Challenges and opportunities for large-scale applications of the electro-Fenton process
Bibliographic record
Abstract
As an electrochemical advanced oxidation process, the electro-Fenton (EF) process has gained significant importance in the treatment of wastewater and persistent organic pollutants in recent years. As recently reported in a bibliometric analysis, the number of scientific publications on EF have increased exponentially since 2002, reaching nearly 500 articles published in 2022 (Deng et al., 2022). The influence of the main operating parameters has been thoroughly investigated for optimization purposes, such as type of electrode materials, reactor design, current density, and type and concentration of catalyst. Even though most of the studies have been conducted at a laboratory scale, focusing on fundamental aspects and their applications to degrade specific pollutants and treat real wastewater, important large-scale attempts have also been made. This review presents and discusses the most recent advances of the EF process with special emphasis on the aspects more closely related to future implementations at the large scale, such as applications to treat real effluents (industrial and municipal wastewaters) and soil remediation, development of large-scale reactors, costs and effectiveness evaluation, and life cycle assessment. Opportunities and perspectives related to the heterogeneous EF process for real applications are also discussed. This review article aims to be a critical and exhaustive overview of the most recent developments for large-scale applications, which seeks to arouse the interest of a large scientific community and boost the development of EF systems in real environments.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".