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Record W2003713913 · doi:10.1139/w06-005

Effect of some environmental parameters on fermentative hydrogen production by <i>Enterobacter cloacae</i> DM11

2006· article· en· W2003713913 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueCanadian Journal of Microbiology · 2006
Typearticle
Languageen
FieldEngineering
TopicAnaerobic Digestion and Biogas Production
Canadian institutionsnot available
Fundersnot available
KeywordsEnterobacter cloacaeHydrogen productionFermentative hydrogen productionFermentationArrhenius equationChemistryHydrogenGompertz functionYield (engineering)Substrate (aquarium)Nuclear chemistryFood scienceBiohydrogenBiochemistryMaterials scienceActivation energyOrganic chemistryBiologyMathematicsEcology

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.003
GPT teacher head0.163
Teacher spread0.161 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it