Recent progress in microbial fuel cells using substrates from diverse sources
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.
Bibliographic record
Abstract
Increasing untreated environmental outputs from industry and the rising human population have increased the burden of wastewater and other waste streams on the environment. The most prevalent wastewater treatment methods include the activated sludge process, which requires aeration and is, therefore, energy and cost-intensive. The current trend towards a circular economy facilitates the recovery of waste materials as a resource. Along with the amount, the complexity of wastewater is increasing day by day. Therefore, wastewater treatment processes must be transformed into cost-effective and sustainable methods. Microbial fuel cells (MFCs) use electroactive microbes to extract chemical energy from waste organic molecules to generate electricity via waste treatment. This review focuses use of MFCs as an energy converter using wastewater from various sources. The different substrate sources that are evaluated include industrial, agricultural, domestic, and pharmaceutical types. The article also highlights the effect of operational parameters such as organic load, pH, current, and concentration on the MFC output. The article also covers MFC functioning with respect to the substrate, and the associated performance parameters, such as power generation and wastewater treatment matrices, are given. The review also illustrates the success stories of various MFC configurations. We emphasize the significant measures required to fill in the gaps related to the effect of substrate type on different MFC configurations, identification of microbes for use as biocatalysts, and development of biocathodes for the further improvement of the system. Finally, we shortlisted the best performing substrates based on the maximum current and power, Coulombic efficiency, and chemical oxygen demand removal upon the treatment of substrates in MFCs. This information will guide industries that wish to use MFC technology to treat generated effluent from various processes.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.016 | 0.001 |
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 it