Effect of chemical species and temperature on the stability of air nanobubbles
Bibliographic record
Abstract
Abstract The colloidal stability of air nanobubbles (NBs) was studied at different temperatures (0–30 °C) and in the presence of sulfates, typically found in mining effluents, in a wide range of Na 2 SO 4 concentrations (0.001 to 1 M), along with the effect of surfactants (sodium dodecyl sulfate), chloride salts (NaCl), and acid/base reagents at a pH range from 4 to 9. Using a nanobubble generator based on hydrodynamic cavitation, 1.2 × 10 8 bubbles/mL with a typical radius of 84.66 ± 7.88 nm were generated in deionized water. Multiple evidence is provided to prove their presence in suspension, including the Tyndall effect, dynamic light scattering, and nanoparticle size analysis. Zeta potential measurements revealed that NBs are negatively charged even after two months (from − 19.48 ± 1.89 to − 10.13 ± 1.71 mV), suggesting that their stability is due to the negative charge on their surface. NBs were found to be more stable in alkaline solutions compared to acidic ones. Further, low amounts of both chloride and sulfate dissolved salts led to a reduction of the size of NBs. However, when high amounts of dissolved salts are present, NBs are more likely to coalesce, and their size to be increased. Finally, the investigation of the stability of air NBs at low temperatures revealed a non-monotonic relationship between temperature and NBs upon considering water self-ionization and ion mobility. This research aims to open a new frontier towards the application of the highly innovative NBs technology on the treatment of mining, mineral, and metal processing effluents, which are challenging aqueous solutions containing chloride and sulfate species.
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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 | 0.000 | 0.001 |
| 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) | 0.001 | 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".