Bibliographic record
Abstract
Clostridium phytofermentans, a newly isolated mesophilic anaerobic bacterium from forest soil, has received considerable attention for its potential application in producing ethanol directly from cellulose. This microorganism produces ethanol, acetate, CO₂ and H₂ as major metabolites from cellulose. Potential applications of this research include the transformation of waste materials into valuable products, such as fuels and organic acids. As an initial part of a multi-staged project, this study is to focus on the characerization of this microorganism growth and to verify the bacterium kinetics, including biomass growth, substrate utilization, and gas production. A series of batch fermentation experiments using cellulose substrate (GS-2C) was performed under the incubation temperature of 37°C. To investigate the effects of pH and substrate concentration (S₀) on growth, 12 trial experiments were conducted with various controlled pH values (7.0 to 8.5) and with various initial cellulose concentration settings (0.1 to 6.0 g/L). Our experimental results showed that the optimal growth condition for C. phytofermentans in batch culture was at pH = 8.4 amd S₀ = 6.0 g/L. Under such condition, the maximum growth rate of 0.37h⁻¹ was observed. Comparing results with other celluloytic clostridium studies, relatively high biomass growth rate using C. phytofermentans is confirmed by our experiments. Mathematical models, using a combination modelling approach with the logistic equation. Monod model, and Luedeking-Piret model, were developed for biomass growth, substrate degradation, and biogas production, respectively, base on our experiment results. This study demonstrated the determination of the four parameters (µmax, Ks, Y, and Smin), which can describe satisfactorily growth or degradation phenomena, using the proposed integration modelling approach. The experiments conducted under wide range conditions, such as changing pH and S₀, not only provide insight into growth kinetics but also provide an opportunity to evaluate the performance of the mathematical models and understand their limitations. This leads to look for improvement or modification to the models. It is foreseen that the findings in this study will enhance the overall understanding of the kinetics of growth and substrate utilization and product formation of this bacterium, and provide important information on the design of the bench-scale anaerobic bioreactor for future studies.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".