Diverse Microbial Communities in Microbial Fuel Cells with Sugar Beet Residue as Substrate
Bibliographic record
Abstract
[Objective] To analyze diverse microbial communities in microbial fuel cells with sugar beet residue as substrate. [Method]With sugar beet residue under different processing ways as substrate,MFC as research device,the electricity production was investigated. Using 454 pyrosequencing,differences in the community structure were analyzed,the feasibility and stability was discussed. [Result] The research found that sugar beet residue with the different treatment as the substrates influence on the electricity generation of MFC. The voltage produced by MFCs with the raw and alkaline treatment was higher than the reactor by the acid treatment. We investigated the microbial community structures in anode biofilms of MFCs. By analyzing pyrosequencing sequences from the bacterial 16 S rRNA gene,we demonstrated that the microbial communities were classified into 14 phyla from three anode biofilm. Hierarchical cluster analyses indicated a clear distinction among three substrates in microbial community structure. Diverse acidogenic bacteria and exoelectrogens were detected in MFCs,and there was syntrophic relationship among bacteria. Diverse acid-producing bacteria can ferment soluble sugar or cellulose to produce organic acids. The exoelectrogens can use them as the electron donor to directly produce the current. [Conclusion] The results provide evidence that sugar beet residue with the different treatments can led to the diversity difference of the anode community. And the structure of the anode community can affect electrical output of MFC.
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.
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) | 0.000 | 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".