SONIC: A Speed of Sound Measurement for Nanobubble Characterization
Bibliographic record
Abstract
Nanobubbles (NBs)─gas inclusions in water with diameters <1 μm─are of growing interest because of their unique properties and their potential for transformative applications. For example, it has been reported that NBs exist in water over long periods (i.e., weeks to months) and can act as free gas reservoirs. However, NBs are a source of scientific debate, particularly regarding characterization methods. Conventional methods, such as dynamic light scattering, nanoparticle tracking analysis, and nanoflow cytometry, cannot distinguish between nanoparticles and NBs since they are insensitive to the differences of the physical properties of the materials. However, acoustic (speed of sound) measurements can be used to quantify NBs because they rely on the compressibility dependence of gases (κ gas ) which is considerably larger than liquids (κ water ) and solids. In the present work, a speed of sound measurement for nanobubble characterization (SONIC) was designed and developed to probe the compressibility variations diagnostic to NBs in water. NBs in water act as acoustic scatters that reduce the speed of sound relative to the bubble-free water. This decrease in the speed of sound can only be attributed to the existence of gas bubbles due to the strong compressibility dependence that solid nanoparticles lack. The results obtained from the acoustic measurements are compared with the observations from nanoparticle tracking analysis to confirm the existence of NBs in water. SONIC was validated in water with different molalities of NaCl (aq), and in the presence of solid nanoparticles of similar size and concentration to the NBs. SONIC is the first technique that addresses an important bottleneck of NB characterization by providing accurate and selective characterization of NBs in complex water mixtures that will help the behavior of NBs to be better understood and accelerate their application in many fields.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".