Impact of chemical stressors on hydrogen metabolism
Bibliographic record
Abstract
The effects of chemical stressing agents on H 2 metabolism were evaluated using thermodynamic, biochemical, genomic and statistical methods. The objectives of this study were to examine the role of homoacetogens and hydrogenotrophic methanogens exposed to different stress treatments under various fermentation conditions. Negligible H 2 consumption was observed at mesophilic and thermophilic temperature (at pH 4.5) when combined with the addition of 2 g L -1 linoleic acid (LA). Genomic analysis revealed that LA-treated cultures were dominated by Clostridium sp. whereas control cultures were dominated by homoacetogens and methanogens. Lauric acid (LUA), LA, fish oil and furfural affected H 2 consumption similarly to BES. The H 2 consumption (%) of the control and chemically treated cultures revealed the following trend: Control > Fish oil = LA = Furfural > BES > LUA. Treatment with different stressing agents also resulted in the formation of diverse fermentation metabolites. The long-term effects of different culture pretreatments under mesophilic condition resulted in higher mean H 2 yields compared to the yields from cultures incubated at thermophilic condition (after 5 glucose additions). Hydrogen consumption studies using long term stress treated cultures showed lower consumption at thermophilic temperature than at mesophilic temperature. Uptake hydrogenase activities correlated positively with the H 2 consumption data. Genomic analysis indicated that both methanogens and homoacetogens were present in control cultures, but they were absent from the pretreated cultures. Studies conducted in anaerobic sequencing batch reactors revealed that lowering the HRT from 37.5 h to 7.5 h reduced the methane yield and increased the H 2 yield. Higher H 2 yields were obtained in cultures operated at thermophilic temperature compared to mesophilic and psychrophilic temperature using corn stalk (CS) as substrate. Cultures fed CS liquor showed lower levels of specific methanogenic activities than cultures fed pure sugars. The results from these studies indicate that all the chemical stressing agents investigated were active against H 2 consumers (methanogens in particular). In addition to different stress treatments, proper control of operational parameters such as pH, HRT and temperature is required to minimize H 2 consumption and maximize H 2 production in dark fermentation process.
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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) | 0.000 | 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 | 0.000 |
| 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".