Evidence map›Paper›PMID 41323426›Full record

ArticleJournal of sol-gel science and technology2025

Nanocomposite hydrogels reinforced with vinyl functionalised silica nanoparticles.

Ali A Mohammed, Archontia Tsiampali, Siwei Li, Alessandra Pinna, Julian R Jones

Abstract read
In one paragraph

Article in Journal of sol-gel science and technology, 2025. 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. Review
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

5 authors.

Ali A MohammedDyson School of Design Engineering, Imperial College London, London, UK.
Archontia TsiampaliDepartment of Materials, Imperial College London, London, UK.
Siwei LiDepartment of Materials, Imperial College London, London, UK.
Alessandra PinnaDepartment of Materials, Imperial College London, London, UK.
Julian R JonesDepartment of Materials, Imperial College London, London, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This work reports double network hydrogel/silica nanocomposites with increased mechanical toughness and strength compared to their soft polymer-only counterparts. Applications are in tissue repair, such as cartilage, soft robotics and motion sensing. Covalent coupling of the sol-gel silica nanoparticles and the gel is vital because the gel swells on contact with water. Here, coupling was achieved through vinyl functionalisation of the silica nanoparticles (VSNPs) that enabled cross-linking to the network using photopolymerisation. The double network gel was an interpenetrating network hydrogel (IPNG) with 2-acrylamido-2-methylpropane-sulfonic acid (AMPS) as the first network, and acrylamide (AAm) as the second network. The effect of vinyl silica nanoparticle size and loading concentration were investigated on swelling behaviour, microstructure, compressive properties and nanoparticle retention. Increased size and loading concentration of VSNPs allowed for tailorability of swelling properties; nanocomposite IPNGs swelled less (88%) compared to control gels (97%). The nanocomposite IPNGs, with 20Wt% VSNPs, exhibited a max compressive strength of 810 ± 80 kPa at a strain of 75 ± 6%, similar to the lower range of articular cartilage, and an order of magnitude higher strength than control gels (90 ± 20 kPa, at a strain of 40 ± 3). SEM images show VSNP-polymer integration, with nanoparticles within the mesh walls. The nanocomposite structure provides reinforcement and toughness to soft IPNGs, making them suitable candidates for soft material repair.

Indexed as

HydrogelsNanocompositesNanoparticlesTissue engineering

Identifiers

PMID41323426
PMCPMC12662862

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Registered trials

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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.