Nanobubble aeration enhanced wastewater treatment and bioenergy generation in constructed wetlands coupled with microbial fuel cells
Bibliographic record
Abstract
Artificial aeration is a widely used approach in wastewater treatment to enhance the removal of pollutants, however, traditional aeration techniques have been challenging due to the low oxygen transfer rate (OTR). Nanobubble aeration has emerged as a promising technology that utilise nano-scale bubbles to achieve higher OTRs owing to their large surface area and unique properties such as longevity and reactive oxygen species generation. This study, for the first time, investigated the feasibility of coupling nanobubble technology with constructed wetlands (CWs) for treating livestock wastewater. The results demonstrated that nanobubble-aerated CWs achieved significantly higher removal efficiencies of total organic carbon (TOC) and ammonia (NH4+-N), at 49 % and 65 %, respectively, compared to traditional aeration treatment (36 % and 48 %) and the control group (27 % and 22 %). The enhanced performance of the nanobubble-aerated CWs can be attributed to the nearly three times higher amount of nanobubbles (Ø < 1 μm) generated from the nanobubble pump (3.68 × 108 particles/mL) compared to the normal aeration pump. Moreover, the microbial fuel cells (MFCs) embedded in the nanobubble-aerated CWs harvested 5.5 times higher electricity energy (29 mW/m2) compared to the other groups. The results suggested that nanobubble technology has the potential to trigger the innovation of CWs by enhancing their capacity for water treatment and energy recovery. Further research needs are proposed to optimise the generation of nanobubbles, allowing them to be effectively coupled with different technologies for engineering implementation.
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How this classification was reachedexpand
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 | 0.000 | 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".