Synthesizing developments in the usage of solid organic matter in microbial fuel cells: A review
Bibliographic record
Abstract
• Solid organic matter substrates usage in MFCs (SOM-MFCs) is emerging due to their practical advantages. • Different aspects of SOM-MFCs research are highlighted through in-depth bibliometric and trend analyses. • Factors for optimal performance in MFCs are highlighted. • Research gaps and areas for future research are synthesized. Microbial Fuel Cells (MFCs) are a prominent feature in renewable and sustainability literature due to their wide range of potential uses. MFCs have found applications in power production, biosensors, and environmental remediation to mention a few. Importantly, however, one of the factors affecting the transition from laboratory to practical usage is the requirement of ensuring that there is enough organic matter supply to sustain microbial activity. To reduce this energy-intensive and human intervention-dependent requirement, there has been a shift towards solid carbon sources in recent times. These solid carbon sources enable the operation of MFCs autonomously for a long time through the slow release or replenishment of organic matter, characteristics of solids. Despite the advantages of solid organic matter substrates, significant progress is not being made due to the uncoordinated and piece-meal information scattered across the existing body of literature. In this work, the substrate categories, electrode materials, reactor configurations, and applications of solid organic matter-based MFCs (SOM-MFCs) have been reviewed comprehensively. We found that although there are a lot of work focused on advancing different aspects or application, one major problem is a lack of contextualization or normalization of results for better planning of future work. Importantly, the present review normalizes and compares the results of different studies using SOM as a substrate in MFCs. Major studies within the identified aspects or application are highlighted while focusing on trends and limitations. Furthermore, to enhance the development of future studies, recommendations for the best approach in future work were made.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".