Effect of some environmental parameters on fermentative hydrogen production by <i>Enterobacter cloacae</i> DM11
Bibliographic record
Abstract
Fermentative hydrogen production was carried out by Enterobacter cloacae DM11, using glucose as the substrate. The effects of initial substrate concentration, initial medium pH, and temperature were investigated. Results showed that at an initial glucose concentration of 1.0% (m/v), the molar yield of hydrogen was 3.31 mol (mol glucose)(-1). However, at higher initial glucose concentration, both the rate and cumulative volume of hydrogen production decreased. The pH of 6.5 +/- 0.2 at a temperature of 37 degrees C was found most suitable with respect to maximum rate of production of hydrogen in batch fermentation. Activation enthalpies of fermentation and that of thermal deactivation of the present process were estimated following a modified Arrhenius equation. The values were 47.34 and 118.67 kJ mol(-1) K(-1), respectively. The effect of the addition of Fe(2+) on hydrogen production was also studied. It revealed that the presence of iron (Fe(2+)) in the media up to a concentration of 20 mg L(-1) had a marginal enhancing effect on total hydrogen production. A simple model developed from the modified Gompertz equation was applied to estimate the hydrogen production potential, production rate, and lag-phase time in a batch process, based on the cumulative hydrogen production curves, using the software program Curve Expert 1.3.
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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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".