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Cited by 359

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Insights into low molecular mass organic gelators: a focus on drug delivery and tissue engineering applications

Skilling, Kathryn J., Citossi, Francesca, Bradshaw, Tracey D., Ashford, Marianne, Kellam, Barrie, Marlow, Maria·2014·Soft Matter

Abstract

In recent years low molecular mass organic gelators (LMOGs) have gained increasing interest as an alternative biomaterial to polymer derived gels, with potential applications in drug delivery and tissue engineering. LMOGs are small organic molecules which self-assemble in water or organic solvents forming a 3D network that entraps the liquid phase resulting in gel formation. In this review, we report the classification of LMOGs into hydrogelators and gelators of organic solvents and we discuss the techniques commonly used to characterise the gels of these gelators with particular reference to specific applications of LMOGs in drug delivery and tissue engineering.

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Crossref · 10.1039/c3sm52244j

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Metadata

Authors
Skilling, K. J., Citossi, F., Bradshaw, T. D., Ashford, M., Kellam, B., & Marlow, M.
Venue
Soft Matter
Published
2014
Volume
10
Issue
2
Pages
237-256
ISSN
  • 1744-683X (ISSN-L)
  • 1744-6848
OpenAlex
W2108587906
Cited by
359
References
1
FWCI
10.90
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Insights into low molecular mass organic gelators: a focus on drug delivery and tissue engineering applications. https://app.cochat.ai/papers/10.1039/c3sm52244j
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Skilling, K. J., Citossi, F., Bradshaw, T. D., Ashford, M., Kellam, B., & Marlow, M. (2014). Insights into low molecular mass organic gelators: a focus on drug delivery and tissue engineering applications. Soft Matter. https://doi.org/10.1039/c3sm52244j

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.