Advanced oxidation processes in microreactors for water and wastewater treatment: Development, challenges, and opportunities
Bibliographic record
Abstract
The miniaturization of reaction processes by microreactors offers many significant advantages over the use of larger, conventional reactors. Microreactors' interior structures exhibit comparatively higher surface area-to-volume ratios, which reduce reactant diffusion distances, enable faster and more efficient heat and mass transfer, and better control over process conditions. These advantages can be exploited to significantly enhance the performance of advanced oxidation processes (AOPs) commonly used for the removal of water pollutants. This comprehensive review of the rapidly emerging area of environmental microfluidics describes recent advances in the development and application of microreactors to AOPs for water and wastewater treatment. Consideration is given to the hydrodynamic properties, construction materials, fabrication techniques, designs, process features, and upscaling of microreactors used for AOPs. The use of microreactors for various AOP types, including photocatalytic, electrochemical, Fenton, ozonation, and plasma-phase processes, showcases how microfluidic technology enhances mass transfer, improves treatment efficiency, and decreases the consumption of energy and chemicals. Despite significant advancements of microreactor technology, organic pollutant degradation mechanisms that operate during microscale AOPs remain poorly understood. Moreover, limited throughput capacity of microreactor systems significantly restrains their industrial-scale applicability. Since large microreactor-inspired AOP systems are needed to meet the high-throughput requirements of the water treatment sector, scale-up strategies and recommendations are suggested as priority research opportunities. While microstructured reactor technology remains in an early stage of development, this work offers valuable insight for future research and development of AOPs in microreactors for environmental purposes.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".