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Cited by 708

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

Techniques for physicochemical characterization of nanomaterials

Open access
Lin, Ping-Chang, Lin, Stephen, Wang, Paul C., Sridhar, Rajagopalan·2014·Biotechnology Advances

Abstract

Advances in nanotechnology have opened up a new era of diagnosis, prevention and treatment of diseases and traumatic injuries. Nanomaterials, including those with potential for clinical applications, possess novel physicochemical properties that have an impact on their physiological interactions, from the molecular level to the systemic level. There is a lack of standardized methodologies or regulatory protocols for detection or characterization of nanomaterials. This review summarizes the techniques that are commonly used to study the size, shape, surface properties, composition, purity and stability of nanomaterials, along with their advantages and disadvantages. At present there are no FDA guidelines that have been developed specifically for nanomaterial based formulations for diagnostic or therapeutic use. There is an urgent need for standardized protocols and procedures for the characterization of nanoparticles, especially those that are intended for use as theranostics.

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Crossref · 10.1016/j.biotechadv.2013.11.006

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Metadata

Authors
Lin, P.-C., Lin, S., Wang, P. C., & Sridhar, R.
Venue
Biotechnology Advances
Publisher
Elsevier BV
Published
2014-07
Volume
32
Issue
4
Pages
711-726
ISSN
0734-9750 (ISSN-L)
OpenAlex
W2115524611
Cited by
708
References
7
FWCI
5.99
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Techniques for physicochemical characterization of nanomaterials. https://app.cochat.ai/papers/10.1016/j.biotechadv.2013.11.006
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Lin, P.-C., Lin, S., Wang, P. C., & Sridhar, R. (2014). Techniques for physicochemical characterization of nanomaterials. Biotechnology Advances. https://doi.org/10.1016/j.biotechadv.2013.11.006

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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.