The role of gas composition in nanobubbles: Generation, characterization, applications, and future directions for sustainable technologies
Bibliographic record
Abstract
Nanobubbles (NBs) are gas-filled entities under 1000 nm with unique physicochemical properties, including longevity and high gas-liquid mass transfer, making them suitable for diverse environmental, agricultural, medical, and energy applications. However, previous analyses often treat NBs monolithically, failing to connect the encapsulated gas core to the specific application mechanism, stability, and concentration. This review provides a targeted, mechanistic analysis establishing that gas composition is the primary design parameter defining NB functionality. We concisely link scalable generation methods and gas-specific methods to their influence on achievable NBs concentration. We demonstrate how the gas composition dictates physicochemical properties, from the high stability of O 2 and O 3 NBs to the inherent instability of CO 2 NBs. Most critically, this review re-frames applications based on a new gas-defined redox framework. (1) Oxidative: O 2 NBs for sustained oxygenation; O 3 NBs as an Advanced Oxidation Process, (2) Reductive: H 2 NBs for remediation, and (3) Physical/Inert: N 2 NBs for physical stripping. This is the first analysis to centralize the role of gas composition from a mechanistic, rather than descriptive, perspective, bridging fundamental properties with how applications function. Future research should prioritize optimizing gas-specific stability and concentration in complex real-world matrices and scaling generation to unlock the full potential of this technology for sustainable solutions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".