by C. beijerinckii Br21.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".