Evidence map›Paper›PMID 40219264›Full record

ArticlePolymers2025

Simulation, Measurement, and Optimization of Sound Absorption in Nanofiber Membrane Composite with a Nonwoven Material.

Xiaofei Shao, Xiong Yan

Abstract read
In one paragraph

Article in Polymers, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xiaofei ShaoKey Laboratory of Textile Science and Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai 201620, China.
Xiong YanKey Laboratory of Textile Science and Technology, Ministry of Education, College of Textiles, Donghua University, Shanghai 201620, China.

Funding

Fundamental Research Funds for the Central Universities and Graduate Student Innovation Fund of Donghua University CUSF-DH-D-2024010
6 · The paper itself

Abstract

To address the increasingly complex demands of noise control, this study investigated the integration of a micro-perforated nanofiber membrane (MPNM) with nonwoven fiber felt (NFF), exploiting their synergistic effects to achieve efficient low-frequency broadband sound absorption. Through theoretical analysis, numerical simulations, and experimental validation, the relationship between the sound absorption performance of the composite structure and factors such as the lamination sequence, bonding area, perforation parameters, thickness of the MPNM, and thickness of the NFF were elucidated. These findings provided new insights for the design of high-performance, tunable, sound-absorbing materials. The results demonstrated that the MPNM-NFF effectively combined two distinct sound absorption mechanisms, thereby expanding the effective absorption bandwidth, with particularly enhanced low-frequency sound absorption. Moreover, through algorithmic optimization of the structural parameters, targeted absorption of noise across different frequency bands was achieved, with the optimal average sound absorption coefficients reaching 0.70 in the low-frequency range, 0.91 in the mid-frequency range, and 0.82 in the full-frequency range. This research offered both theoretical foundations and practical guidance for the development of composite materials with high efficiency and broadband sound absorption characteristics, paving the way for innovative applications in noise control materials.

Indexed as

compositeslow-frequencymicro-perforated nanofiber membranesnonwoven fiber feltsound absorption mechanism

Identifiers

PMID40219264
PMCPMC11991465

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.