Advances in microbubble-based separation technologies for microplastics removal from water
Bibliographic record
Abstract
The widespread occurrence of microplastics (MPs) in aquatic environments has raised significant ecological concerns, particularly due to their interactions with coexisting pollutants and potential impact on aquatic ecosystems . Microbubble (MB) flotation has emerged as a low-cost, scalable solution, achieving removal efficiencies of 75–95 % for MPs (50–5000 µm) through tailored bubble size (10– 100 µm) and surface charge optimization. This review systematically examines the physicochemical properties of MPs, their role as pollutant carriers, and recent advancements in CFD-guided MB generation techniques, including the effects of MP aging and aquatic chemistry on separation performance. By synthesizing experimental and computational studies, we highlight how CFD modeling has uncovered critical mechanisms-such as turbulent flow regimes and bubble- MP collision probabilities-that enhance capture efficiency. Furthermore, we discuss innovations in pulsatile flow MB systems and surfactant-free stabilization strategies. This work identifies key gaps in CFD-MB integration, such as multiscale MP heterogeneity and biofilm interactions, and proposes adaptive modeling frameworks to address them. By bridging experimental insights with computational advances, this review summarizes the advantages and limitations of MB-enhanced MP separation and provides the perspective on potential applications after further improvements.
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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 | 0.000 |
| 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 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".