Stearic acid as interface modifier and lubricant agent of the system: Polypropylene/calcium carbonate nanoparticles
Bibliographic record
Abstract
The toughness and the rheology of polypropylene (PP)‐calcium carbonate (CaCO 3 ) nanocompounds using stearic acid as an interface modifier were studied in this work. Compounding of the nanocomposites was carried out with a twin‐screw extruder. The benchmark samples (untreated) and stearic acid‐treated CaCO 3 nanoparticles were characterized by Fourier transform infrared (FTIR) spectroscopy. The nanocompounds were characterized by impact test, tensile test, scanning electron microscopy (SEM), rheological analysis, and differential scanning calorimetry (DSC). The elongation‐at‐break and impact resistance were increased in nanocompounds with interface modifier (stearic acid in hopper of the extruder). Nanocompounds with stearic acid showed the best dispersion state. Stearic acid helps to reduce complex viscosity acting as a lubricant, reducing frictional forces between nanoparticles of calcium carbonate (NCC) and PP chains. Nanocompounds with better dispersion state had crystallization temperatures very similar to the PP homopolymer. POLYM. ENG. SCI., 59:E279–E285, 2019. © 2019 Society of Plastics Engineers
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 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 | 0.000 |
| 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 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".