Development of a Consolidated Anaerobic Digester and Microbial Fuel Cell to Produce Biomethane and Electricity from Cellulosic Biomass Using Bovine Rumen Microorganisms
Bibliographic record
Abstract
Microbial fuel cells (MFCs) are bioelectrochemical systems that convert chemical energy contained in organic matter into electrical energy by using the catalytic (metabolic) activity of living microorganisms. Mediator-less two chamber H-type MFCs were constructed in the current study, using dairy digester microbial population as anode inocula to convert finely ground pine tree (Avicel) at 2% (w/v) to electricity. MFCs were placed at 37°C and after the circuit voltage was stabilized on d9, bovine rumen microorganisms cultured anaerobically for 48 hrs in cellulose broth media were added to treatment group of MFC at 1% v/v dosage. MFC power and current across an external resistor were measured daily for 10 d. At the end of incubation on d19 head space gas and anode chamber liquid solutions were collected and analyzed for total gas volume and composition, and volatile fatty acids, respectively. Addition of enriched rumen microorganisms to anaerobic anode chamber increased cellulose digestibility and increased both CO2 and methane production; however, it decreased the methane to CO2 ratio. Over the experimental period, electricity generation was increased with rumen microorganism addition, and power density normalized to anode surface area was 17.6 to 67.2 mW/m2 with average of 36.0 mW/m2 in treatment, while control group had 3.6 to 21.6 (AVE 12.0) mW/m2. These observations imply that biocatalysis in MFCs requires additional cellulolytic activities to utilize structural biomass in bioenergy production.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".