Evidence map›Paper›PMID 41871255›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2026

Amplifying toughness in silica-reinforced natural rubber by preserving long chains.

Matthew Wei Ming Tan, Guodong Nian, Zheqi Chen, Xianyang Bao, Yakov Kutsovsky, Zhigang Suo

Abstract read
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

6 authors.

Matthew Wei Ming Tan *John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Guodong Nian *John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.ORCID 0000-0001-7005-634X
Zheqi ChenJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.ORCID 0000-0001-7193-5426
Xianyang BaoJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.
Yakov KutsovskyExpert-in-Residence, Office of Technology Development, Harvard University, Cambridge, MA 02138.
Zhigang SuoJohn A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138.ORCID 0000-0002-4068-4844

Funding

DOD | AF | AMC | AFRL | Air Force Office of Scientific Research (AFOSR) FA9550-20-1-0397NSF (NSF) DMR-2011754
6 · The paper itself

Abstract

Natural rubber outperforms synthetic rubbers because of its long chains and strain-induced crystallization (SIC). However, these advantages are largely lost when the natural rubber chains are masticated during processing, and silica particles are added for reinforcement. Mastication eases mixing but shortens chains and lowers performance. Silica particles require covalent interlinks with rubber chains, but these interlinks restrict chain stretch and alignment, reducing SIC. Here, we show that the performance of silica-reinforced natural rubber can be markedly enhanced by preserving long natural rubber chains. We use a solvent to dissolve natural rubber latex into individual rubber chains and use the solution to uniformly disperse silica particles. After drying, the uncured compound can be stored and molded prior to curing. The long rubber chains are then sparsely crosslinked with one another and interlinked with the silica particles. The long strands readily align under stretch and increase SIC. Preserving long chains elevates toughness by an order of magnitude, from ~2 to 44 kJ m

Indexed as

fracturenatural rubberparticlespolymersprocessing

Identifiers

PMID41871255
PMCPMC13037892

What OpenQuestion holds

Textmetadata
Read underepoch 390

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.