A review on the application of nanomaterials in improving microbial fuel cells
Bibliographic record
Abstract
Materials at the nanoscale show exciting and different properties. In this review, the applications of nanomaterials for modifying the main components of microbial fuel cell (MFC) systems (i.e., electrodes and membranes) and their effect on cell performance are reviewed and critically discussed. Carbon and metal-based nanoparticles and conductive polymers could contribute to the growth of thick anodic and cathodic microbial biofilms, leading to enhanced electron transfer between the electrodes and the biofilm. Extending active surface area, increasing conductivity, and biocompatibility are among the significant attributes of promising nanomaterials used in MFC modifications. The application of nanomaterials in fabricating cathode catalysts (catalyzing oxygen reduction reaction) is also reviewed herein. Among the various nanocatalysts used on the cathode side, metal-based nanocatalysts such as metal oxides and metal-organic frameworks (MOFs) are regarded as inexpensive and high-performance alternatives to the conventionally used high-cost Pt. In addition, polymeric membranes modified with hydrophilic and antibacterial nanoparticles could lead to higher proton conductivity and mitigated biofouling compared to the conventionally used and expensive Nafion. These improvements could lead to more promising cell performance in power generation, wastewater treatment, and nanobiosensing. Future research efforts should also take into account decreasing the production cost of the nanomaterials and the environmental safety aspects of these compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".