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Cited by 358

The DOI matches its Crossref record; 111 of the 111 references it cites were found in a registry or an index.

Nanomaterial-Based Sensors for Detection of Disease by Volatile Organic Compounds

Broza, Yoav Y, Haick, Hossam·2013·Nanomedicine

Abstract

The importance of developing new diagnostic and detection technologies for the growing number of clinical challenges is rising each year. Here, we present a concise, yet didactic review on a new diagnostics frontier based on the detection of disease-related volatile organic compounds (VOCs) by means of nanomaterial-based sensors. Nanomaterials are ideal for such sensor arrays because they are easily fabricated, chemically versatile and can be integrated into currently available sensing platforms. Following a general introduction, we provide a brief description of the VOC-related diseases concept. Then, we focus on detection of VOC-related diseases by selective and crossreactive sensing approaches, through chemical, optical and mechanical transducers incorporating the most important classes of nanomaterials. Selected examples of the integration of nanomaterials into selective sensors and crossreactive sensor arrays are given. We conclude with a brief discussion on the integration possibilities of different types of nanomaterials into sensor arrays, and the expected outcomes and limitations.

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Metadata

Authors
Broza, Y. Y., & Haick, H.
Venue
Nanomedicine
Published
2013-05
Volume
8
Issue
5
Pages
785-806
ISSN
  • 1743-5889 (ISSN-L)
  • 1748-6963
OpenAlex
W2134986799
Cited by
358
References
111
FWCI
15.94
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Nanomaterial-Based Sensors for Detection of Disease by Volatile Organic Compounds. https://app.cochat.ai/papers/10.2217/nnm.13.64
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Broza, Y. Y., & Haick, H. (2013). Nanomaterial-Based Sensors for Detection of Disease by Volatile Organic Compounds. Nanomedicine. https://doi.org/10.2217/nnm.13.64

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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.