The polymer nanocomposite characteristics on various mixtures and mixing times in simple mixing method
Bibliographic record
Abstract
Science and technological developments in the material field have been currently dedicated to a super strong material potential based on nanotechnology. The super strong material can be created from the mixture of epoxy-resin polymer and SiO2 (silicon dioxide) nanoparticles. Polymers exist as a nanoparticle adhesive due to nano-SiO2, which possesses a high amorphic level, resulting in a stronger, more flexible, and stiffer combination than the current super strong material. The advantages of nanocomposite polymer using epoxy- resin and nano-SiO2 produce strong and light products with an easier production process, utilizing local materials that can improve the following material quality. This study used four material variations, namely P30, P35, P40, and P45, combined with nanoparticles at 0%, 1%, 2%, 3%, and 4%. Based on the results, the highest compressive strength level was found on the PNK 40 EH2:1N1 mixture at 53.18 MPa with 1627 kg/m3 weight density. From the X-Ray Diffraction (XRD) test results, the following mixture had the lowest amorphic phase, while Fourier Transform Infra-Red (FTIR) test results showed that the following mixture absorbed more hydrogen elements, and Scanning Electron Microscope (SEM) observation on the following material mixture had more organized particle distribution.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".