Water-Dispersible and Ferroelectric PEGylated Barium Titanate Nanoparticles
Bibliographic record
Abstract
Dispersions of ferroelectric nanoparticles in aqueous medium can find promising applications in electro-optical, medical, and smart fluid technologies. In this report, we show the development of highly dispersed nano-sized ferroelectric barium titanate (BaTiO3) powders with high dielectric constant prepared using a simple, one-step low temperature solution method. The surface of these tetragonal-structured nanoparticles were modified with polyethylene glycol as a stabilizer and dispersant. The crystal structure, morphology and dielectric constant of samples are discussed in detail. The colloidal stability and surface behavior of these PEGylated barium titanate nanoparticles are studied by means of z -potential and dynamic light scattering measurements. We show changing the reaction conditions allows to tune the nanoparticle size. This research promotes a pathway to develop advanced ferroelectric nanomaterials with engineered properties in a simple way.
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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 | 0.001 |
| 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".