Advanced oxidation processes for water and wastewater treatment – Guidance for systematic future research
Bibliographic record
Abstract
Advanced oxidation processes (AOPs) are a growing research field with a large variety of different process variants and materials being tested at laboratory scale. However, despite extensive research in recent years and decades, many variants have not been transitioned to pilot- and full-scale operation. One major concern are the inconsistent experimental approaches applied across different studies that impede identification, comparison, and upscaling of the most promising AOPs. The aim of this tutorial review is to streamline future studies on the development of new solutions and materials for advanced oxidation by providing guidance for comparable and scalable oxidation experiments. We discuss recent developments in catalytic, ozone-based, radiation-driven, and other AOPs, and outline future perspectives and research needs. Since standardized experimental procedures are not available for most AOPs, we propose basic rules and key parameters for lab-scale evaluation of new AOPs including selection of suitable probe compounds and scavengers for the measurement of (major) reactive species. A two-phase approach to assess new AOP concepts is proposed, consisting of (i) basic research and proof-of-concept (technology readiness levels (TRL) 1-3), followed by (ii) process development in the intended water matrix including a cost comparison with an established process, applying comparable and scalable parameters such as UV fluence or ozone consumption (TRL 3-5). Subsequent demonstration of the new process (TRL 6-7) is briefly discussed, too. Finally, we highlight important research tools for a thorough mechanistic process evaluation and risk assessment including screening for transformation products that should be based on chemical logic and combined with complementary tools (mass balance, chemical calculations).
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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 | 0.006 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".