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Record W4375850262 · doi:10.3390/polym15092208

Formulation and Performance of NBR/CR-Based High-Damping Rubber Composites for Soundproof Using Orthogonal Test

2023· article· en· W4375850262 on OpenAlexaff
Xiankui Zeng, Jinmei Zhu, Gao‐Wei Li, Qing Miao, Mohini Sain, Ranran Jian

Bibliographic record

VenuePolymers · 2023
Typearticle
Languageen
FieldEngineering
TopicAcoustic Wave Phenomena Research
Canadian institutionsUniversity of Toronto
FundersNatural Science Foundation of Shandong ProvinceNational Natural Science Foundation of China
KeywordsMaterials scienceComposite materialNatural rubberUltimate tensile strengthNitrile rubberComposite numberMontmorillonite

Abstract

fetched live from OpenAlex

), 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.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.539
Threshold uncertainty score0.431

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.261
Teacher spread0.233 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2023
Admission routes1
Has abstractyes

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