Adsorption and Photo(electro)catalysis for Micropollutant Degradation at the Outlet of Wastewater Treatment Plants: Bibliometric Analysis and Challenges to Implementation
Bibliographic record
Abstract
Micropollutants (MPs), which include both natural and manmade substances, are becoming more prevalent in aquatic habitats as a result of the insufficient removal of these compounds in wastewater treatment plants (WWTPs). Advanced remediation techniques are required due to their persistence and potential ecotoxicological hazards. Although adsorption and photo(electro)catalysis exhibit potential in laboratory-scale investigations, the effects of their use in actual WWTP systems are still poorly understood. However, before large-scale application can be implemented, a number of issues need to be resolved, including material limitations, reactor design and optimization, and actual wastewater complexities. This study critically evaluates the application of adsorption and photo(electro)catalysis to actual wastewater, as well as recent advancements in adsorption and photo(electro)catalytic systems for the removal of micropollutants. We also explore the particular difficulties and strategies involved in the large-scale use of adsorption and photo(electro)catalysis in the treatment of wastewater. Emerging trends such as nanocomposites, metal–organic frameworks (MOFs), heterojunctions, and single-atom catalysts (SACs) are highlighted by the bibliometric analysis. We also evaluate MPs’ ecological effects in aquatic environments and the incorporation of artificial intelligence (AI) for process optimization. A strategy for transferring nanotechnologies from laboratory-scale research to wastewater treatment implementation is presented in this paper. In this strategy, implementation is proposed based on actual wastewater conditions, focusing on the development of adsorbents and catalysts, reactor design and optimization, synergy between adsorption and catalysis, life cycle analysis, and cost–benefit studies.
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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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.018 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".