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Gram bean extract-mediated synthesis of Fe3O4 nanoparticles for tuning the magneto-structural properties that influence the hyperthermia performance
Shivaji Univ, Kolhapur, Maharashtra, India; Shivaji Univ, Kolhapur, Maharashtra, India.ORCID iD: 0000-0002-3063-2178
Mid Sweden University, Faculty of Science, Technology and Media, Department of Natural Sciences. DY Patil Univ, Kolhapur, Maharashtra, India.
Univ Mumbai, Mumbai, Maharashtra, India.
Shivaji Univ, Kolhapur, Maharashtra, India.
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2019 (English)In: Journal of the Taiwan Institute of Chemical Engineers / Elsevier, ISSN 1876-1070, E-ISSN 1876-1089, Vol. 95, p. 357-368Article in journal (Refereed) Published
Abstract [en]

A green synthesis of biocompatible magnetite (Fe3O4) nanoparticles (MNPs) using a combination of urea (U) and gram-bean extract (GBE, Cicer arietinum L.) is reported. The particle size of similar to 13 nm and highly stable magnetite phase is observed for GBE-U mediated MNPs. On the other hand, the MNPs synthesized using either U or GBE shows larger particle size and uneven size distribution. Interestingly, the sample with particle size similar to 13 nm shows optimum heat generation capacity (measured in specific absorption rate, i.e., SAR) near to the therapeutic temperature (43 degrees C) with least-variance. To investigate the influence of various factors such as variation in MNPs weight concentration (W-t), applied alternating magnetic field (AMF), saturation magnetization (M-s), magnetization rate (R-m), etc. on SAR, a multiple linear regression model (MLRM) is used. The study reveals a positive correlation of SAR with R-m, and AMF values while the negative correlation with M-s and W-t. Ultimately, the present green synthesis is the affordable approach for preparing stable and tiny MNPs. Moreover, MLRM is found to be a useful theoretical tool for understanding the influence of MNPs on hyperthermia performance. 

Place, publisher, year, edition, pages
2019. Vol. 95, p. 357-368
Keywords [en]
Biosynthesis, Hyperthermia, Iron oxide nanoparticles, Pectin, Regression model
Identifiers
URN: urn:nbn:se:miun:diva-35810DOI: 10.1016/j.jtice.2018.07.039ISI: 000458942000039OAI: oai:DiVA.org:miun-35810DiVA, id: diva2:1297110
Available from: 2019-03-19 Created: 2019-03-19 Last updated: 2019-03-19Bibliographically approved

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Phadatare, Manisha R.

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