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Quantitative Comparison of Photothermal Heat Generation between Gold Nanospheres and Nanorods

Identifieur interne : 000833 ( Pmc/Curation ); précédent : 000832; suivant : 000834

Quantitative Comparison of Photothermal Heat Generation between Gold Nanospheres and Nanorods

Auteurs : Zhenpeng Qin [États-Unis] ; Yiru Wang [États-Unis] ; Jaona Randrianalisoa [France] ; Vahid Raeesi [Canada] ; Warren C. W. Chan [Canada] ; Wojciech Lipi Ski [Australie] ; John C. Bischof [États-Unis]

Source :

RBID : PMC:4956767

Abstract

Gold nanoparticles (GNPs) are widely used for biomedical applications due to unique optical properties, established synthesis methods, and biological compatibility. Despite important applications of plasmonic heating in thermal therapy, imaging, and diagnostics, the lack of quantification in heat generation leads to difficulties in comparing the heating capability for new plasmonic nanostructures and predicting the therapeutic and diagnostic outcome. This study quantifies GNP heat generation by experimental measurements and theoretical predictions for gold nanospheres (GNS) and nanorods (GNR). Interestingly, the results show a GNP-type dependent agreement between experiment and theory. The measured heat generation of GNS matches well with theory, while the measured heat generation of GNR is only 30% of that predicted theoretically at peak absorption. This then leads to a surprising finding that the polydispersity, the deviation of nanoparticle size and shape from nominal value, significantly influences GNR heat generation (>70% reduction), while having a limited effect for GNS (<10% change). This work demonstrates that polydispersity is an important metric in quantitatively predicting plasmonic heat generation and provides a validated framework to quantitatively compare the heating capabilities between gold and other plasmonic nanostructures.


Url:
DOI: 10.1038/srep29836
PubMed: 27445172
PubMed Central: 4956767

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PMC:4956767

Le document en format XML

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<p>Gold nanoparticles (GNPs) are widely used for biomedical applications due to unique optical properties, established synthesis methods, and biological compatibility. Despite important applications of plasmonic heating in thermal therapy, imaging, and diagnostics, the lack of quantification in heat generation leads to difficulties in comparing the heating capability for new plasmonic nanostructures and predicting the therapeutic and diagnostic outcome. This study quantifies GNP heat generation by experimental measurements and theoretical predictions for gold nanospheres (GNS) and nanorods (GNR). Interestingly, the results show a GNP-type dependent agreement between experiment and theory. The measured heat generation of GNS matches well with theory, while the measured heat generation of GNR is only 30% of that predicted theoretically at peak absorption. This then leads to a surprising finding that the polydispersity, the deviation of nanoparticle size and shape from nominal value, significantly influences GNR heat generation (>70% reduction), while having a limited effect for GNS (<10% change). This work demonstrates that polydispersity is an important metric in quantitatively predicting plasmonic heat generation and provides a validated framework to quantitatively compare the heating capabilities between gold and other plasmonic nanostructures.</p>
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</TEI>
<pmc article-type="research-article">
<pmc-dir>properties open_access</pmc-dir>
<front>
<journal-meta>
<journal-id journal-id-type="nlm-ta">Sci Rep</journal-id>
<journal-id journal-id-type="iso-abbrev">Sci Rep</journal-id>
<journal-title-group>
<journal-title>Scientific Reports</journal-title>
</journal-title-group>
<issn pub-type="epub">2045-2322</issn>
<publisher>
<publisher-name>Nature Publishing Group</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="pmid">27445172</article-id>
<article-id pub-id-type="pmc">4956767</article-id>
<article-id pub-id-type="pii">srep29836</article-id>
<article-id pub-id-type="doi">10.1038/srep29836</article-id>
<article-categories>
<subj-group subj-group-type="heading">
<subject>Article</subject>
</subj-group>
</article-categories>
<title-group>
<article-title>Quantitative Comparison of Photothermal Heat Generation between Gold Nanospheres and Nanorods</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<name>
<surname>Qin</surname>
<given-names>Zhenpeng</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="author-notes" rid="n1">*</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Wang</surname>
<given-names>Yiru</given-names>
</name>
<xref ref-type="aff" rid="a1">1</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Randrianalisoa</surname>
<given-names>Jaona</given-names>
</name>
<xref ref-type="aff" rid="a2">2</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Raeesi</surname>
<given-names>Vahid</given-names>
</name>
<xref ref-type="aff" rid="a3">3</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Chan</surname>
<given-names>Warren C. W.</given-names>
</name>
<xref ref-type="aff" rid="a3">3</xref>
<xref ref-type="aff" rid="a4">4</xref>
<xref ref-type="aff" rid="a5">5</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Lipiński</surname>
<given-names>Wojciech</given-names>
</name>
<xref ref-type="aff" rid="a6">6</xref>
</contrib>
<contrib contrib-type="author">
<name>
<surname>Bischof</surname>
<given-names>John C.</given-names>
</name>
<xref ref-type="corresp" rid="c1">a</xref>
<xref ref-type="aff" rid="a1">1</xref>
<xref ref-type="aff" rid="a7">7</xref>
</contrib>
<aff id="a1">
<label>1</label>
<institution>Department of Mechanical Engineering, University of Minnesota</institution>
, Minneapolis, MN 55455,
<country>USA</country>
</aff>
<aff id="a2">
<label>2</label>
<institution>Groupe de Recherche en Sciences pour l’Ingénieur (GRESPI) - EA 4694, University of Reims Champagne-Ardenne</institution>
, 51687 Reims Cedex 2,
<country>France</country>
</aff>
<aff id="a3">
<label>3</label>
<institution>Department of Materials Science and Engineering, University of Toronto</institution>
, Toronto, Ontario M5S 3G9,
<country>Canada</country>
</aff>
<aff id="a4">
<label>4</label>
<institution>Institute of Biomaterials and Biomedical Engineering, Department of Chemistry, Department of Chemical Engineering, University of Toronto</institution>
, Toronto, Ontario M5S 3G9,
<country>Canada</country>
</aff>
<aff id="a5">
<label>5</label>
<institution>Donnelly Center for Cellular and Biomolecular Research, University of Toronto</institution>
, Toronto, Ontario M5S 3E1,
<country>Canada</country>
</aff>
<aff id="a6">
<label>6</label>
<institution>Research School of Engineering, The Australian National University</institution>
, Canberra, ACT 2601,
<country>Australia</country>
</aff>
<aff id="a7">
<label>7</label>
<institution>Department of Biomedical Engineering, University of Minnesota</institution>
, Minneapolis, MN 55455,
<country>USA</country>
</aff>
</contrib-group>
<author-notes>
<corresp id="c1">
<label>a</label>
<email>bischof@umn.edu</email>
</corresp>
<fn id="n1">
<label>*</label>
<p>Present address: Department of Mechanical Engineering, Department of Bioengineering, University of Texas at Dallas, Richardson, TX 75080, USA.</p>
</fn>
</author-notes>
<pub-date pub-type="epub">
<day>21</day>
<month>07</month>
<year>2016</year>
</pub-date>
<pub-date pub-type="collection">
<year>2016</year>
</pub-date>
<volume>6</volume>
<elocation-id>29836</elocation-id>
<history>
<date date-type="received">
<day>25</day>
<month>04</month>
<year>2016</year>
</date>
<date date-type="accepted">
<day>21</day>
<month>06</month>
<year>2016</year>
</date>
</history>
<permissions>
<copyright-statement>Copyright © 2016, Macmillan Publishers Limited</copyright-statement>
<copyright-year>2016</copyright-year>
<copyright-holder>Macmillan Publishers Limited</copyright-holder>
<license license-type="open-access" xlink:href="http://creativecommons.org/licenses/by/4.0/">
<pmc-comment>author-paid</pmc-comment>
<license-p>This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit
<ext-link ext-link-type="uri" xlink:href="http://creativecommons.org/licenses/by/4.0/">http://creativecommons.org/licenses/by/4.0/</ext-link>
</license-p>
</license>
</permissions>
<abstract>
<p>Gold nanoparticles (GNPs) are widely used for biomedical applications due to unique optical properties, established synthesis methods, and biological compatibility. Despite important applications of plasmonic heating in thermal therapy, imaging, and diagnostics, the lack of quantification in heat generation leads to difficulties in comparing the heating capability for new plasmonic nanostructures and predicting the therapeutic and diagnostic outcome. This study quantifies GNP heat generation by experimental measurements and theoretical predictions for gold nanospheres (GNS) and nanorods (GNR). Interestingly, the results show a GNP-type dependent agreement between experiment and theory. The measured heat generation of GNS matches well with theory, while the measured heat generation of GNR is only 30% of that predicted theoretically at peak absorption. This then leads to a surprising finding that the polydispersity, the deviation of nanoparticle size and shape from nominal value, significantly influences GNR heat generation (>70% reduction), while having a limited effect for GNS (<10% change). This work demonstrates that polydispersity is an important metric in quantitatively predicting plasmonic heat generation and provides a validated framework to quantitatively compare the heating capabilities between gold and other plasmonic nanostructures.</p>
</abstract>
</article-meta>
</front>
<floats-group>
<fig id="f1">
<label>Figure 1</label>
<caption>
<title>Flow-chart for the combined theoretical and experimental approach.</title>
</caption>
<graphic xlink:href="srep29836-f1"></graphic>
</fig>
<fig id="f2">
<label>Figure 2</label>
<caption>
<title>Comparison of DDA computation with experiment (i.e. UV–Vis spectroscopy and photothermal measurement) for GNS.</title>
<p> (
<bold>A</bold>
) TEM images of 15 nm, 30 nm, 60 nm and 100 nm GNS; (
<bold>B</bold>
) Measured (UV–Vis) versus DDA-computed optical extinction spectrum; (
<bold>C</bold>
) Photothermal conversion efficiency (η) and absorption cross section (
<italic>C</italic>
<sub>abs</sub>
at 532 nm): quantitative measurement versus DDA prediction.</p>
</caption>
<graphic xlink:href="srep29836-f2"></graphic>
</fig>
<fig id="f3">
<label>Figure 3</label>
<caption>
<title>Predicted normalized efficiency factor
<italic>Q</italic>
<sup>*</sup>
of monodispersed GNR with nominal size, demonstrating a deviation from experimentally measured values.</title>
<p>Dielectric constants include bulk and size dependent properties based on radius, 4
<italic>V/S</italic>
, and ray-tracing which considers anisotropy of GNR. Nanorod nominal size:
<italic>D</italic>
 = 10.6 nm,
<italic>L</italic>
 = 40 nm.</p>
</caption>
<graphic xlink:href="srep29836-f3"></graphic>
</fig>
<fig id="f4">
<label>Figure 4</label>
<caption>
<title>Inclusion of polydispersity into prediction leads to agreement between DDA prediction and UV–Vis measurement for GNR.</title>
<p>Two GNR were studied with nominal sizes (
<bold>A</bold>
)
<italic>D</italic>
 = 10.6 nm and
<italic>L</italic>
 = 40 nm; (
<bold>B</bold>
)
<italic>D</italic>
 = 8.6 nm and
<italic>L</italic>
 = 27 nm; (
<bold>C,D</bold>
) Polydispersity measurement. The polydispersity data for Rod 2 was reproduced from Khlebstov
<italic>et al</italic>
. with permission
<xref ref-type="bibr" rid="b34">34</xref>
. (
<bold>E,F</bold>
) Measured (UV–Vis) versus DDA-computed optical extinction spectrum; (
<bold>G,H</bold>
) Photothermal conversion efficiency (η) and absorption cross section (
<italic>C</italic>
<sub>abs</sub>
): quantitative measurement versus DDA prediction for Rod 1 (peak refers to 740 nm).</p>
</caption>
<graphic xlink:href="srep29836-f4"></graphic>
</fig>
<fig id="f5">
<label>Figure 5</label>
<caption>
<title>Dielectric constants do not significantly change the optical properties of GNR (
<italic>D</italic>
 = 8.6 nm,
<italic>L</italic>
 = 27 nm) after incorporating polydispersity measured from TEM (shown in
<xref ref-type="fig" rid="f4">Fig. 4</xref>
).</title>
</caption>
<graphic xlink:href="srep29836-f5"></graphic>
</fig>
<fig id="f6">
<label>Figure 6</label>
<caption>
<title>Differential impact of polydispersity for GNS and GNR.</title>
<p>(
<bold>A,B</bold>
) Changing polydispersity does not significantly affect the optical properties of GNS (
<italic>D</italic>
 = 30 nm in this example); (
<bold>C,D</bold>
) polydispersity changes the optical properties of GNR including extinction peak and peak width. Polydispersity is modeled by fixing
<italic>D</italic>
 = 10 nm and varying
<italic>L</italic>
; (
<bold>E</bold>
) Summary of the impact of polydispersity on the optical properties (peak extinction efficiency) of GNS and GNR. Here polydispersity is defined as the standard deviation by the mean. (
<bold>F</bold>
) Impact of polydispersity on peak wavelength shift.</p>
</caption>
<graphic xlink:href="srep29836-f6"></graphic>
</fig>
<fig id="f7">
<label>Figure 7</label>
<caption>
<title>Incorporating polydispersity to predict plasmonic photothermal absorption and heat generation.</title>
<p>The absorption cross section of a GNS (15 nm) is compared with a GNR (Rod 1 in
<xref ref-type="fig" rid="f4">Fig. 4</xref>
at peak absorption, with similar gold volume with 15 nm GNS) with and without accounting polydispersity.</p>
</caption>
<graphic xlink:href="srep29836-f7"></graphic>
</fig>
</floats-group>
</pmc>
</record>

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