Challenges and opportunities for large-scale applications of the electro-Fenton process
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it