Evidence map›Paper›PMID 42146740›Full record

ArticleACS materials Au2026

Composite of Long Polymer Chains and Structured Silica Particles Achieves High Modulus and Low Hysteresis.

Matthew Wei Ming Tan, Chenghai Li, Masako Yoshihara Yang, Mikaya Cooper Parente, Yakov Kutsovsky, Zhigang Suo

Abstract read
In one paragraph

Article in ACS materials Au, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

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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

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0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

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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

6 authors.

Matthew Wei Ming TanJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.ORCID https://orcid.org/0000-0002-2872-8314
Chenghai LiJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.
Masako Yoshihara YangJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.
Mikaya Cooper ParenteJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.
Yakov KutsovskyExpert-in-Residence, Office of Technology Development, Harvard University, Cambridge, Massachusetts 02138, United States.
Zhigang SuoJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts 02138, United States.ORCID https://orcid.org/0000-0002-4068-4844

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Many applications require rubber-like materials to fulfill two important requirements: high modulus to resist excessive deformation and low hysteresis to reduce energy loss. High modulus is commonly realized by mixing rubber with inorganic fillers, but the fillers also increase the hysteresis. This conflict between the modulus and hysteresis presents a challenge in material development. Here, we show that a high modulus and low hysteresis are achieved simultaneously in a composite of long rubber chains and structured fillers. We prepare a model system using natural rubber latex with long polymer chains and structured fillers in which primary silica particles fuse into aggregates. Each aggregate has an open structure that occludes the rubber. The composite forms two networks: a network of cross-linked natural rubber chains and a network of percolated silica aggregates. The two networks intertwine with each other and interlink through covalent bonds. We measure the modulus and hysteresis of the composites prepared with various volume fractions of aggregates and molar fractions of the initiator. We discuss the mechanisms by which long rubber chains and structured silica aggregates lower hysteresis while maintaining a high modulus. This work provides insights into the development of rubber products for energy efficiency.

Indexed as

compositeshysteresismodulusnatural rubbersilica particles

Identifiers

PMID42146740
PMCPMC13177404

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LicenceCC BY
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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.