Improving the Colloidal Stability of PEGylated BaTiO3 Nanoparticles withSurfactants
Bibliographic record
Abstract
Barium titanate, BaTiO3, nanoparticles (NPs) have been widely used as a ferroelectric/piezoelectric/pyroelectric material in the electronic-optical ceramic industry. However, the stability of BaTiO3 NP suspension are a matter of concern for their advanced applications in wet-ceramic manufacturing, imaging, and electrorheological fluids. In this study, we investigated the effect of three different surfactants (sodium dodecylbenzenesulfonate (anionic), cetyltrimethylammonium bromide (cationic), and sorbitan monooleate (non-ionic)) on the stability of PEGylated BaTiO3 nanoparticles in two solvents (water and ethylene glycol) by means of dynamic light scattering, ζ potential, UV-visible spectroscopy, scanning electron microscopy, and visual observation. Our findings indicate that the anionic surfactant acted as the best stabilizer for BaTiO3 nanofluids, while the cationic surfactant was the least favourable stabilizer in both water and ethylene glycol, due to the balance between attraction and repulsive forces. The results of this research provide a simple and effective approach to control and improve the colloidal stability of BaTiO3 nanoparticles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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 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".