Microbial Fuel Cells for Power Generation by Treating Mine Tailings: Recent Advances and Emerging Trends
Bibliographic record
Abstract
Microbial fuel cells (MFCs) have gained considerable attention in recent years due to their dual potential in waste treatment and clean energy production. In the field of mine tailings treatment, MFCs exhibit a unique advantage by integrating pollutant degradation with electricity generation, gradually emerging as a significant research focus. Based on 1321 relevant publications retrieved from the Web of Science Core Collection (WoSCC) from 2004 to 2024, this study employs bibliometric analysis to systematically explore the research status and future trends of MFCs in mine tailings treatment and power generation. The main research themes include (1) distinctive publication characteristics of MFC studies in the context of mine tailings treatment; (2) key information on leading countries, institutions, journals, and disciplines contributing to this field; and (3) a comprehensive summary of technological breakthroughs, emerging research hotspots, and future development directions of MFCs in mine tailings management. By thoroughly evaluating the existing body of research, this study provides valuable guidance for scholars new to the fields of MFCs and mine tailings treatment while offering insights into the technological advancements shaping the future of this domain.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.024 | 0.054 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".