Morphological Characterization by SEM, TEM and AFM of Nanoparticles and Functional Nanocomposites Based on Natural Rubber Filled with Oxide Nanopowders
Bibliographic record
Abstract
Nanocomposites were prepared from mixture of different concentrations of ferroelectric nanoparticles in an elastomeric matrix based on the vulcanized natural rubber. The morphological characterization of nanocomposites was carried out using Scanning electron microscopy (SEM), Transmission electron microscopy (TEM) and Atomic force microscopy (AFM). The nanocrystalline ferroelectric oxide is potassium strontium niobate (KSN) with stoichiometry KSr 2 Nb 5 O 15 , and was synthesized by the chemical route using a modified polyol method, obtaining particle size and microstrain equal to 20 nm and 0.32, respectively. These ferroelectric nanoparticles were added into the natural rubber in concentrations equal to 1, 3, 5, 10, 20 and 50 phr (parts per hundred of rubber) forming ferroelectric nanocomposites (NR/KSN). Using morphological characterization, we identified the maximum value of surface roughness at low concentrations, in particular, sample with 3 phr of nanoparticles and factors such as encapsulation and uniformity in the distribution of nanoparticles into the natural rubber matrix are investigated and discussed.
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 | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".