Microbial Electrolysis Cells for H2 Generation by Treating Acid Mine Drainage: Recent Advances and Emerging Trends
Bibliographic record
Abstract
Microbial electrolysis cells (MECs) are receiving increasing scholarly recognition for their capacity to simultaneously remediate contaminated streams and generate renewable hydrogen. Within the realm of acid mine drainage (AMD) treatment, MECs demonstrate pronounced advantages by merging pollutant mitigation with hydrogen production, thereby attracting intensified research interest. Drawing on 1321 pertinent publications extracted from the Web of Science Core Collection (2004–2024), this bibliometric assessment systematically elucidates the current research landscape and prospective directions in MEC-based AMD remediation and H2 synthesis. Key thematic areas encompass (1) a detailed appraisal of distinctive publication dynamics within this specialized domain; (2) insights into the principal contributing nations, institutions, journals, and academic fields; and (3) a synthesized overview of technological milestones, emerging investigative foci, and prospective developmental pathways. By critically reviewing extant knowledge, this evaluation offers meaningful guidance to researchers newly engaging with MEC-driven AMD treatment while illuminating the technological trajectories poised to shape the future of this evolving field.
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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.048 | 0.110 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".