Recent advances in bubble-based technologies: Underlying interaction mechanisms and applications
Bibliographic record
Abstract
Gas bubbles widely exist in nature and numerous industrial processes. The physicochemical characteristics of bubbles such as large specific surface area, low density, and hydrophobicity make them an ideal platform for developing colloidal and interfacial technologies. Over the past few decades, much effort has been devoted to investigating the properties and behaviors of bubbles and their applications. A series of bubble-based technologies (BBTs) have been developed, which have attracted increasing attention and shown great importance in a wide range of engineering, material, and biological fields. These BBTs, such as bubble flotation and the bubble-liposome system, provide feasible and promising solutions to mineral separation, material assembling, medical diagnosis, and drug delivery. In this work, we have systematically reviewed the physicochemical characteristics of bubbles and how to modulate their behaviors in complex fluid systems, as well as the underlying fundamental interaction mechanisms of bubbles in related BBTs. Advanced nanomechanical techniques such as atomic force microscopy, which are used to quantify the interaction mechanisms in bubble-containing systems, have been introduced. The effects of various influential factors on the bubble behaviors are discussed, which provide potential approaches to improve the controllability and performance of BBTs. The recent advances in the applications of selected BBTs in engineering, biomedical, and material areas are presented. Some remaining challenging issues and perspectives for future studies have also been discussed. This review improves the fundamental understanding of characteristics and surface interaction mechanisms of bubbles, with useful implications for developing advanced BBTs.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".