Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization
This article develops a set of design guidelines for maximizing heat dissipation characteristics of magnetic ferrite MFe[subscript 2]O[subscript 4] (M = Mn, Fe, Co) nanoparticles in alternating magnetic fields. Using magnetic and structural nanoparticle characterization, we identify key synthetic pa...
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| Format: | Article |
| Language: | en_US |
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American Chemical Society (ACS)
2015
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| Online Access: | http://hdl.handle.net/1721.1/92978 https://orcid.org/0000-0002-6420-1616 https://orcid.org/0000-0001-6495-5197 https://orcid.org/0000-0003-0946-0401 |
| _version_ | 1811070176821510144 |
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| author | Chen, Ritchie Anikeeva, Polina Olegovna Christiansen, Michael Gary |
| author2 | Massachusetts Institute of Technology. Department of Materials Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Materials Science and Engineering Chen, Ritchie Anikeeva, Polina Olegovna Christiansen, Michael Gary |
| author_sort | Chen, Ritchie |
| collection | MIT |
| description | This article develops a set of design guidelines for maximizing heat dissipation characteristics of magnetic ferrite MFe[subscript 2]O[subscript 4] (M = Mn, Fe, Co) nanoparticles in alternating magnetic fields. Using magnetic and structural nanoparticle characterization, we identify key synthetic parameters in the thermal decomposition of organometallic precursors that yield optimized magnetic nanoparticles over a wide range of sizes and compositions. The developed synthetic procedures allow for gram-scale production of magnetic nanoparticles stable in physiological buffer for several months. Our magnetic nanoparticles display some of the highest heat dissipation rates, which are in qualitative agreement with the trends predicted by a dynamic hysteresis model of coherent magnetization reversal in single domain magnetic particles. By combining physical simulations with robust scalable synthesis and materials characterization techniques, this work provides a pathway to a model-driven design of magnetic nanoparticles tailored to a variety of biomedical applications ranging from cancer hyperthermia to remote control of gene expression. |
| first_indexed | 2024-09-23T08:30:03Z |
| format | Article |
| id | mit-1721.1/92978 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T08:30:03Z |
| publishDate | 2015 |
| publisher | American Chemical Society (ACS) |
| record_format | dspace |
| spelling | mit-1721.1/929782022-09-23T12:44:45Z Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization Chen, Ritchie Anikeeva, Polina Olegovna Christiansen, Michael Gary Massachusetts Institute of Technology. Department of Materials Science and Engineering Massachusetts Institute of Technology. Research Laboratory of Electronics Anikeeva, Polina Olegovna Anikeeva, Polina Olegovna Chen, Ritchie Christiansen, Michael Gary This article develops a set of design guidelines for maximizing heat dissipation characteristics of magnetic ferrite MFe[subscript 2]O[subscript 4] (M = Mn, Fe, Co) nanoparticles in alternating magnetic fields. Using magnetic and structural nanoparticle characterization, we identify key synthetic parameters in the thermal decomposition of organometallic precursors that yield optimized magnetic nanoparticles over a wide range of sizes and compositions. The developed synthetic procedures allow for gram-scale production of magnetic nanoparticles stable in physiological buffer for several months. Our magnetic nanoparticles display some of the highest heat dissipation rates, which are in qualitative agreement with the trends predicted by a dynamic hysteresis model of coherent magnetization reversal in single domain magnetic particles. By combining physical simulations with robust scalable synthesis and materials characterization techniques, this work provides a pathway to a model-driven design of magnetic nanoparticles tailored to a variety of biomedical applications ranging from cancer hyperthermia to remote control of gene expression. Sanofi Aventis (Firm) (Biomedical Innovation Award) National Science Foundation (U.S.). Materials Research Science and Engineering Centers (Program) (DMR-0819762) National Science Foundation (U.S.). Graduate Research Fellowship Program 2015-01-20T16:23:46Z 2015-01-20T16:23:46Z 2013-09 2013-07 Article http://purl.org/eprint/type/JournalArticle 1936-0851 1936-086X http://hdl.handle.net/1721.1/92978 Chen, Ritchie, Michael G. Christiansen, and Polina Anikeeva. “Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization.” ACS Nano 7, no. 10 (October 22, 2013): 8990–9000. https://orcid.org/0000-0002-6420-1616 https://orcid.org/0000-0001-6495-5197 https://orcid.org/0000-0003-0946-0401 en_US http://dx.doi.org/10.1021/nn4035266 ACS Nano Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use. application/pdf American Chemical Society (ACS) Prof. Anikeeva via Angie Locknar |
| spellingShingle | Chen, Ritchie Anikeeva, Polina Olegovna Christiansen, Michael Gary Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization |
| title | Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization |
| title_full | Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization |
| title_fullStr | Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization |
| title_full_unstemmed | Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization |
| title_short | Maximizing Hysteretic Losses in Magnetic Ferrite Nanoparticles via Model-Driven Synthesis and Materials Optimization |
| title_sort | maximizing hysteretic losses in magnetic ferrite nanoparticles via model driven synthesis and materials optimization |
| url | http://hdl.handle.net/1721.1/92978 https://orcid.org/0000-0002-6420-1616 https://orcid.org/0000-0001-6495-5197 https://orcid.org/0000-0003-0946-0401 |
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