Formulation and Performance of NBR/CR-Based High-Damping Rubber Composites for Soundproof Using Orthogonal Test
Bibliographic record
Abstract
), 25 groups of samples were prepared by using a low-temperature one-time rubber mixing process. With tensile strength, average transmission loss, and damping peak as indexes, the influence degree of different factors and levels on the properties of acoustic composites was quantitatively discussed and analyzed. The matrix weight analysis was employed to optimize the material formula of rubber composites, and the corresponding influence weight was given. Results showed that the acoustic composite with blending ratio of 70/30 for NBR/CR matrix had preferable mechanical and acoustic properties; adding mica powder (MP) and montmorillonite (MMT) in matrix contributed to improve all above three indexes owing to their specific lamellar structures; hollow glass beads (HGB) had a positive influence on improving acoustic property due to its hollow microcavities, however, it had a negative impact on damping property because of the smooth spherical surfaces. Accordingly, the optimal formulation was found to be NBR/CR blending ratio of 70/30, MP of 10 phr (per hundred rubber), HGB of 4 phr, and MMT of 10 phr.
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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 | 0.000 |
| 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".