INVESTIGATION of STRUCTURAL EFFECTS on the AC MAGNETIC PROPERTIES of IRON OXIDE NANOPARTICLES by ERIC C. ABENOJAR Submitted in P
INVESTIGATION OF STRUCTURAL EFFECTS ON THE AC
MAGNETIC PROPERTIES OF IRON OXIDE NANOPARTICLES
by
ERIC C. ABENOJAR
Submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
Dissertation Adviser: Prof. Anna Cristina S. Samia
Department of Chemistry
CASE WESTERN RESERVE UNIVERSITY
May, 2018
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CASE WESTERN RESERVE UNIVERSITY
SCHOOL OF GRADUATE STUDIES
We hereby approve the thesis/dissertation of
ERIC C. ABENOJAR
Candidate for the degree of Doctor of Philosophy*.
Committee Chair
Prof. Geneviève Sauvé (Department of Chemistry, CWRU)
Committee Member
Prof. Clemens Burda (Department of Chemistry, CWRU)
Committee Member
Prof. Carlos E. Crespo-Hernández (Department of Chemistry, CWRU)
Committee Member
Prof. Anna Cristina S. Samia (Department of Chemistry, CWRU)
Committee Member
Prof. João Maia (Department of Macromolecular Science and Engineering, CWRU)
Date of Defense December 18, 2017
*We also certify that written approval has been obtained for any proprietary material contained therein.
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Table of Contents
List of Tables ...... viii
List of Figures ...... ix
List of Symbols and Abbreviations...... xxii
Acknowledgements ...... xxvi
Abstract ...... xxviii
Chapter 1. Introduction to Nanomaterials ...... 1
1.1 General Introduction ...... 1
1.2 Fundamentals of Nanomagnetism...... 2
1.2.1 Magnetic materials ...... 2
1.2.2 Superparamagnetism ...... 2
1.2.3 Iron Oxide Spinel Ferrites ...... 5
1.3 Synthetic Methods ...... 6
1.4 Magnetic Hyperthermia ...... 7
1.4.1 Heating of Magnetic Nanoparticles in an AC field ...... 8
1.4.2 Magnetic Hyperthermia Measurement ...... 11
1.4.2.1 Calorimetric Method ...... 11
1.4.2.2 Magnetometric Method ...... 12
1.5 Magnetic Particle Imaging (MPI) ...... 13
1.5.1 Emerging Research Directions: Magnetic Imaging Guided–Hyperthermia .....15
1.6 Nanoparticle Characterization Methods ...... 17
1.6.1 Spectroscopic methods...... 17
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1.6.1.1 Fourier-Transform Infrared Spectroscopy (FT-IR) ...... 17
1.6.1.2 Atomic Absorption Spectroscopy (AAS) ...... 18
1.6.1.3 Energy Dispersive X-ray (EDS) ...... 19
1.6.2 Electron Microscopy Methods ...... 19
1.6.2.1 Transmission Electron Microscopy (TEM) ...... 19
1.6.2.2 Scanning Electron Microscopy (SEM) ...... 20
1.6.3 Thermogravimetric Analysis (TGA) ...... 21
1.6.4 Powder X-ray Diffraction (PXRD) ...... 21
1.6.5 Dynamic Light Scattering (DLS) ...... 22
1.7 References ...... 23
Chapter 2. Size and Matrix Effect on the Magnetic Hyperthermia Properties of Iron Oxide
Magnetite Nanoparticles ...... 29
2.1 Introduction ...... 29
2.2 Experimental Methods ...... 32
2.2.1 Materials and Reagents ...... 32
2.2.2 Nanoparticle Synthesis and Nanocomposite Fabrication ...... 32
2.2.2.1 Synthesis of Magnetite Nanoparticles ...... 32
2.2.2.2 Fabrication of Fe3O4-PE Nanocomposite Films ...... 34
2.2.3 Fe3O4 NP Characterization ...... 34
2.2.4 Fe3O4-PE Nanocomposite Characterization ...... 35
2.2.5 Fe3O4 NP and Fe3O4-PE Nanocomposite Magnetic Characterization ...... 35
2.2.6 Magnetic Hyperthermia Measurements ...... 36
2.3 Results and Discussion ...... 37
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2.3.1 Structural and Magnetic Properties of the Synthesized Fe3O4 NPs ...... 37
2.3.2 Structural and Magnetic Properties of the Fe3O4-PE Nanocomposites ...... 39
2.3.3 Magnetic Hyperthermia Properties ...... 41
2.3.3.1 Effect of NP Size and Immobilization ...... 41
2.3.3.2 Effect of Varying the Alternating Current (AC) Magnetic Field
Amplitude ...... 45
2.4 Conclusions ...... 46
2.5 References ...... 47
Chapter 3. Thermoresponsive Magnetic Hydrogel Nanocomposite for Combined Thermal and D-Amino Acid Assisted Biofilm Disruption...... 51
3.1 Introduction ...... 51
3.2 Materials and Methods ...... 56
3.2.1 Materials and Reagents ...... 56
3.2.2 Synthesis of Iron Oxide (Fe3O4) nanoparticles ...... 56
3.2.2.1 Synthesis of Spherical Iron Oxide Nanoparticles ...... 56
3.2.2.2 Synthesis of Cubic Iron Oxide Nanoparticles ...... 57
3.2.3 Synthesis of Glycol Chitin Hydrogel ...... 58
3.2.4 Preparation of Magnetic Hydrogels Using Water Soluble Fe3O4
Nanoparticles ...... 58
3.2.5 Biofilm Formation and Dispersal Assays ...... 59
3.2.6 Magnetic Hyperthermia Aided Biofilm Dispersal Assays ...... 59
3.2.7 Cell Viability Assays ...... 60
3.2.8 Statistical analyses ...... 60
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3.3 Results and Discussion ...... 61
3.3.1 Dose Dependent Effect of Antibiotics on S. Aureus Biofilm Disruption ...... 61
3.3.2 Concentration Dependent Effect of Amino Acids on S. Aureus Biofilm
Disruption ...... 63
3.4 Magnetic Hyperthermia Performance Evaluation of Fe3O4 Spheres and Cube for
Biofilm Disruption ...... 69
3.5 Preparation of Magnetic Thermoresponsive Glycol Chitin
Hydrogel ...... 73
3.6 Biofilm Disruption Using D-AA Loaded Magnetic Thermoresponsive Glycol Chitin
Hydrogel ...... 77
3.7 Conclusions ...... 79
3.8 References ...... 79
Chapter 4. Tuning the Magnetic Relaxation Processes in Superparamagnetic Magnetite-
Polyethylene Nanocomposites ...... 84
4.1 Introduction ...... 84
4.2 Experimental Section ...... 88
4.2.1 Materials ...... 88
4.2.2 Characterization Methods ...... 88
4.2.3 Synthesis of Iron Oleate Precursor Compound ...... 89
4.2.4 Synthesis of Iron Oxide Nanoparticles ...... 89
4.2.5 Preparation of Magnetite-Polyethylene Nanocomposites (MPE-NCs) ...... 90
4.2.6 Magnetic Particle Spectrometer (MPS) Design Details ...... 90
4.2.7 Sample Preparation for MPS Measurements ...... 91
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4.3 Results and Discussion ...... 92
4.3.1 Matrix-dependent MPS Behavior of Nanoparticles Using System Matrix
Approach ...... 92
4.3.2 Wear debris MPS behavior of MPE-NCs using x-space approach MPI ...... 98
4.4 Conclusions ...... 100
4.5 References ...... 100
Chapter 5. Research Outlook ...... 103
5.1 References ...... 104
Bibliography ...... 105
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List of Tables
Table 2.3.1.1. Summary of Rietveld analysis for the different sizes of NPs showing the
percentage of magnetite and maghemite in each sample...... 38
Table 2.3.3.1.1. Table showing the correlation of the calculated SAR values (averaged
out from triplicate magnetic hyperthermia measurements; [Fe]: 3.75
mg/mL; H = 30 kA/m; f = 380 kHz) with the particle size for Fe3O4 NPs
that are mobile in solution and immobilized in the Fe3O4-PE
nanocomposite films...... 43
Table 2.3.3.2.1. Table showing the correlation of the calculated SAR values (averaged out
from triplicate magnetic hyperthermia measurements in W/g) with the
particle size for Fe3O4 NPs that are mobile in solution (a) and immobilized
in the Fe3O4-PE nanocomposite films (b). The hyperthermia measurements
were performed at a total iron concentration of 3.75 mg/mL, f = 380 kHz
and varying field amplitudes (i.e. 15, 30, 45, 60 kA/m)...... 46
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List of Figures
Figure 1.2.1.1. Representation of the energy barriers governing single domain particles.
...... 4
Figure 1.3.1. A thermal decomposition set-up used to synthesize iron oxide nanoparticles
using iron oleate as the precursor...... 6
Figure 1.3.2. LaMer diagram showing the mechanism of nanocrystal growth...... 7
Figure 1.4.2.1. Different approaches to evaluate the heating efficiency of magnetic
nanoparticles: (a) calorimetric method, wherein the sample is placed in the
middle of an AC field generating coil and an insulator is used to minimize
heat losses to the surroundings (left). The SLP or SAR is proportional to
the initial slope of the change in temperature vs. time curve (right) and (b)
magnetometric method measures the AC susceptibility of the sample with
(left) schematic showing the detection principle using pick-up coils for
high frequency hysteresis loop measurements, M-H hysteresis loops
obtained from AC magnetometry measurements wherein the SLP is
proportional to the area of the hysteresis curve...... 13
Figure 2.3.1.1. TEM micrographs and respective size distribution measurement of the
Fe3O4 nanoparticles showing good size monodispersity...... 38
Figure 2.3.1.2. Powder XRD patterns for the different NP samples (a), the corresponding
field-dependent magnetization measured at 5 K (b), and temperature-
dependent magnetization at 7.96 kA/m of the synthesized NPs (c). Plot
showing the size dependence of the blocking temperature, TB, obtained
ix
from M(T) shown in (c) and the calculated anisotropy, K, for the Fe3O4 NP
samples (d)...... 38
Figure 2.3.2.1. Differential scanning calorimetry (DSC) plot obtained for the ultra-high
molecular weight polyethylene used in the fabrication of the Fe3O4-PE
nanocomposites; the indicated temperature is the evaluated melting
temperature for the polymer. The DSC measurements were performed in
air atmosphere at a heating rate of 10 °C/min...... 40
Figure 2.3.2.2. TEM image of a representative Fe3O4-PE nanocomposite containing Fe3O4
NPs with an average diameter of 19 nm (a) and the comparison of their
field-dependence magnetization measured at 5 K (b)...... 41
Figure 2.3.2.3. Thermogravimetric analysis (TGA) curves obtained for the Fe3O4-PE
nanocomposite films fabricated with the Fe3O4 NPs of different sizes. The
TGA measurements were performed by heating the samples from 25 to 800
°C at a heating rate of 10 °C/min under air atmosphere...... 41
Figure 2.3.3.1.1. Plot comparing the calculated SAR values of the freely moving Fe3O4
NPs in solution and the immobilized nanoparticles in the Fe3O4-PE
nanocomposite films as a function of NP size. All the hyperthermia
measurements were performed at a concentration of 3.75 mg Fe/mL, H =
30 kA/m and f = 380 kHz, and the reported SAR values were averaged
from three measurement trials; the details of the statistical analysis is
available in Table 2.3.3.1.1...... 42
Figure 2.3.3.1.2 TEM image of Fe3O4 NPs deposited on a Cu grid in the presence of a
magnetic field...... 43
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Figure 2.3.3.2.1. Plots showing the relation of the calculated SAR values as a function of
nanoparticles size measured at different amplitudes (15, 30, 45, 60 kA/m)
for Fe3O4 NPs dispersed in p-xylene (a), and those embedded in the Fe3O4-
PE nanocomposite films submerged in p-xylene (b). All the hyperthermia
measurements were performed at a concentration of 3.75 mg Fe/mL and f
= 380 kHz, and the reported SAR values were averaged from three
measurement trials; the details of the statistical analysis is available in
Table 2.3.3.2.1...... 45
Figure 3.3.1.1. Concentration dependent effects of vancomycin against biofilm disruption
of S. aureus at concentrations ranging from 2 ppm to 256 ppm. Bacterial
cultures made overnight were diluted to an OD595 of 0.1 and further diluted
100x (~105 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5%
glucose) and each well was filled with 2 mL of the diluted bacterial
solution and incubated at 37 oC for 24 h. Biofilm disruption assay was
evaluated by comparison of the biofilm biomass relative to that of the
positive control by measuring the absorbance of solubilized crystal violet
stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the
difference of means is statistically significant at p < 0.05 while NS
indicates that difference of means is not statistically significant at p < 0.05.
...... 62
Figure 3.3.1.2. Concentration dependent effects of bacitracin against biofilm disruption of
S. aureus at concentrations ranging from 32 ppm to 625 ppm. Bacterial
cultures made overnight were diluted to OD595 of 0.1 and further diluted
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100x (~105 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5%
glucose) and each well was filled with 2 mL of the diluted bacterial
solution and incubated at 37 oC for 24 h. Biofilm disruption assay was
evaluated by comparison of the biofilm biomass relative to that of the
positive control by measuring the absrbance of solubilized crystal violet
stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the
difference of means is statistically significant at p < 0.05 while NS
indicates that difference of means is not statistically significant at p < 0.05..
...... 62
Figure 3.3.2.1. Concentration dependent effects of individual D-amino acids against
biofilm disruption of S. aureus. Bacterial cultures made overnight were
5 diluted to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in
modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was
filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for
24 h. Biofilm dispersive activity of individual D-amino acids D-
methionine (D-met) (D-50 mM), D-tryptophan (D-trp) (54 mM), D-
tyrosine (D-tyr) (2.5 mM), and D-phenylalanine (D-phe) (143.5 mM) were
evaluated. Biofilm disruption assay was evaluated by comparison of the
biofilm biomass relative to that of the positive control by measuring the
absorbance of solubilized crystal violet stain at 595 nm following 24 h
treatment. An asterisk (*) indicates that the difference of means is
statistically significant at p < 0.05 while NS indicates that difference of
means is not statistically significant at p < 0.05...... 65
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Figure 3.3.2.2. Concentration dependent effects of D-amino acid mixture of D-trp, D-tyr,
and D-phe against biofilm disruption of S. aureus. Bacterial cultures made
5 overnight were diluted to an OD595 of 0.1 and further diluted 100x (~10
CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each
well was filled with 2 mL of the diluted bacterial solution and incubated at
37 oC for 24 h. Dispersive activity of D-amino acid mixture was evaluated
using a 1:22:57 molar ratio of D-tyr, D-trp, and D-phe, respectively.
Biofilm disruption assay was evaluated by comparison of the biofilm
biomass relative to that of the positive control by measuring the absorbance
of solubilized crystal violet stain at 595 nm following 24 h treatment. An
asterisk (*) indicates that the difference of means is statistically significant
at p < 0.05 while NS indicates that difference of means is not statistically
significant at p < 0.05...... 65
Figure 3.3.2.3. Concentration dependent effects of L-amino acid mixture of L-trp, L-tyr,
and L-phe against biofilm disruption of S. aureus. Bacterial cultures made
5 overnight were diluted to an OD595 of 0.1 and further diluted 100x (~10
CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each
well was filled with 2 mL of the diluted bacterial solution and incubated at
37 oC for 24 h. Dispersive activity of L-amino acid mixture was evaluated
using a 1:22:57 molar ratio of L-tyr, L-trp, and L-phe, respectively. Biofilm
disruption assay was evaluated by comparison of the biofilm biomass
relative to that of the positive control by measuring the absorbance of
solubilized crystal violet stain at 595 nm following 24 h treatment. An
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asterisk (*) indicates that the difference of means is statistically significant
at p < 0.05 while NS indicates that difference of means is not statistically
significant at p < 0.05...... 66
Figure 3.3.2.4. Concentration dependent effects of a mixture of 4 D-amino acids D-trp, D-
tyr, D-phe, and D-met against biofilm disruption of S. aureus. Bacterial
cultures made overnight were diluted to an OD595 of 0.1 and further diluted
100x (~105 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5%
glucose) and each well was filled with 2 mL of the diluted bacterial
solution and incubated at 37 oC for 24 h. Dispersive activity of D-amino
acid mixture was evaluated using a 1:20:22:57 molar ratio of D-tyr, D-met,
D-trp, and D-phe, respectively. Biofilm disruption assay was evaluated by
comparison of the biofilm biomass relative to that of the positive control
by measuring the absorbance of solubilized crystal violet stain at 595 nm
following 24 h treatment. An asterisk (*) indicates that the difference of
means is statistically significant at p < 0.05 while NS indicates that
difference of means is not statistically significant at p < 0.05...... 66
Figure 3.3.2.5. Biofilm disruption assay using 200 mM vancomycin (prepared at pH 2 and
7) and NeutroPhase (0.01 % HClO). Bacterial cultures made overnight
5 were diluted to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL)
in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was
filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for
24 h. Biofilm disruption assay was evaluated by comparison of the biofilm
biomass relative to that of the positive control by measuring the absorbance
xiv
of solubilized crystal violet stain at 595 nm following 24 h treatment. An
asterisk (*) indicates that the difference of means is statistically significant
at p < 0.05 while NS indicates that difference of means is not statistically
significant at p < 0.05...... 67
Figure 3.3.2.6. Time dependent in vitro cytotoxicity assay demonstrates that D- and L-
amino acid mixtures (D-tyr: D-trp: D-phe in 1:22:57 molar ratio,
respectively) have limited toxicity up to 2 h exposure but are toxic at 24 h.
On the other hand, vancomycin at a concentration of 200 mM and
NeutroPhase show toxicity at both 2 and 24 h incubation. Viability of HeLa
cells was determined by exposure to media supplemented D- or L-amino
o acids for the specified time (0.5 – 24 h) at 37 C in 5% CO2. Cell viability
was determined using the Presto Blue assay by measuring absorbance at
570 and 600 nm. The percent viability is represented relative to non-treated
controls...... 67
Figure 3.3.2.7. Time dependent biofilm disruption assay using 200 mM of the D-amino
acid mixture of D-trp, D-tyr, and D-phe. Biofilm disruption assay was
evaluated by comparison of the biofilm biomass relative to that of the
positive control by measuring the absorbance of solubilized crystal violet
stain at 595 nm following treatment...... 68
Figure 3.3.2.8. Combination treatments showed statistically higher biofilm disruption
using a combination of D-amino acids compared to L-amino acids
(tyr:trp:phe in 1:22:57 molar ratio, respectively). Bacterial cultures made
5 overnight were diluted to an OD595 of 0.1 and further diluted 100x (~10
xv
CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each
well was filled with 2 mL of the diluted bacterial solution and incubated at
37 oC for 24 h. Biofilm disruption assay was evaluated by comparison of
the biofilm biomass relative to that of the positive control by measuring
the absorbance of solubilized crystal violet stain at 595 nm following 2 h
treatment. An asterisk (*) indicates that the difference of means is
statistically significant at p < 0.05 while NS indicates that difference of
means is not statistically significant at p < 0.05...... 68
Figure 3.3.2.9. Biofilm disruption assay using 200 mM vancomycin (prepared at pH 2 and
7) and NeutroPhase (0.01 % HClO). Bacterial cultures made overnight
5 were diluted to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL)
in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was
filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for
24 h. Biofilm disruption assay was evaluated by comparison of the biofilm
biomass relative to that of the positive control by measuring the absorbance
of solubilized crystal violet stain at 595 nm following 2 h treatment. An
asterisk (*) indicates that the difference of means are statistically
significant at p < 0.05 while NS indicates that difference of means are not
statistically significant at p <0.05...... 69
Figure 3.4.1. TEM images of Fe3O4 spheres (A) and cubes (D) evaluated for magnetic
hyperthermia aided disruption of biofilms. Corresponding PXRD patterns of
Fe3O4 spheres (B) and cubes (E) showing similar PXRD peaks and pattern as the
reference. DLS measurements of the Fe3O4 spheres (C) and cubes (F) before
xvi
(oleic acid) and after phase conversion to aqueous phase (silane) showing a slight
increase in the hydrodynamic diameter (DH) for both particle shapes...... 70
Figure 3.4.2. Magnetic hyperthermia measurements were used to calculate for the heating
efficiency using the parameter specific absorption rate (SAR in W/g) of the
synthesized Fe3O4 spheres and cubes at different concentrations of Fe (250
– 750 ug Fe/mL) and variable amplitude (1 – 5 kA/m) at a fixed frequency
of 380 kHz. (A) SAR values of Fe3O4 spheres and cubes dispersed in
toluene showing the cubes having much higher heating efficiency
compared to the sphere. (B) A similar observation was observed for Fe3O4
spheres and cubes dispersed in water with the spheres especially showing
minimal rise in temperature ...... 71
Figure 3.4.3. Magnetic hyperthermia heating profiles of the synthesized Fe3O4 spheres (A–
C) and cubes (D–F) dispersed in toluene at different concentrations of Fe
(250 – 750 ug Fe/mL) and variable amplitude (H) (1 – 5 kA/m) at a fixed
frequency (f) of 380 kHz. Higher temperatures were reached with
increasing concentration and amplitude...... 71
Figure 3.4.4. Magnetic hyperthermia heating profile of the synthesized Fe3O4 spheres (A–
C) and cubes (D–F) dispersed in water at different concentrations of Fe
(250 – 750 ug Fe/mL) and variable amplitude (H) (1 – 5 kA/m) at a fixed
frequency (f) of 380 kHz. Higher temperatures were reached with
increasing concentration and amplitude...... 72
Figure 3.4.5. Time dependent in vitro cytotoxicity show that the synthesized Fe3O4 spheres
and cubes are non-toxic at 2 h and 24 h treatment for a concentration range
from 250 to 750 ug Fe/mL. Viability of HeLa cells was determined by
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exposure to media supplemented with the nanoparticles for the specified
o time (0.5 – 24 h) at 37 C in 5% CO2. Cell viability was determined using
the Presto Blue assay by measuring absorbance at 570 and 600 nm. The
percent viability is represented relative to non-treated controls...... 72
Figure 3.5.1. FT-IR spectroscopy analyses showing the conversion of the precursor glycol
chitosan to glycol chitin as revealed by the loss of the amine (-NH2) peak
and the presence of stronger carbonyl (-C=O) and amide (-CONH2) peaks
in glycol chitin ...... 74
Figure 3.5.2. 1H NMR spectra showing the successful conversion of (A) glycol chitosan
and (B) glycol chitin as indicated by the absence of the amine (-NH2) peak
in the spectrum for glycol chitin...... 74
Figure 3.5.3 Sol-gel transition of magnetic nanoparticles incorporated in glycol chitin
dissolved in saline (5 % wt in 0.9 % NaCl) solution showing a transition
from sol to gel at 37 oC...... 75
Figure 3.5.4 Concentration and temperature dependent sol-gel transition of magnetic
nanoparticles incorporated in different glycol chitin concentrations
(dissolved in saline, 0.9 % NaCl) as quantified by the distance travelled by
the gel. The maximum distance travelled is 28 mm, which is the length of
L...... 75
Figure 3.5.5. Concentration dependence on viscosity changes of glycol chitin solutions (1
– 5 %) as measured by rheometer in (A) over a temperature range of 15–
75 oC and (B) comparison of viscosity with changing concentration at
37 oC...... 75
xviii
Figure 3.5.6. Magnetic hyperthermia heating profile of the synthesized Fe3O4 cubes
dispersed in (A) saline solution (0.9 % NaCl) and (B) in 5 % glycol chitin
solution (hydrogel) at a concentration of 750 ug Fe/mL, an amplitude (H)
of 5 kA/m at a frequency (f) of 380 kHz...... 76
Figure 3.5.7. (A) Cumulative release of D-AA (200 mM) loaded in 5% glycol chitin
solution (hydrogel) after 2 h and 24 h. (B) Cumulative release of D-AA
(200 mM) loaded in 5% glycol chitin solution (hydrogel) after 2 h and 2 h
plus an additional ten minutes of applied magnetic field application using
750 ug Fe/mL at an amplitude (H) of 5 kA/m and a frequency (f) of 380
kHz...... 76
Figure 3.6.1. Biofilm disruption comparison of the different treatments used in this study.
Combined D-AA amino acid (200 mM) treatment for 2 h and additional
magnetic hyperthermia treatment for 10 min in a hydrogel matrix) at 750
ug Fe/mL at an amplitude (H) of 5 kA/m and a frequency (f) of 380 kHz
showed total disruption...... 77
Figure 3.6.2. Crystal violet stained samples of biofilm disruption using different treatments
shown in Figure 3.6.1...... 78
Figure 3.6.3. Time dependent in vitro cytotoxicity show that the treatments shown in
Figure 3.5.1 are non-toxic to HeLa cells for 2 h while those incorporated
with amino acids are toxic after 24 h exposure...... 78
Figure 4.3.1.1. (A) Transmission electron microscope (TEM) image of the synthesized
oleic acid-coated iron oxide nanoparticles. (Inset: Histogram showing
average diameter of 17.7 ± 0.9 nm); (B) Powder x-ray diffraction data
xix
obtained from the synthesized iron oxide nanoparticles confirming the
magnetite (Fe3O4) crystal phase; (C) Fabrication process for the
magnetite-polyethylene nanocomposite (MPE-NC) and interaction of the
resulting nanocomposites with an external FeNdB bar magnet; (D) TEM
image of a ball–milled magnetite-polyethylene nanocomposite (MPE-NC)
sample showing good particle dispersion and preservation of nanoparticle
morphology; (E) Hysteresis curves taken at 5 K for a sample containing
0.5% Fe3O4 nanoparticles mixed withpolyethylene powder (Fe3O4-NP +
PE) and a corresponding magnetite-polyethylene nanocomposite (MPE-
NC) film with the same amount of loaded magnetic nanoparticles. Both
samples exhibit similar saturation magnetization indicating that there is no
phase change during the compression molding fabrication (i.e. Ms values
for the Fe3O4-NPs with PE powder and MPE-NC were measured to be
95.5 emu/g and 90.3 emu/g respectively. The inset shows measurements
carried out at 300 K, where both samples demonstrate superparamagnetic
behavior (coercivity, Hc = 0 Oe for both samples)...... 93
Figure 4.3.1.2. Attenuated total reflectance- FTIR (ATR-FTIR) spectra of UHMWPE
compressed at different temperatures; highlighted area corresponds to the
C=O stretching frequency (1710-1740 cm-1) – its presence is indicative of
oxidative degradation of the polymer...... 94
Figure 4.3.1.3. (A) Schematic diagram of the magnetic particle spectrometer (MPS) used
in this study. (B) Comparison of the MPS signal of Fe3O4-NPs in three
different media at a fixed concentration of 3.75 mg Fe/mL. (C) Measured
xx
intensity ratios of different harmonics, with signals normalized to the
respective third harmonic of each sample...... 96
Figure 4.3.1.4. (A) Field strength dependence of the MPS signal for a MPE-NC with 3.75
mg Fe/mL concentration. (B) Effect of Fe concentration on MPS signal of
MPE-NCs; signals were normalized to their respective third
harmonics...... 96
Figure 4.3.2.1. (A) MPE-NC samples after ball milling and magnet separation. (B) SEM
image of ball milled samples. (C) Size distribution of the samples based on
SEM measurement. (D) DLS size measurement and distribution of debris..
...... 98
Figure 4.3.2.2. (A) PSF distribution vs. excitation field plot of debris samples. (B) MPI
signal vs. mass of debris show a linear relationship (r2 = 0.997)...... 99
xxi
List of Symbols and Abbreviations
0D zero dimensional 1H NMR proton nuclear magnetic resonance 3D three dimensional A exchange energy AA amino acid AAS atomic absorption spectroscopy AC alternating current Ag silver Ahys area of hysteresis loop AMF alternating magnetic field ANOVA analysis of variance Ar argon ATR attenuated total reflectance Au gold C volumetric specific heat capacity c weight concentration CCD charge-coupled device c-di-AMP cyclic di-adenosine monophosphate c-di-GMP cyclic di-guanosine monophosphate (CH3)3NO trimethylamine n-oxide CF cystic fibrosis CFTR cystic fibrosis transmembrane regulator CFU colony forming unit Co cobalt CO2 carbon dioxide COD crystallographic open database Cr chromium CT computed tomography Cu copper Dc critical diameter DGC diguanylate cyclases DH hydrodynamic diameter DI deionized water DLS dynamic light scattering DMEM Dulbecco’s modified eagle medium DSC differential scanning calorimetry dT change in temperature dt change in time Dt translational diffusion coefficient
xxii
EDS energy dispersive X-ray EPM extracellular polymeric matrix EPS extracellular polymeric substances EZ Zeeman energy f frequency FBS fetal bovine serum Fe iron Fe(C5H7O2)3 iron (III) acetylacetonate Fe3O4 magnetite FeCl3 iron (III) chloride FeO wüstite FFP field free point FFR field free region FOV field of view FT-IR fourier transform infrared FWHM full width at half maximum h hour H applied field HCl hydrochloric acid HClO hypochlorous acid magnetic particle imaging guided hMPI hyperthermia HNO3 nitric acid hyp hyperthermia Hz hertz international crystallographic diffraction ICDD database ILP intrinsic loss power IONP iron oxide nanoparticle joint committee on powder diffraction JCPDS standards K Kelvin K anisotropy energy kB Boltzmann constant kg kilogram L thermal loss parameter leu leucine LOD limit of detection LOQ limit of quantitation LRT linear response theory M molar
xxiii
M mass met methionine Mg magnesium mg milligram μg microgram min minute mL milliliter μL microliter MNP magnetic nanoparticle mol mole MPE-NC magnetite polyethylene nanocomposite MPI magnetic particle imaging MPS magnetic particle spectroscopy MRI magnetic resonance imaging MRI magnetic resonance imaging Ms saturation magnetization mT millitesla Na sodium NdFeB neodymium iron boron Ni nickel nm nanometer NP nanoparticle NS non-significant oC degree Celsius Oe Oersted P power dissipation PBS phosphate buffered saline PDE phosphodiesterases PE polyethylene pen penicillin PET positron emission tomography phe phenyalanine ppm parts per million pro proline Pseudomonas aeruginosa P. aeurginosa PSF point spread function PXRD powder X-ray diffractometry p-xylene para-xylene rf radiofrequency rpm revolutions per minute s second
xxiv
SAR specific absorption rate SEM scanning electron microscopy SLP specific loss power SPECT single photon emission computed tomography SPION superparamagnetic iron oxide nanoparticles SQUID scanning quantum interference device Staphylococcus aureus S. aureus strep streptomycin T temperature t time TB blocking temperature TEM transmission electron microscopy TGA thermogravimetric analysis trp tryptophan tyr tyrosine UHMWPE ultra high molecular weight polyethylene V volume van vancomycin VH hydrodynamic volume Vs volume of solution VSM vibrating sample magnetometry β size broadening η dynamic viscosity θ angle λ wavelength γ-Fe2O3 maghemite τ effective relaxation time τ0 attempt time
τΒ Brownian relaxation time
τm measurement time τΝ Néel relaxation time μ0 permeability of free space χ" out-of-phase component χ0 actual susceptibility
xxv
To my parents, Roberto and Le Eng
Acknowledgements
I would like to express my deepest gratitude to my mentor, Prof. Anna Cristina S.
Samia, for always pushing me to strive for the best in all my academic endeavors. I am
very thankful for all the advice and guidance you have provided me throughout my stay
here in Case. I could not be any luckier. I would like to thank my committee members,
Prof. Geneviève Sauvé, Prof. Clemens Burda, Prof. Carlos E. Crespo-Hernández, and Prof.
João Maia, who have been very supportive intellectually and personally as I navigated graduate school.
It has also been very intellectually fulfilling to work with different collaborators, which has allowed me to expand my scientific knowledge: Dr. Carlos Higuera, Alison
Klika, and Jaiben George of Cleveland Clinic for the work with biofilm; Dr. Steven Chuang and Wenbin Yin of the University of Akron for helping me with the ball milling of nanocomposites; Dr. Lisa Bauer and Prof. Mark Griswold for magnetic particle relaxometry and magnetic particle imaging; Prof. João Maia for polymer characterization; and Midori Hitomi and Mei Yin for TEM assistance.
I would like to thank the people who helped me proofread my thesis: Al, Sam, Suzi, and Kelsey. I am also fortunate to have wonderful colleagues in the Samia group who I worked with everyday: Dr. Shu Situ-Lowenstein, Sameera Wickramansinghe, Dr. Michele
Pablico-Lansigan, Dr. Adriana Popa, Stella Minseon Ju, Monica Navarreto Lugo, Sarika
Uppaluri, Bueniel Kim, Hyo Joo Shin, Ava Kotvas, Zhao Yu, Adam Vianna, Jesba Bas-
Concepcion, Pawel Kraj, Yutthana Lakliang, Dean Balabanov, and Christian Petersen. I would also like to thank members of the Burda group who have been very helpful
xxvi throughout my stay: Charles Kolodziej, Dr. Christopher McCleese, Dr. Anton Kovalsky,
Dr. Xin Guo, Dr. Wei Chun Lin, Dr. Keng Chu, Maria Escamilla, and Dr. Thiago Alves.
I would also like to extend a huge thank you to my family here in Cleveland: Ryan,
Jenny, Joey, Al, Bea, Berna, Hazel, Christine, Kevin, Randy, Andrew, Nico, Carrie Ann,
Lawrence, and Adrian, for making Cleveland my home away from home. Your support has
been essential in keeping me motivated throughout this program.
Finally, I would like to thank my family: papa, mama, Pacita, Emma, Lilian, Elaine,
Kevin, Liza, and Peter, without whom this would not be possible at all.
xxvii
Investigation of Structural Effects on the AC Magnetic Properties of Iron Oxide Nanoparticles
ERIC C. ABENOJAR
Abstract
Iron oxide nanoparticles (IONPs) are widely researched due to their unique magnetic properties, biocompatibility, and potential applications as diagnostic and therapeutic agents (e.g. magnetic particle imaging and magnetic hyperthermia). In this thesis, I have investigated the effect of size, shape, and nanoparticle matrix on the alternating current (AC) magnetic field properties of IONPs. Different routes were explored in order to come up with optimized synthetic procedures to prepare nanoparticles with good size monodispersity and controllable shape. The first part of this work focuses on understanding the size dependent AC magnetic hyperthermia response of IONPs when placed in two different matrices (Chapter 2). Monodisperse spherical IONPs with different sizes (10–25 nm) were prepared and placed in solution (where the particles are free to move) and embedded in a polymer matrix (where the particles have restricted mobility).
Magnetic hyperthermia measurements were performed on these two systems at varying excitation field strengths (15–60 kA/m) at a fixed frequency (380 kHz) and their AC magnetic heating properties were measured and analyzed. The second part of this work presents the fabrication of a magnetic thermoreversible glycol chitin-based hydrogel nanocomposite loaded with D-amino acids to be used as a potential viable treatment method for biofilm disruption (Chapter 3). This method utilizes the biofilm dispersal activity of D-amino acids and the magnetic hyperthermia properties of IONPs to fully
xxviii disrupt pre-formed in vitro biofilms formed by the clinically prevalent bacteria,
Staphyloccocus aureus (S. aureus). The last part of this thesis focuses on the correlation between magnetic relaxation and magnetic particle imaging (MPI) signal generation. IONP mobility was gradually restricted by embedding the particles in matrices of increasing viscosity (solution, gel, and polymer film). This allows the exploration of MPI signal generation when Brownian relaxation of the nanoparticles is restricted. The complete suppression of Brownian relaxation in the polymer film provides a unique platform for optimizing MPI tracers. In summary, this thesis provides mechanistic insight into the AC magnetic field properties of iron oxide nanoparticles as a function of size and shape using optimized synthetic routes to prepare highly monodisperse nanoparticles with good shape.
xxix
Chapter 1. Introduction to Nanomaterials
1.1 General Introduction
Nanomaterials, with a size range between 1–100 nm, are organic and inorganic materials that have unique size-dependent properties (physical, chemical, optical, electronic, and magnetic) compared to their bulk counterparts. In addition, the possibility to tailor the properties of these materials by size tuning has propelled the field of nanotechnology to proffer rapid advancements in the field of science and technology and has led to the synthesis and use of new materials for emerging applications in medicine
(biotechnology and healthcare), information technology, transportation, electronics, energy, food safety, and environmental science.1 Specifically, magnetic nanoparticles possess unique magnetic properties (superparamagnetism, low coercivity, high magnetic susceptibility) that have been developed for different applications such as data storage, catalysis, and different biomedical applications involving diagnostic (magnetic resonance imaging (MRI), magnetic particle imaging (MPI)) and therapeutic (magnetic hyperthermia treatment, drug delivery) applications. This thesis focuses on the synthesis and development of magnetic iron oxide nanoparticle composites as a tool in potential magnetic hyperthermia and MPI biomedical applications. The magnetic properties of the nanoparticles were controlled by carefully tuning the size, shape, and matrix of the nanoparticles. The performance of the synthesized iron oxide nanoparticles were evaluated by magnetic hyperthermia and MPI measurements as well as their effectiveness in biofilm disruption. This chapter provides an introduction to nanomagnetism, nanosynthesis, and characterization of iron oxide nanoparticles.
1
1.2 Fundamentals of Nanomagnetism
1.2.1 Magnetic Materials
The origin of magnetism, or the magnetic properties of a material, arises from the
motion of electrons within the atom, resulting in a magnetic moment in the crystal lattice
of a material. Magnetic materials are characterized based on its response to a magnetic
field and are broadly classified into five different types: (1) ferromagnetic materials (e.g.
iron, cobalt, nickel) contain unpaired electrons in which the magnetic moments all line up
parallel to the applied field resulting in a large net magnetization, (2) ferrimagnetic materials (e.g. Fe3O4) have magnetic moments that line up and opposite to the magnetic
field but these magnetic moments are not equal in magnitude resulting in a net overall
magnetization, (3) antiferromagnetism occurs for materials (e.g. NiO, CoO) which have magnetic moments that line up parallel and anti-parallel to the applied field resulting in zero net magnetization, (4) diamagnetism occurs when a material (e.g. graphite, copper, silver, gold) does not have any unpaired electron spin resulting in zero magnetic moment, and (5) paramagnetism involves materials (e.g. chromium, sodium, magnesium) that have randomly oriented unpaired electrons resulting in a non-zero net magnetic moment.
1.2.2 Superparamagnetism
Magnetic materials have been used for a wide variety of applications from ancient compasses to electronic devices and to superconducting magnets used in MRI. Magnetic
nanoparticles possess unique magnetic properties compared to bulk magnets and
researchers have taken advantage of these to develop new materials for newer applications.
Bulk magnets have different magnetic domains in order to minimize its magnetostatic
2
energy. Each domain has the magnetic spins aligned in one direction. The net
magnetization of bulk magnets is then affected by the movements of these domains and the
realignment of the dipoles in response to an externally applied field. When the size of the
magnets get small (< 100 nm), a critical size is reached when it is not possible to form a domain wall and the thermal energy needed to form them does not overcome the minimization of energy obtained from domain formation. As a consequence, the magnetic material becomes single domain with uniform magnetization as demonstrated by a coherent rotation of its magnetic spins. The formation of single domain particles was first predicted in 1930 by Frenkel and Dorman2 and critical sizes for multi- to single domain particle
transition were first estimated by Kittel.3 The critical diameter ( ) for which a material
𝑐𝑐 acts as a single domain particle is given by,4 𝐷𝐷
= (1.2.2.1) 36√𝐾𝐾𝐾𝐾 2 𝐷𝐷𝑐𝑐 𝜇𝜇0𝑀𝑀𝑠𝑠 where is the anisotropy energy, is the permeability of free space, is the exchange
0 energy,𝐾𝐾 and is the saturation magnetization𝜇𝜇 of the material. 𝐴𝐴
𝑠𝑠 In 1948,𝑀𝑀 Stoner and Wohlfarth created a model for the magnetic behavior of a single
domain nanoparticle (Figure 1.2.2.1).5,6 The total energy ( ) of such a system is defined
by both the anisotropy energy ( ) and the Zeeman energy 𝐸𝐸( ):
𝐴𝐴 𝑧𝑧 𝐸𝐸 𝐸𝐸 = + = cos( ) (1.2.2.2) 2 𝐸𝐸 𝐸𝐸𝐴𝐴 𝐸𝐸𝑧𝑧 𝐾𝐾𝑉𝑉𝑉𝑉𝑉𝑉𝑉𝑉 𝜃𝜃 − 𝐻𝐻𝐻𝐻𝑀𝑀𝑠𝑠 𝜃𝜃 − 𝜙𝜙 where, is the uniaxial magnetic anisotropy, is the applied field, is the
nanoparticle volume,𝐾𝐾 is the saturation magnetization,𝐻𝐻 is the angle between 𝑉𝑉the easy
𝑀𝑀𝑠𝑠 𝜃𝜃
3
axis and the nanoparticle magnetization, and is the angle between the easy axis and the
applied magnetic field. At = 0, the energy 𝜙𝜙barrier is equivalent to .
When the size of the𝐻𝐻 nanoparticle continues to decrease, the𝐾𝐾𝐾𝐾 anisotropy energy
barrier becomes comparable to the thermal activation energy ( ~ ) and magnetic
𝐵𝐵 moment𝐾𝐾 𝐾𝐾switching becomes feasible leading to superparamagnetic𝐾𝐾𝐾𝐾 behavior.𝑘𝑘 𝑇𝑇 7 The term
superparamagnetism is due to the fact that the particles behave like paramagnets but the
magnetic moment involved is that of the whole grain unlike paramagnets where the
magnetic moment is that of an atom or a molecule. Superparamagnetism involves the
thermal activated switching of the magnetic moment of a nanoparticle.8 This occurs above
the so-called blocking temperature ( ), which is described by the equation,
𝐵𝐵 𝑇𝑇 = (1.2.2.3) ( ) 𝐾𝐾𝐾𝐾 𝑇𝑇𝐵𝐵 𝑘𝑘𝐵𝐵𝑙𝑙𝑙𝑙 𝜏𝜏𝑚𝑚⁄𝜏𝜏0
where, Ris the Boltzmann constant, is the measurement time, and is the
𝐵𝐵 𝑚𝑚 -9 0 attempt time, which𝑘𝑘 is typically assumed to be 𝜏𝜏equivalent to 10 s. For magnetite𝜏𝜏 (Fe3O4) nanoparticles, superparamagnetic behavior at room temperature typically occurs at particle sizes smaller than 30 nm in diameter.9
Figure 1.2.2.1. Representation of the energy barriers governing single domain particles.
4
1.2.3 Iron Oxide Spinel Ferrites
The magnetic nature of iron oxide based ferrite nanoparticles can be understood by
looking into the magnetic ordering in its crystal lattice. The most magnetic crystal phase
of iron oxide, magnetite (Fe3O4), belongs to a special class of ferrites called spinels, which
is represented by the general formula MFe2O4, where M can be any of the divalent metal
ions. The spinel ferrite structure can be best described as a face-centered cubic arrangement of oxygen atoms, with M2+ and Fe3+ occupying any of two different crystallographic sites.
These sites have tetrahedral or octahedral oxygen coordination, which are often termed A
and B-sites, respectively.10,11 In the unit cell, there are 8 A-sites and 16 B-sites, where the magnetic moments of the cations are aligned parallel with respect to one another. Between the A and B-sites, the magnetic ordering is anti-parallel and as there are twice as many B- sites than A-sites, there is a net moments of spins yielding ferrimagnetic ordering for the
3+ crystal. Fe3O4 has an inverse spinel structure with the tetrahedral A-sites occupied by Fe
2+ 3+ ions while the octahedral B-sites are occupied by Fe and Fe ions. Therefore, Fe3O4 has
an idealized magnetic moment of 4 per formula unit.
𝐵𝐵 One way to tune the magnetism𝜇𝜇 of spinel ferrites is to dope the system with non-
2+ magnetic Zn ions resulting in a mixed ferrite phase such as (ZnxFe1-x)Fe2O4. When
2+ 0.4, the Zn ions occupying the tetrahedral A-sites can lower the antiferromagnetic𝑥𝑥 ≤ coupling interactions between the Fe3+ ions in the A and B sites resulting in a net increase
in magnetization for the Zn-doped system.12 At higher Zn2+ doping levels, the
antiferromagnetic coupling interactions between Fe3+ ions in each B-site become dominant
and net magnetization decreases.12
5
1.3 Synthetic Methods
Nanomaterial synthesis is generally performed using either the top-down or
bottom-up approach. Various synthetic methods have been developed with regard to the
synthesis of iron oxide nanoparticles in order to produce nanoparticles with high yield,
controllable shape, monodisperse size distribution, good stability, and improved
biocompatibility. Common methods employed include ball milling, co-precipitation,
aerosol/vapor technology, microemulsion, hydrothermal, and sonochemical syntheses.13,14
While these methods can produce high yields of iron oxide nanoparticles, they suffer from
poor size distribution and shape as well as complicated surface chemistries.13 In light of
this, the thermal decomposition approach was developed to produce spinel ferrite
nanoparticles with very narrow size distribution, excellent shape control, and very high
yields. This method involves the use of metal precursors in the presence of surfactants (acts
as ligands) and organic solvents with high boiling points in an inert atmosphere (Figure
1.3.1). The shape and size distribution can be carefully controlled and tuned by varying
different aspects of the synthesis such as the surfactant, solvent, heating rate, as well as the
precursor.
Figure 1.3.1. A thermal decomposition set-up used to synthesize iron oxide nanoparticles using iron oleate as the precursor.
6
The thermal decomposition method involves the formation of nanocrystals
consisting of two stage, nucleation and crystal growth, that follows the LaMer model
(Figure 1.3.2).15,16 In this model, monomers are generated by the chemical reaction of the
precursors that were initially dissolved in the appropriate solvents. As the monomer
concentration increases to a supersaturation level, nuclei is generated by monomer self- nucleation and aggregation. These monomers then continuously aggregate on pre-existing nuclei leading to nanocrystal growth, which is then terminated when the concentration of the monomers goes down below a critical level.
Figure 1.3.2. LaMer diagram showing the mechanism of nanocrystal growth.
1.4 Magnetic Hyperthermia
The following section (1.4) is adapted with permission from Abenojar, E.C.;
Wickramasinghe, S.; Bas-Concepcion, J.; Samia, A. C. S. Structural effects on the magnetic hyperthermia properties of iron oxide nanoparticles. Prog. Nat. Sci. 2016, 26,
440-448. Copyright 2016 Chinese Materials Research Society.
7
Iron oxide nanoparticles are widely investigated due to their tunable magnetic
properties and potential as diagnostic (i.e. as magnetic resonance imaging contrast agent
and magnetic particle imaging tracer) and therapeutic (e.g. drug and gene delivery,
hyperthermia) agents.17–20 Upon excitation with an AC field, these unique materials can
transform electromagnetic energy to heat, and the heat generated can be utilized to destroy
cancer cells or pathogenic microbes. In magnetic hyperthermia, the heating can occur by
any of the three mechanisms: (1) eddy current heating due to the effects of induction from
the application of an alternating pulsed magnetic field; (2) frictional heating induced by the
interaction between the nanoparticles and the surrounding medium, and (3) relaxation and
hysteretic losses of the magnetic nanoparticles.21
The use of iron oxide nanoparticles for magnetic hyperthermia treatment of cancers
was first demonstrated by Gilchrist et al. in 1957.22 Following this seminal work, various
groups have investigated the important operational parameters to effectively carry out the
use of magnetic hyperthermia in cancer therapy.23–25 In 2004, the first clinical magnetic
hyperthermia treatment system was developed at Charité – Medical University of Berlin26
and a few years later, Magforce® obtained European regulatory approval to treat patients
with brain tumor using magnetic hyperthermia.27 Over the years, the utility of magnetic
hyperthermia has been extended to other applications including heat triggered drug
delivery.28–31 biofilm inactivation,32,33 and fabrication of smart heat responsive materials.34
1.4.1 Heating of Magnetic Nanoparticles in an AC field
Various models and experimental data have been employed to better understand the
heating process in magnetic hyperthermia.5,35–39 In 2002, Rosensweig developed the linear response theory (LRT) to explain the heating of colloidal magnetic fluids subjected to an
8
alternating magnetic field.37 In his formulation, it was assumed that the heat generation was
only due to the rotational relaxation of non-interacting single domain nanoparticles, and
the magnetization of the nanoparticles varies linearly with the applied magnetic field. From
the LRT model, an expression for the power dissipation ( ) was derived as follows:37
𝑃𝑃 = (1.4.1.1) ′′ 2 𝑃𝑃 𝜇𝜇0𝜋𝜋𝜒𝜒 𝑓𝑓𝐻𝐻 where, and are the amplitude and frequency of the AC magnetic field,
respectively, 𝐻𝐻 represents𝑓𝑓 the permeability of free space, and " is the out-of-phase
0 component of𝜇𝜇 the colloidal magnetic fluid AC susceptibility. In turn,𝜒𝜒 " can be expressed as: 𝜒𝜒
= (1.4.1.2) ( ) ′′ 𝜔𝜔𝜔𝜔 2 𝜒𝜒 1+ 𝜔𝜔𝜔𝜔 𝜒𝜒0 where, = 2 , is the actual susceptibility, and is the effective relaxation
0 time. The effective𝜔𝜔 relaxation𝜋𝜋𝜋𝜋 𝜒𝜒 time is dependent on the collective𝜏𝜏 contributions of both
Néel and Brownian relaxation processes. Néel relaxation involves the internal rotation of the magnetic moment and has a characteristic time, , which is expressed as:
𝑁𝑁 𝜏𝜏 ( ) = ( ) 𝐾𝐾𝐾𝐾 (1.4.1.3) 𝜋𝜋 exp 𝑘𝑘𝐵𝐵𝑇𝑇 𝜏𝜏𝑁𝑁 2 𝜏𝜏0 𝐾𝐾𝐾𝐾 �𝑘𝑘𝐵𝐵𝑇𝑇 On the other hand, Brownian relaxation pertains to the physical rotation of the
magnetic nanoparticle itself and the characteristic relaxation time, , is represented in the
𝐵𝐵 following equation: 𝜏𝜏
= (1.4.1.4) 3𝜂𝜂𝑉𝑉𝐻𝐻 𝜏𝜏 𝐵𝐵 𝑘𝑘𝐵𝐵𝑇𝑇 where, is the dynamic viscosity and is the hydrodynamic volume.
𝜂𝜂 𝑉𝑉𝐻𝐻 9
The effective relaxation time can then be expressed as:
= (1.4.1.5) 𝜏𝜏𝑁𝑁𝜏𝜏𝐵𝐵 𝜏𝜏 𝜏𝜏𝑁𝑁+𝜏𝜏𝐵𝐵 Substituting equation 1.4.1.4 into equation 1.4.1.3 results in an expanded form of the power dissipation expression:
= [ ] (1.4.1.6) 2 2𝜋𝜋𝜋𝜋𝜋𝜋 2 𝑃𝑃 𝜇𝜇0𝜋𝜋 𝜒𝜒0𝑓𝑓𝐻𝐻 �1+ 2𝜋𝜋𝜋𝜋 𝜋𝜋 � which showcases the dependence of the heat generation process with the frequency and amplitude of the applied AC field, and the magnetic nanoparticle relaxation processes.
The heating efficiency is represented by the specific loss power (SLP) also referred to as the specific absorption rate (SAR), which is defined as the ratio of the heat power dissipated and the mass of the magnetic nanoparticles, :
𝑀𝑀𝑀𝑀𝑀𝑀 𝑚𝑚 = (1.4.1.7) 𝑃𝑃 𝑆𝑆 𝑆𝑆𝑆𝑆 𝑚𝑚𝑀𝑀𝑀𝑀𝑀𝑀 One limitation of the SAR representation is its dependence with , which makes 2 direct comparison of reported literature values difficult owing to the 𝐻𝐻variations in the applied AC field conditions. To overcome this issue, the intrinsic loss power ( ) can be calculated whereby the SAR is normalized to the AC field strength and frequency:𝐼𝐼𝐼𝐼𝐼𝐼 40
= (1.4.1.8) 𝑆𝑆𝑆𝑆𝑆𝑆 2 𝐼𝐼𝐼𝐼 𝐼𝐼 𝑓𝑓 𝐻𝐻 The ILP representation, however, is only applicable at low field strengths and low frequency AC excitations.
10
1.4.2 Magnetic Hyperthermia Measurement
1.4.2.1 Calorimetric Method
The calorimetric approach is the most commonly adapted method in evaluating the
magnetic hyperthermia properties of magnetic nanoparticles (Figure 1.4.2.1). In this
method, the temperature increase in the sample is recorded over a period of time as the
magnetic nanoparticles are exposed to an AC field of a particular amplitude and frequency
(Figure 1.4.2.1a). A fiber optic temperature probe is typically used in conjunction with a
magnetic induction heating system consisting of a water cooled coil that is connected to a
high power generator. Samples are placed in a thermally insulated container to avoid
heat loss to 𝑟𝑟the𝑟𝑟 environment during measurement, and the SAR is calculated from the temperature derivative over time at instant = 0 as,
𝑡𝑡 = (1.4.2.1) 𝐶𝐶𝑉𝑉𝑠𝑠 𝑑𝑑𝑑𝑑 𝑆𝑆𝑆𝑆𝑆𝑆 𝑚𝑚𝑀𝑀𝑀𝑀𝑀𝑀 ∙ 𝑑𝑑𝑑𝑑�𝑡𝑡=0
where, is the volumetric specific heat capacity of the sample solution, is the
𝑠𝑠 sample volume,𝐶𝐶 is the mass of the magnetic material, and / is the initial 𝑉𝑉slope of
the temperature versus𝑚𝑚 time curve. 𝑑𝑑𝑑𝑑 𝑑𝑑𝑑𝑑
Evaluation of the heating efficiency using the calorimetric method is ideal when
measurements are performed under adiabatic conditions where there is no heat exchange
between the sample and the surroundings.41 To eliminate conduction, convection, and
thermal radiation heat losses, high vacuum conditions and adiabatic shields are used during
measurements.42 However, such systems are costly and difficult to set-up and operate;
hence, adiabatic systems are rarely used in calorimetric magnetic hyperthermia
measurements.41–43
11
On the other hand, the heating efficiency evaluated using non-adiabatic systems shows an average decrease of about 21 % in the measured SAR value.42 However, non- adiabatic systems are commonly adopted because of the quick measurement time and ease of operation. By taking into account all the thermal losses from a non-adiabatic set-up it can also be a reliable approach to measure the heating efficiency of magnetic NPs.44 In a
non-adiabatic system, the heating curve starts to drop as higher temperatures are reached
due to thermal losses (Figure 1.4.2.1a). Wildeboer et al. proposed an alternative method to
better evaluate the SAR by adding a thermal loss parameter, , which can be estimated by
determining the slope for multiple temperatures along the heating𝐿𝐿 curves.44
= 𝑑𝑑𝑑𝑑 (1.4.2.2) �𝐶𝐶𝑉𝑉𝑠𝑠 𝑑𝑑𝑑𝑑+𝐿𝐿∆𝑇𝑇� 𝑆𝑆𝑆𝑆𝑆𝑆𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐𝑐 𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠𝑠 𝑚𝑚𝑀𝑀𝑀𝑀𝑀𝑀 where, is the average temperature difference between the sample and the baseline. ∆𝑇𝑇 1.4.2.2 Magnetometric Method
A second approach that can be employed to evaluate the heating efficiency in magnetic hyperthermia is based on the measurement of the dynamic magnetization ( ) of the sample. In this magnetometric method, the SAR can be calculated by integrating𝑀𝑀 𝑡𝑡
(performed over one period 2 / ) the dynamic magnetization with respect to the applied field strength:45,46 𝜋𝜋 𝑓𝑓
= ( ) (1.4.2.3) 𝑓𝑓
𝑆𝑆𝑆𝑆𝑆𝑆 𝑐𝑐 ∮ 𝑀𝑀 𝑡𝑡 𝑑𝑑𝑑𝑑 where, is the frequency and is the weight concentration of the NP. In this
approach the SAR𝑓𝑓 is proportional to the area𝑐𝑐 of the AC hysteresis loop ( ) as illustrated
ℎ𝑦𝑦𝑦𝑦 in Figure 1.4.2.1b. 𝐴𝐴
12
Figure 1.4.2.1. Different approaches to evaluate the heating efficiency of magnetic nanoparticles: (a) calorimetric method, wherein the sample is placed in the middle of an AC field generating coil and an insulator is used to minimize heat losses to the surroundings (left). The SLP or SAR is proportional to the initial slope of the change in temperature vs. time curve (right) and (b) magnetometric method measures the AC susceptibility of the sample with (left) schematic showing the detection principle using pick-up coils for high frequency hysteresis loop measurements, M-H hysteresis loops obtained from AC magnetometry measurements wherein the SLP is proportional to the area of the hysteresis curve.47
1.5 Magnetic Particle Imaging (MPI)
Magnetic particle imaging (MPI) is a new tomographic three-dimensional (3D) imaging modality proposed by Gleich and Weizenecker.48–50 MPI uses the non-linear
magnetization response curve of superparamagnetic iron oxide nanoparticle (SPIONs)
tracers to an oscillating magnetic field to obtain images based on tracer distribution
maps.48,51–55 The signal generated by the changing magnetization of the SPION tracers results in the occurrence of higher order harmonics of the excitation frequency, which facilitates the quantitative mapping of the local distribution of the magnetic nanoparticles at high spatial and temporal resolution. In MPI, the typical AC excitation frequency used is around 25 kHz with an amplitude of 16 kA/m.55–57 SPIONs are called as tracers in MPI because the nanoparticles are the direct source of the signal and the visualized element and they do not merely act as a supportive contrast agent. This means that tissue, which are
13
diamagnetic, do not interfere with the MPI signal.56 Hence, MPI is able to offer high
temporal and spatial resolution, fast acquisition rates, as well as images with superb
contrast.53 In addition, MPI show superior sensitivity compared to current medical imaging
techniques such as MRI, positron emission tomography (PET), and computed tomography
(CT).56 MPI has recently been reported as a useful technique for in vivo cell tracking,
angiography, and cancer imaging.58,59
MPI signals are generated from/or by a combination of Néel relaxation, Brownian
rotational diffusion, and hysteretic reversal mechanisms that results from the response of
SPIONs to the applied magnetic field. The MPI imaging process is based on the use of
strong, static gradient fields for spatial localization, and time-varying homogeneous
magnetic fields for excitation.48,60–62 In MPI, the use of an AC magnetic field creates time- dependent magnetization of the sample that induces a voltage in the receiver coil. A gradient field is used which creates a field free point (FFP), where the magnetic field is zero. SPIONs in the FFP rapidly aligns with the magnetic field experiencing fast changes in magnetization contributing to the MPI signal. SPIONs outside the FFP have saturated magnetization and do not generate MPI signal.60,61,63 In order to form an image, the FFP is scanned across the sample or the imaging field of view (FOV).
Two established methods in MPI image reconstruction have been developed: (1) system matrix and (2) x-space.52 In both systems, the magnetization curve response of the
SPION tracers for excitation and selective saturation is used to generate a signal. The
system matrix reconstruction method uses the signal’s higher order harmonic content and
distinguishes it from the AC field applied using Fourier analysis.57,64,65 On the other hand,
x-space MPI uses the point spread function (PSF) to describe the shape of the
14
magnetization reversal. The PSF is a product of the magnetization derivative, dm/dH, and
the instrument field gradient, dH/dx, where x is the distance. System matrix and x-space
reconstruction approaches are linked by the Fourier transform. In both cases, the rate of
magnetization change of the tracer should be maximized for a given applied AC field to
obtain the best tracer. In system matrix MPI, this will translate to maximization of the
number of harmonics and its intensity. For x-space MPI, faster changes in magnetization in response to an applied field means that the dm/dH height is higher (increased sensitivity) and the full width at half maximum (FWHM) of the PSF is minimized/narrower translating to better spatial resolution.
Tracers developed for MPI are commonly evaluated using magnetic particle
spectroscopy (MPS).51,63,66 For x-space MPI, 1-dimensional PSF is used to evaluate tracers.60 In MPS, the spatial distribution of nanoparticles is known and the selection field
is excluded from the signal chain due to the small sample size. The measurement is
performed wherein a sequence of time-varying magnetic fields is applied to the particles
and the particle responses are then recorded.53 The amount of particles is proportional to
the amplitude of the particle response and MPI tracers can be evaluated this way.53
1.5.1 Emerging Research Directions: Magnetic Imaging Guided– Hyperthermia
The ability to perform magnetic hyperthermia treatment in an imaging environment
could revolutionize the way we diagnose and eradicate diseases. Combined hyperthermia
and imaging could have several advantages including the ability to evaluate the local
concentration of the magnetic NPs trapped inside diseased tissues, thereby enabling a more
defined treatment plan to prevent overheating side effects. Moreover, the magnetic field
used in hyperthermia could be provided by the same hardware utilized in magnetic
15
imaging, which will facilitate the ease in adapting a coupled theranostic approach in disease
management.
The development of a combined hyperthermia therapy with magnetic particle
imaging (MPI) has been proposed.67–69 MPI is an emerging imaging modality that is highly sensitive and directly detects magnetic NP tracers with no background signal coming from the tissue, thus, offering real-time, high resolution imaging of iron oxide-based NP tracers.48 The magnetic NPs being developed for magnetic hyperthermia are also explored
in MPI, whereby Néel and Brownian relaxation processes mainly dictate the heating
mechanism in hyperthermia as well as the signal generation in MPI, making this coupled
technology ideal in developing optimized magnetic NPs for theranostic applications.
It has recently been reported and demonstrated for the first time the use of tailored
iron oxide-based NPs for an MPI-guided magnetic hyperthermia (hMPI) approach.70 In
this study, shape and composition effects on MPI signal were investigated using magnetic
particle spectroscopy by comparing spherical and cubic NPs of Fe3O4 and Zn04.Fe2.6O4 with
the same magnetic volume (3,600-3,700 nm3). Magnetic hyperthermia experiments were
also performed under static gradient fields similar to the one used in MPI. Zn04.Fe2.6O4
spherical NPs showed a 2-fold enhancement in MPI signal compared to as-synthesized
Fe3O4 and an 8-fold improvement compared to commercially available IONPs. Moreover,
Zn04.Fe2.6O4 cubic IONPs showed a 5-fold improvement in the heating efficiency compared
to the commercially available sample with good MPI signal. Outside the FFP, the
magnetization of the NPs are saturated and magnetic hyperthermia and MPI signal are non-
existent. Thus, targeted and selective heating using magnetic hyperthermia can be achieved
using MPI as a diagnostic tool.
16
1.6 Nanoparticle Characterization Methods
At the heart of every scientific endeavor involving magnetic nanomaterials is the ability to fully characterize the material synthesized and developed in order to understand its physical, chemical, and magnetic properties for various applications. Spectroscopic methods such as Fourier transform infrared spectroscopy (FTIR), atomic absorption spectroscopy (AAS), and energy dispersive X-ray spectroscopy (EDS) are used to obtain chemical information such as elemental and functional groups present on the nanomaterial.
Microscopic techniques such as transmission electron microscopy (TEM) and scanning electron microscopy (SEM) are used to image the nanoparticles in order to determine their size and morphology. Thermal calorimetric method like thermogravimetric analysis (TGA) is used to determine the thermal stability of the material. In addition, magnetic materials are usually characterized using powder X-ray diffractometry (PXRD) to identify the crystalline phase of the material and superconducting quantum interference device
(SQUID) or vibrating sample magnetometry (VSM) are used to determine the magnetic properties of the material. In this section, the different instruments used for this thesis are briefly described.
1.6.1 Spectroscopic Methods
1.6.1.1 Fourier Transform Infrared Spectroscopy (FTIR)
Fourier transform infrared spectroscopy is a widely used technique to provide chemical structure of materials. In this method, infrared radiation is either absorbed by or transmitted through the sample. The signal obtained from the detector is then converted using Fourier transform to generate a unique spectrum that shows the absorption or transmission of infrared radiation by the molecules.71 The chemical structure of a material
17 can then be deduced by looking at the pattern of the FTIR spectra where each molecule has a unique IR fingerprint. FTIR spectroscopy can also be used to perform quantitative analysis by comparing the intensity of specific peaks in the spectrum. Different sampling techniques can be used in FTIR such as transmission, attenuated total reflection (ATR), specular reflection, and diffuse reflectance.
1.6.1.2 Atomic Absorption Spectroscopy (AAS)
Atomic absorption spectroscopy is a technique used to quantitatively determine the concentration of metals in a sample. In this method, an atomizer is used to convert liquid samples to the gaseous (free atoms) state, which absorb light (radiation) at specific frequencies produced by a hollow cathode lamp.72 Each metal has a unique wavelength where it absorbs the maximum radiation. The concentration of the metals is proportional to the light absorbed. Metal oxide samples such as iron oxide nanoparticles are typically dissolved in highly concentrated acids such as hydrochloric (HCl) or nitric (HNO3). The samples are then atomized using a high temperature flame such as air-acetylene in which the atoms in the gaseous state are promoted to an excited energy state. In the process, radiation at a specific wavelength unique to the electron transition is released by the specific element (metal) and the amount of radiation absorbed is proportional to the concentration of the metal. In practice, a calibration curve is generated by preparing different concentrations of the metals using known standards of the metals of interest. The concentration of the unknown samples are then determined by using the absorbance obtained and plugging it into the equation obtained from the calibration curve of the standard solutions following the Beer-Lambert Law.
18
1.6.1.3 Energy Dispersive X-ray Spectroscopy (EDS)
Energy dispersive X-ray spectroscopy (EDS) is an advanced technique used for
quantitative elemental analysis and identification. This tool is typically integrated into a
TEM or an SEM to provide chemical information on the electron microscopy images
obtained. This is because the electron beam used in TEM or SEM can be used to simulate the X-ray emitted from the sample allowing it to provide chemical information as a consequence. The fundamental principle of EDS is based on the unique atomic structure of each element that gives rise to unique and specific X-ray emission spectra. In this method,
high energy beam of electrons or X-rays are used to interact with the sample.73 The incident beam can excite and eject an electron in the inner shell of the atom resulting in an electron hole or vacancy. An electron from an outer shell can fill the hole or vacancy and in the in the process release energy in the form of an X-ray. The characteristic energy and amount of the X-ray released is measured by an energy dispersive spectrometer, which can be correlated to a specific element since each element has a unique atomic structure. When used in tandem with a TEM or an SEM, a qualitative and quantitative spatial map can also be obtained by performing a scan over the entire sample instead of a single point to generate an elemental composition map.
1.6.2 Electron Microscopy Methods
1.6.2.1 Transmission Electron Microscopy (TEM)
Transmission electron microscopy (TEM) is an advanced imaging tool used to
provide 2D images of samples at the nanoscale. Unlike optical microscopes that uses light,
TEM uses a beam of high energy electrons transmitted through a sample to form a gray
scale image.73 The image is formed when the sample interacts with the electrons, which
19 are then diffracted and a shadow of the sample image is then magnified and focused onto a detector that is an imaging device such as a fluorescent screen. A charge-coupled device
(CCD) camera allows the image to be detected and converted digitally. The smaller de
Broglie wavelength of electrons allows TEM to image smaller objects compared to optical microscopes. In practice, TEM imaging is widely used in nanoparticle synthesis to visualize the shape morphology and evaluate the size distribution of the samples. Samples are prepared by placing a small drop (5-10 µL) of a dilute nanoparticle solution on a 400 mesh Formvar–coated copper grid and the solvent is evaporated at room temperature before performing TEM imaging.
1.6.2.2 Scanning Electron Microscopy (SEM)
Scanning electron microscopy (SEM) is a powerful imaging device used to provide high resolution 3D images of samples by scanning the surface of the sample in a raster pattern. This tool uses a beam of low energy electrons that probes or scans a small area of the specimen in a raster pattern allowing it to provide information on the topography of the sample and when combined with EDS gives chemical composition information as well. In this method, the interactions of the electron beam and the sample surface give rise to the generation of different types of electrons (secondary electrons, backscattered electrons) and characteristic X-rays. These signals are unique to each type of atoms on the surface of the sample and provide different information (topographical, morphological, and compositional) on the sample. SEM samples are typically electrically conductive to obtain high resolution images and non-conductive samples are rendered electrically conductive by evaporating a thin film of metal such as gold on the sample surface.
20
1.6.3 Thermogravimetric Analysis (TGA)
Thermogravimetric analysis (TGA) is a thermal analysis technique that measures thermal properties of a material by quantifying changes in the mass of a sample as a function of change in temperature or as a function of time (isothermal) in a controlled atmosphere. Thermal stability of the samples are determined based on its response to changes in temperature up to 1000 oC. Thermal physical properties such as degradation temperature can be identified as a result of mass loss due to decomposition, oxidation, or dehydration. For iron oxide polymer composites, TGA is used to determine the surface ligand coverage of iron oxide nanoparticles as well as the iron oxide content of the iron oxide filled polymer composite.
1.6.4 Powder X-ray Diffraction (PXRD)
Powder X-ray diffraction (PXRD) is an important technique used to provide qualitative and quantitative information on phase composition and particle size of nanomaterials.74,75 The principle of PXRD is based on the constructive interference of monochromatic X-rays and the sample atomic spacings. In a typical PXRD instrument, X- rays generated by cathode ray tubes are filtered to produce monochromatic radiation and concentrated using collimators after which it is then directed towards the sample and the
X-ray signals are recorded by a detector. Bragg’s law (nλ = 2d sinθ) describes the relationship between the wavelength of the X-ray (λ), the diffraction angle (θ), and the crystal lattice spacing of the sample (d).
PXRD measurements involve scanning the sample in a range of 2θ angles to obtain a characteristic diffraction pattern based on diffracted X-ray from different lattice directions, which is unique to specific crystal lattices that are used to estimate crystal phase
21 purity, size of the particles, and d-spacing determination. The peak position and relative intensity of the peaks of the obtained diffraction pattern is then compared to experimental diffraction patterns available in a standard database in order to verify the crystalline phase of the sample. Databases commonly used include the ICDD (International Crystallographic
Diffraction Database), JCPDS (Joint Committee on Powder Diffraction Standards), and
COD (Crystallography Open Database).
The size of crystalline nanoparticles can also be estimated from peak broadening in the diffraction pattern using the Scherrer equation,
= (1.6.4.1) 𝐾𝐾𝐾𝐾 𝑑𝑑 𝛽𝛽 cos 𝜃𝜃 where is the size of the nanoparticle, is the dimensionless shape factor, is the wavelength of 𝑑𝑑the X-ray, is the size broadening𝐾𝐾 given by the full width at half maximum𝜆𝜆
(FWHM), and is the Bragg𝛽𝛽 angle.
1.6.5𝜃𝜃 Dynamic Light Scattering (DLS)
Dynamic light scattering (DLS) is an instrument used to measure the hydrodynamic size of nanoparticles by random changes in light scattered in a solution or suspension.76
DLS measures nanoparticle stability in a colloidal dispersion as well as the presence of aggregation (interparticle interaction). In DLS, a laser light source passes through the sample in solution and in response, light is diffracted in different directions depending on the nature of the sample. The fluctuations in scattered light are then detected by a fast phonon detector at a known scattering angle (θ) and is used to calculate the size of the particle using the Stokes-Einstein equation:
= (1.6.5.1) 𝑘𝑘𝐵𝐵𝑇𝑇 𝐷𝐷ℎ 3𝜋𝜋𝜂𝜂𝐷𝐷𝑡𝑡
22
where is the hydrodynamic diameter, is the Boltzmann constant, is the
ℎ 𝐵𝐵 temperature, 𝐷𝐷 is the viscosity, and is the translational𝑘𝑘 diffusion coefficient. 𝑇𝑇
𝑡𝑡 𝜂𝜂 𝐷𝐷 1.7 References
(1) Nanotechnology & You: Benefits and Applications
https://www.nano.gov/you/nanotechnology-benefits (accessed Nov 23, 2017).
(2) Frenkel, J.; Doefman, J. Nature 1930, 126 (3173), 274.
(3) Kittel, C. Phys. Rev. 1946, 70 (11–12), 965.
(4) Cullity, B. D.; Graham, C. D. Introduction to Magnetic Materials, Second.; Wiley-
IEEE Press, 2011.
(5) Chuev, M. a; Hesse, J. J. Phys. Condens. Matter 2007, 19 (50), 506201.
(6) Stoner, E. C.; Wohlfarth, E. P. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 1948,
240 (826), 599.
(7) Bean, C. P. J. Appl. Phys. 1955, 26 (11), 1381.
(8) Bean, C. P.; Livingston, J. D. J. Appl. Phys. 1959, 30 (4), S120.
(9) Krishnan, K. M. IEEE Trans. Magn. 2010, 46 (7), 2523.
(10) Lee, J.-H.; Huh, Y.-M.; Jun, Y.; Seo, J.; Jang, J.; Song, H.-T.; Kim, S.; Cho, E.-J.;
Yoon, H.-G.; Suh, J.-S.; Cheon, J. Nat. Med. 2007, 13 (1), 95.
(11) Fiorillo, F. Characterization and Measurement of Magnetic Materials; 2004.
(12) Jang, J. T.; Nah, H.; Lee, J. H.; Moon, S. H.; Kim, M. G.; Cheon, J. Angew. Chemie
- Int. Ed. 2009, 48 (7), 1234.
(13) Ling, D.; Hyeon, T. Small 2013, 9 (9–10), 1450.
(14) Wu, W.; He, Q.; Jiang, C. Nanoscale Res. Lett. 2008, 3 (11), 397.
(15) Barnes, M. D.; La Mer, V. K. J. Colloid Sci. 1946, 1 (1), 79.
23
(16) LaMer, V.; Dinegar, R. J. Am. Chem. … 1950, 72 (8), 4847.
(17) Lee, N.; Yoo, D.; Ling, D.; Cho, M. H.; Hyeon, T.; Cheon, J. Chem. Rev. 2015, 115
(19), 10637.
(18) Liu, G.; Gao, J.; Ai, H.; Chen, X. Small 2013, 9 (9–10), 1533.
(19) Liang, H.; Zhang, X. B.; Lv, Y.; Gong, L.; Wang, R.; Zhu, X.; Yang, R.; Tan, W.
Acc. Chem. Res. 2014, 47 (6), 1891.
(20) Reddy, L. H.; Arias, J. L.; Nicolas, J.; Couvreur, P. Chem. Rev. 2012, 112, 5818.
(21) Giustini, A. J.; Petryk, A. A.; Casssim, S. M.; Tate, J. A.; Baker, I.; Hoopes, P. J.
Nano Life 2010, 1 (01n02), 17.
(22) Gilchrist, R. K.; Medal, R.; Shorey, W. D.; Hanselman, R. C.; Parrott, J. C.; Taylor,
C. B. Ann. Surg. 1957, 146 (4), 596.
(23) Gordon, R. T.; Hines, J. R.; Gordon, D. Med. Hypotheses 1979, 5 (1), 83.
(24) Atkinson, W. J.; Brezovich, I. A.; Chakraborty, D. P. IEEE Trans. Biomed. Eng.
1984, BME-31 (1), 70.
(25) Jordan, A.; Wust, P.; Fähling, H.; John, W.; Hinz, A.; Felix, R. Int. J. Hyperth. 2009,
25 (7), 499.
(26) Gneveckow, U.; Jordan, A.; Scholz, R.; Brüss, V.; Waldöfner, N.; Ricke, J.;
Feussner, A.; Hildebrandt, B.; Rau, B.; Wust, P. Med. Phys. 2004, 31 (2004), 1444.
(27) Magforce®: The nanomedicine company
http://www.magforce.de/en/unternehmen/ueber-uns.html (accessed Nov 29, 2017).
(28) Lu, F.; Popa, A.; Zhou, S.; Zhu, J.-J.; Samia, A. C. S. Chem. Commun. (Camb).
2013, 49, 11436.
(29) Pernia Leal, M.; Torti, A.; Riedinger, A.; La Fleur, R.; Petti, D.; Cingolani, R.;
24
Bertacco, R.; Pellegrino, T. ACS Nano 2012, 6 (12), 10535.
(30) Kong, S. D.; Zhang, W.; Lee, J. H.; Brammer, K.; Lal, R.; Karin, M.; Jin, S. Nano
Lett. 2010, 10 (12), 5088.
(31) Riedinger, A.; Guardia, P.; Curcio, A.; Garcia, M. A.; Cingolani, R.; Manna, L.;
Pellegrino, T. Nano Lett. 2013, 13 (6), 2399.
(32) Park, H.; Park, H. J.; Kim, J. A.; Lee, S. H.; Kim, J. H.; Yoon, J.; Park, T. H. J.
Microbiol. Methods 2011, 84 (1), 41.
(33) Nguyen, T.-K.; Duong, H. T. T.; Selvanayagam, R.; Boyer, C.; Barraud, N. Sci. Rep.
2016, 5 (1), 18385.
(34) Yakacki, C.; Satarkar, N.; Gall, K.; Likos, R.; Hilt, Z. J. Appl. Polym. Sci. 2009,
112, 3166.
(35) Dennis, C. L.; Ivkov, R. Int J Hyperth. 2013, 29 (8), 715.
(36) Mamiya, H. 2013, 2013.
(37) Rosensweig, R. E. E. J. Magn. Magn. Mater. 2002, 252 (0), 370.
(38) Shliomis, M. Sov. Phys. Uspekhi (Engl. transl.) 1974, 17 (2), 153.
(39) Carrey, J.; Mehdaoui, B.; Respaud, M. J. Appl. Phys. 2011, 109 (8).
(40) Ortega, D.; Pankhurst, Q. A. Nanosci. Vol. 1 Nanostructures through Chem. 2013,
1, 60.
(41) Natividad, E.; Castro, M.; Mediano, A. J. Magn. Magn. Mater. 2009, 321, 1497.
(42) Natividad, E.; Castro, M.; Mediano, A. Appl. Phys. Lett. 2008, 92.
(43) Andreu, I.; Natividad, E. Int. J. Hyperthermia 2013, 29, 739.
(44) Wildeboer, R. R.; Southern, P.; Pankhurst, Q. A. J. Phys. D. Appl. Phys. 2014, 47
(49), 495003.
25
(45) Connord, V.; Mehdaoui, B.; Tan, R.; Carrey, J.; Respaud, M. Rev. Sci. Instrum.
2014, 85 (9), 93904.
(46) Das, R.; Alonso, J.; Nemati Porshokouh, Z.; Kalappattil, V.; Torres, D.; Phan, M.-
H.; Garaio, E.; Garcia, J. A.; Sánchez Llamazares, J. L.; Srikanth, H. J. Phys. Chem.
C 2016, acs. jpcc.6b02006.
(47) Abenojar, E. C.; Wickramasinghe, S.; Bas-Concepcion, J.; Samia, A. C. S. Prog.
Nat. Sci. Mater. Int. 2016, 26 (5), 440.
(48) Gleich, B.; Weizenecker, J. Nature 2005, 435 (7046), 1214.
(49) Gleich, B.; Weizenecker, J.; Borgert, J. Phys. Med. Biol. 2008, 53 (6), N81.
(50) Weizenecker, J.; Gleich, B.; Rahmer, J.; Dahnke, H.; Borgert, J. Phys. Med. Biol.
2009, 54 (5).
(51) Pablico-Lansigan, M. H.; Situ, S. F.; Samia, A. C. S. Nanoscale 2013, 5 (10), 4040.
(52) Bauer, L. M.; Situ, S. F.; Griswold, M. A.; Samia, A. C. S. J. Phys. Chem. Lett.
2015, 6 (13), 2509.
(53) Panagiotopoulos, N.; Vogt, F.; Barkhausen, J.; Buzug, T. M.; Duschka, R. L.;
Lüdtke-Buzug, K.; Ahlborg, M.; Bringout, G.; Debbeler, C.; Gräser, M.; Kaethner,
C.; Stelzner, J.; Medimagh, H.; Haegele, J. Int. J. Nanomedicine 2015, 3097.
(54) Khandhar, A. P.; Ferguson, R. M.; Krishnan, K. M. In Journal of Applied Physics;
2011; Vol. 109.
(55) Goodwill, P. W.; Saritas, E. U.; Croft, L. R.; Kim, T. N.; Krishnan, K. M.; Schaffer,
D. V.; Conolly, S. M. Adv. Mater. 2012, 24 (28), 3870.
(56) Borgert, J.; Schmidt, J. D.; Schmale, I.; Rahmer, J.; Bontus, C.; Gleich, B.; David,
B.; Eckart, R.; Woywode, O.; Weizenecker, J.; Schnorr, J.; Taupitz, M.; Haegele, J.;
26
Vogt, F. M.; Barkhausen, J. J. Cardiovasc. Comput. Tomogr. 2012, 6 (3), 149.
(57) Biederer, S.; Knopp, T.; Sattel, T. F.; Lüdtke-Buzug, K.; Gleich, B.; Weizenecker,
J.; Borgert, J.; Buzug, T. M. J. Phys. D. Appl. Phys. 2009, 42 (20), 205007.
(58) Zheng, B.; Vazin, T.; Goodwill, P. W.; Conway, A.; Verma, A.; Ulku Saritas, E.;
Schaffer, D.; Conolly, S. M. Sci. Rep. 2015, 5 (September), 1.
(59) Zheng, B.; Von See, M. P.; Yu, E.; Gunel, B.; Lu, K.; Vazin, T.; Schaffer, D. V.;
Goodwill, P. W.; Conolly, S. M. Theranostics 2016, 6 (3), 291.
(60) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imaging 2010, 29 (11), 1851.
(61) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imaging 2010, 64 (5–6), 267.
(62) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imag. 2011, 30 (9), 1581.
(63) Arami, H.; Ferguson, R. M.; Khandhar, A. P.; Krishnan, K. M. Med. Phys. 2013, 40
(7), 71904.
(64) Ferguson, R. M.; Minard, K. R.; Khandhar, A. P.; Krishnan, K. M. Med. Phys. 2011,
38 (3), 1619.
(65) Rahmer, J.; Weizenecker, J.; Gleich, B.; Borgert, J. BMC Med. Imaging 2009, 9 (1),
4.
(66) Ferguson, R. M.; Khandhar, A. P.; Krishnan, K. M. J. Appl. Phys. 2012, 111 (7),
07B318.
(67) Murase, K.; Aoki, M.; Banura, N.; Nishimoto, K.; Mimura, A.; Kuboyabu, T.;
Yabata, I. Open J. Med. Imaging 2015, 5 (2), 85.
(68) Murase, K.; Takata, H.; Takeuchi, Y.; Saito, S. Phys. Medica 2013, 29 (6), 624.
(69) Kuboyabu, T.; Yabata, I.; Aoki, M.; Banura, N.; Nishimoto, K.; Mimura, A.;
Murase, K. Open J. Med. Imaging 2016, 6 (1), 1.
27
(70) Bauer, L. M.; Situ, S. F.; Griswold, M. A.; Samia, A. C. S. Nanoscale 2016, 8 (24),
12162.
(71) Bruice, P. Y. Organic Chemistry, 5th ed.; Prentice Hall, 2006.
(72) Skoog, D.; West, D.; Holler, F. J.; Crouch, S. Fundamentals of Analytical
Chemistry, 9th ed.; Brooks/Cole: Belmont, CA, 2014.
(73) Goodhew, P. J.; Humphreys, J.; Beanland, R. Electron Microscopy and Analysis,
3rd ed.; Taylor & Francis: New York, NY, 2001.
(74) Louër, D. In Encyclopedia of Spectroscopy and Spectrometry; Elsevier, 2017; pp
723–731.
(75) Kim, W.; Suh, C.-Y.; Cho, S.-W.; Roh, K.-M.; Kwon, H.; Song, K.; Shon, I.-J.
Talanta 2012, 94, 348.
(76) Hoo, C. M.; Starostin, N.; West, P.; Mecartney, M. L. J. Nanoparticle Res. 2008, 10
(S1), 89.
28
Chapter 2: Size and Matrix Effect on the Magnetic Hyperthermia Properties of Iron Oxide Magnetite Nanoparticles
2.1 Introduction
Iron oxide nanoparticles (IONPs) are widely investigated due to their tunable
magnetic properties and potential as diagnostic (i.e. as magnetic resonance imaging
contrast agent and magnetic particle imaging tracer) and therapeutic (e.g. drug and gene
delivery, hyperthermia) agents.1-5 Upon excitation with an AC field, IONPs can transform
electromagnetic energy to heat, and the heat generated can be utilized to destroy cancer
cells or pathogenic microbes. In magnetic hyperthermia, the heating can occur by any of
the three mechanisms: (1) eddy current heating due to the effects of induction from the
application of an alternating pulsed magnetic field; (2) frictional heating induced by the
interaction between the NPs and the surrounding medium, and (3) relaxation and hysteretic
losses of the magnetic NPs.2
Various models and experimental data have been employed to better understand the
heating process in magnetic hyperthermia.1–6 In 2002, Rosensweig developed the linear response theory (LRT) to explain the heating of colloidal magnetic fluids subjected to an alternating magnetic field.4 In his formulation, it was assumed that the heat generation was
only due to the rotational relaxation of non-interacting single domain nanoparticles, and
the magnetization of the nanoparticles varies linearly with the applied magnetic field. From
the LRT model, an expression for the power dissipation ( ) was derived as follows:4
𝑃𝑃 = (2.1.1) ′′ 2 𝑃𝑃 𝜇𝜇0𝜋𝜋𝜒𝜒 𝑓𝑓𝐻𝐻 where, and are the amplitude and frequency of the AC magnetic field,
respectively, 𝐻𝐻 represents𝑓𝑓 the permeability of free space, and " is the out-of-phase
0 𝜇𝜇 29 𝜒𝜒
component of the colloidal magnetic fluid AC susceptibility. In turn, " can be expressed
as: 𝜒𝜒
= (2.1.2) ( ) ′′ 𝜔𝜔𝜔𝜔 2 𝜒𝜒 1+ 𝜔𝜔𝜔𝜔 𝜒𝜒0 where, = 2 , is the actual susceptibility, and is the effective relaxation
0 time. The effective𝜔𝜔 relaxation𝜋𝜋𝜋𝜋 𝜒𝜒 time is dependent on the collective𝜏𝜏 contributions of both
Néel and Brownian relaxation processes. Néel relaxation involves the internal rotation of
the magnetic moment and has a characteristic time, , which is expressed as:
𝑁𝑁 𝜏𝜏 ( ) = ( ) 𝐾𝐾𝐾𝐾 (2.1.3) 𝜋𝜋 exp 𝑘𝑘𝐵𝐵𝑇𝑇 𝜏𝜏𝑁𝑁 2 𝜏𝜏0 𝐾𝐾𝐾𝐾 �𝑘𝑘𝐵𝐵𝑇𝑇 On the other hand, Brownian relaxation pertains to the physical rotation of the
magnetic nanoparticle itself and the characteristic relaxation time, , is represented in the
𝐵𝐵 following equation: 𝜏𝜏
= (2.1.4) 3𝜂𝜂𝑉𝑉𝐻𝐻 𝜏𝜏 𝐵𝐵 𝑘𝑘𝐵𝐵𝑇𝑇 where, is the dynamic viscosity and is the hydrodynamic volume. The
𝐻𝐻 effective relaxation𝜂𝜂 time can then be expressed as:𝑉𝑉
= + (2.1.5) 1 1 1 𝜏𝜏 𝜏𝜏𝐵𝐵 𝜏𝜏𝑁𝑁 Substituting equation 4 into equation 3 results in an expanded form of the power
dissipation expression:
= [ ] (2.1.6) 2 2𝜋𝜋𝜋𝜋𝜋𝜋 2 𝑃𝑃 𝜇𝜇0𝜋𝜋 𝜒𝜒0𝑓𝑓𝐻𝐻 �1+ 2𝜋𝜋𝜋𝜋 𝜋𝜋 �
30
which showcases the dependence of the heat generation process with the frequency
and amplitude of the applied AC field, and the magnetic nanoparticle relaxation processes.
The heating efficiency is represented by the specific loss power (SLP), also referred
to as the specific absorption rate (SAR), which is defined as the ratio of the heat power
dissipated and the mass of the magnetic nanoparticles, : 7
𝑀𝑀𝑀𝑀𝑃𝑃 𝑚𝑚 = (2.1.7) 𝑃𝑃 𝑆𝑆 𝑆𝑆𝑆𝑆 𝑚𝑚𝑀𝑀𝑀𝑀𝑀𝑀 A high heating efficiency (SAR) is important in magnetic hyperthermia
applications of iron oxide based nanoparticles for biomedical and environmental
applications. Most magnetic hyperthermia studies have focused on the heating efficiency
of IONPs of in solution.8–11 Fewer studies have been performed on immobilized magnetic
nanoparticles.12,13 Nanoparticle mobility is usually restricted using agar,14,15 wax,13 or a viscous solvent.16 To our knowledge there are no comprehensive studies that have been
reported on the heat efficiency of Fe3O4 NPs embedded in high density polymer matrices.
Studying magnetic nanoparticles in immobilize/restricted conditions are important since in
vivo applications of these materials can usually lead to the nanoparticles adhering to target
tissues. Other applications such as magnetic remote actuation of artificial muscles or smart
textiles involve the magnetic nanoparticles in an immobilized state.17,18
In this study, Fe3O4 NPs were embedded in ultra-high molecular weight polyethylene (UHMWPE) and their heating efficiency in the immobilized state in the polymer was compared to the nanoparticles that are dispersed in solution and are free to move. A range of monodisperse oleic-acid capped spherical Fe3O4 NPs with sizes from 10 to 25 nm were prepared using a thermal decomposition synthesis. These particles were then
31
incorporated in the UHMWPE to obtain magnetite–polyethylene (Fe3O4-PE) nanocomposites. The heating efficiency of the synthesized Fe3O4 NPs in solution and the
Fe3O4-PE nanocomposites was evaluated using different excitation field amplitudes (15–
60 kA/m) at a fixed frequency of 380 kHz.
2.2 Experimental Methods
2.2.1. Materials and Reagents
Iron (III) chloride hexahydrate (98%), iron (III) acetylacetonate (99%), oleic acid
(90%), 1-octadecene (90%), toluene, p-xylene, trimethylamine N-oxide (98%), and ultra-
high molecular weight polyethylene (MW 3,000,000 – 6,000,000 g/mol) were purchased
from Sigma-Aldrich and used as received. Hexane, sodium oleate, and ethanol were
purchased from Fisher Scientific and were used without further purification steps.
2.2.2. Nanoparticle Synthesis and Nanocomposite Fabrication
Fe3O4 nanoparticles were prepared by a modified thermal decomposition method
based on a previously published procedure by Hyeon.19 The nanoparticles were
subsequently dispersed in p-xylene and incorporated into a polyethylene nanocomposite.
2.2.2.1. Synthesis of Magnetite Nanoparticles
Spherical Fe3O4 nanoparticles were prepared in two steps. The first step involves
the thermal decomposition of an iron oleate complex to form wüstite (FeO) nanoparticles,
19 which was then subsequently converted to the Fe3O4 phase using a mild oxidation step.
The iron oleate precursor was prepared by dissolving iron (III) chloride hexahydrate
(FeCl3•6H2O, 40 mmol) and sodium oleate (120 mmol) in a flask containing deionized (DI)
water (60 mL), ethanol (80 mL), and hexane (140 mL). The mixture was then heated to
reflux (58°C) for 4 hours. After reflux, the organic layer containing the iron oleate
32
precursor was separated from the aqueous layer and washed several times with warm water
to remove by-products and excess reagents. The iron oleate mixture was then dried under
vacuum for 72 h and stored for further use.
In a typical synthesis for 15 nm magnetite nanoparticles, iron oleate (3.6 g), oleic acid (4.2 mL), and 1-octadecene (12 mL) were vigorously stirred under argon (Ar)
atmosphere for 10 min at room temperature. The solution was heated to 100 °C for 1 h,
after which the temperature of the reaction mixture was increased (3 °C/min) to 320 °C
and held at reflux for an additional hour. The reaction mixture was then cooled to room
temperature and the obtained FeO nanoparticles were precipitated out by centrifugation for
20 min at 7000 rpm using a 1:1 ethanol: toluene solvent mixture (30 mL).
FeO nanoparticles were converted to Fe3O4 using trimethylamine N-oxide
[(CH3)3NO)] as an oxidizing agent. Briefly, (CH3)3NO (0.1 mmol) was added to FeO
nanoparticles (100 mg), oleic acid (0.5 mL), and 1-octadecene (20 mL). The reaction
mixture was heated to 130 oC (10 oC/min) for 2 h and the temperature was further raised to
280 oC (10 oC/min) and held at that temperature for 1 h. The nanoparticles were then cooled
down to ambient temperature and transferred to a 50 mL centrifuge tube. 30 mL of 1:1
toluene:ethanol was added to the solution and centrifuged at 7,000 rpm for 20 min. The
precipitate collected was dissolved in 10 mL of toluene, degassed with Ar, and stored. By
varying the iron oleate to oleic acid ratio, Fe3O4 NPs ranging from 10 to 20 nm in diameter
were synthesized. In order to synthesize Fe3O4 NPs with a diameter > 20 nm, iron (III)
acetylacetonate [(Fe(C5H7O2)3, 2 mmol] was added to the reaction mixture while keeping
all other reaction conditions the same.
33
2.2.2.2. Fabrication of Fe3O4-PE Nanocomposite Films
The Fe3O4-PE nanocomposites containing 10% w/w magnetite (Fe3O4) were
prepared by re-dispersing Fe3O4 nanoparticles (17.7 nm) in toluene in the presence of small amounts of oleic acid, and mixing with ultra-high molecular weight polyethylene
(UHMWPE, Sigma Aldrich MW range 3,000,000 to 6,000,000). The resulting slurry was mixed using a mechanical paddle attached to a IKA-Vibrax-VXR electric motor for one hour before drying in a Schlenk line while stirring for 6-8 h. The dried powder was further mixed using a high-speed blade mixer and compression molded between layers of PTFE sheets and iron steel plates in a Carver Model C laboratory press at 200°C under 7 metric ton of pressure. The fabricated Fe3O4-PE nanocomposite films were then cut into small pieces and submerged in 0.5 mL p-xylene or DI water to achieve a final concentration of
3.75 mg Fe/mL for all the hyperthermia measurements.
2.2.3. Fe3O4 NP Characterization
The Fe3O4 NP size distribution and shape were evaluated by transmission electron
microscopy (TEM). TEM samples were prepared by placing 5 μL of a dilute suspension of
the Fe3O4 NPs on a 400 mesh Formvar-coated copper grid and allowing the solvent to
evaporate slowly at room temperature. TEM images were obtained with a Hitachi H-9500
transmission electron microscope operated at 300 kV. The mean particle size and size
distribution were evaluated by measuring at least 200 nanoparticles for each sample. The
crystal structure of the samples was identified by powder x-ray diffractometry (PXRD)
performed in a Rigaku MiniFlex powder x-ray diffractometer using Cu-Kα radiation
(λ=0.154 nm). For the XRD analysis, the diffraction patterns were collected within a 2θ
range of 25 to 75°. To evaluate the size of the Fe3O4 NPs in solution, dynamic light
34
scattering (DLS) experiments were performed at a scattering angle of 90 °C using a
Brookhaven ZetaPALS particle size analyzer with a TurboCorr correlator. The total Fe
concentration in each sample was measured using a fast sequential atomic absorption
spectrophotometer (AAS) Varian 220FS AA. For the elemental Fe analysis, the samples
were digested with concentrated hydrochloric acid overnight to completely dissolve the
Fe3O4 NPs.
2.2.4. Fe3O4-PE Nanocomposite Characterization
Thermogravimetric analysis (TGA) was used to determine the final amount of
Fe in the nanocomposite films. The TGA measurements were performed by heating samples from 25 to 800 °C at a heating rate of 10 °C/min under air atmosphere using a TA
Instruments Q500 Thermogravimetric Analyzer. The amount of nanocomposite used for the heating efficiency experiments was calculated based on the TGA results to obtain 3.75 mg Fe/mL when the nanocomposite chips were submerged in 0.5 mL p-xylene or DI water.
2.2.5. Fe3O4 NP and Fe3O4-PE Nanocomposite Magnetic Characterization
The magnetic characterization was performed with a Quantum Design MPMS5
Superconducting Quantum Interference Device (SQUID) magnetometer. The field- dependent magnetic properties of the NP samples were obtained at 5 K from -3.98 x 106
A/m to 3.98 x 106 A/m, and the temperature-dependent magnetic susceptibility curves were measured at 7.96 kA/m from 5 to 350 K. The blocking temperatures (TB) of the samples
were determined from the obtained temperature-dependent magnetic susceptibility curves,
as the temperature where the magnetization is at its maximum.
35
2.2.6. Magnetic Hyperthermia Measurements
The magnetic hyperthermia measurements were performed using a MSI
Automation bench mount magnetic induction heating system. The Fe3O4 samples were
exposed to an alternating current (AC) magnetic field excitation with variable magnetic
field amplitude (H) of 15-60 kA/m at a fixed frequency (f) of 380 kHz. All samples were
measured inside insulated NMR glass tubes with an internal diameter of 7.5 mm, at an iron
concentration of 3.75 mg/mL and a fixed sample volume (Vs) of 0.5 mL, in order to
minimize magnetic field inhomogeneities due to the height of the solenoid coils. To
evaluate the temperature profiles of the samples upon excitation with a continuous
alternating magnetic field, the change in temperature of the samples was monitored with a
fiber optic temperature probe (Neoptix T1™) and was recorded every 5 s. Prior to turning
the magnetic field on, the sample temperature was recorded for 60 s to obtain a stable
baseline for the calculation of the SAR values. The SAR was obtained from the initial slope
over the first 120 s of the heating curve using the following equation:
= (2.2.6.1) 𝐶𝐶𝑉𝑉𝑠𝑠 𝑑𝑑𝑑𝑑 𝑚𝑚 𝑑𝑑𝑑𝑑 where𝑆𝑆 dT/dt𝑆𝑆𝑆𝑆 is the initial slope of the heating curve, C is the volumetric specific heat capacity of the sample, Vs is the volume of the sample, and m is the mass of magnetic
material in the sample.
The effect of particle size and particle immobilization on the SAR values were
investigated using synthesized Fe3O4 NPs with diameters of 12.3, 14.9, 19.8, and 24.3 nm.
Also, the heating efficiencies of the Fe3O4 NPs in solution and the Fe3O4-PE
nanocomposite films were characterized as a function of field amplitude (15 to 60 kA/m).
The frequency (380 kHz), concentration of the samples (3.75 mg Fe/mL), and sample
36
volume (0.5 mL) were kept constant for all hyperthermia measurements. All hyperthermia
measurements were obtained in triplicates.
2.3. Results and Discussion
2.3.1. Structural and Magnetic Properties of the Synthesized Fe3O4 NPs
Fe3O4 NPs with increasing diameters from 12 to 25 nm were synthesized by a thermal decomposition method by adjusting the ratio of metal precursor to ligand. TEM
images show good spherical shape and monodisperse (σ/d < 5%) or near monodisperse
20 (σ/d < 15%) Fe3O4 NPs of different sizes, where σ is the standard deviation and d is the
diameter of the NPs (Figure 2.3.1.1). The particle sizes measured from TEM matched the
results from the powder XRD patterns (Figure 2.3.1.2a). Based on the Debye-Scherrer
equation, the diffraction peaks broaden as the NP size decreases. From the powder XRD
patterns it can also be verified that all of the synthesized NP samples show an inverse spinel
21,22 structure that is representative of the Fe3O4 or γ-Fe2O3 phase (Figure 2.3.1.2a). Rietveld
analysis was also performed on the powder XRD patterns obtained and it shows that the
samples are mainly composed of Fe3O4 (Table 2.3.1.1). The weight percentage of the iron
oxide phases was estimated using a 2-θ range from 56 to 58.5° that was scanned at 0.01° step size and 10 s/step.22 The peak profiles were fitted with pseudo-Voigt function and corrected with
March−Dollase function. Additionally, SQUID magnetometry was used to further characterize the magnetic properties of the NPs.
37
Figure 2.3.1.1. TEM micrographs and respective size distribution measurement of the Fe3O4 nanoparticles showing good size monodispersity.
Table 2.3.1.1. Summary of Rietveld analysis for the different sizes of NPs showing
the percentage of magnetite and maghemite in each sample. Nanoparticles Magnetite (%) Maghemite (%) Sample 12.3 96 4 14.8 96 4 19.8 85 15 24.5 98 2
Figure 2.3.1.2. Powder XRD patterns for the different NP samples (a), the corresponding field-dependent magnetization measured at 5 K (b), and temperature-dependent magnetization at 7.96 kA/m of the synthesized NPs (c). Plot showing the size dependence of the blocking temperature, TB, obtained from M(T) shown in (c) and the calculated anisotropy, K, for the Fe3O4 NP samples (d).
38
The field-dependent magnetic behavior of the NP samples was measured at 5 K
6 6 from -3.98 x 10 A/m to 3.98 x 10 A/m and the saturation magnetization (Ms) of the samples was evaluated to be in the 85-102 Am2/kg range, which resembles the
magnetization of bulk magnetite (92 Am2/kg) more so than that of maghemite (73-74
Am2/kg).23,24 Additionally, the temperature-dependent susceptibility curves show an
increase in blocking temperature (TB) as the size of the Fe3O4 NPs increases (Figure
2.3.1.2c and d). This behavior is related to the increase of the magnetocrystalline anisotropy
energy barrier with increasing nanoparticle volume, and is consistent with the Stoner–
25 19 Wohlfarth theory and reported data. From the measured TB, the magnetic anisotropy
constant, K, was estimated using the equation K = 25kBTB/V, which assumes no particle
interaction is present and where kB is the Boltzmann constant and V is the volume of the
nanoparticle.26,27 The calculated magnetic anisotropy constant was found to increase with
decreasing particle size similar to previous reports in literature Figure 2.3.1.2d.19,28
2.3.2. Structural and Magnetic Properties of the Fe3O4-PE Nanocomposites
The previously synthesized Fe3O4 NPs were also incorporated into a polymer matrix using a liquid-solid compounding method, followed by a compression molding
29,30 process to obtain Fe3O4-PE nanocomposite films. The compression molding process required temperatures (200 oC) above the melting temperature (143 °C) of the UHMWPE
polymer. (Figure 2.3.2.1), which was measured using differential scanning calorimetry
(DSC). The Fe3O4 NPs were exposed to high temperature (200 °C) and pressure (7 metric
tons) conditions during the fabrication of the magnetic nanocomposite films. The
nanocomposite films were ball-milled and the resulting sub-micron debris were analyzed
by TEM to evaluate if there any morphological changes that occurred to the Fe3O4 NPs.
39
The TEM image revealed that the Fe3O4 NPs in the nanocomposite film retained their size
and spherical morphology (Figure 2.3.2.2a). The magnetic properties of the Fe3O4-PE nanocomposite films were measured by SQUID magnetometry and from the obtained magnetic hysteresis loops it can be inferred that the nanocomposites have comparable Ms
values to those of the Fe3O4 NPs prior to fabrication (Figure 2.3.2.2b). For example, the Ms
2 of a Fe3O4-PE nanocomposite sample containing 19 nm Fe3O4 NPs was 90.3 Am /kg, as
2 compared to a 95.5 Am /kg value for 19 nm Fe3O4 NPs. Also, the nanocomposites show
superparamagnetic behavior (no coercivity and remanence at 300 K) indicating that the
magnetic behavior of the Fe3O4 NPs is retained despite being subjected to high temperature
and pressure processing conditions during the fabrication of the magnetic nanocomposite
films. TGA was performed on the Fe3O4-PE nanocomposite films to determine the actual
amount of iron in the samples (Figure 2.3.2.3).
Figure 2.3.2.1. Differential scanning calorimetry (DSC) plot obtained for the ultra high molecular weight polyethylene used in the fabrication of the Fe3O4-PE nanocomposites; the indicated temperature is the evaluated melting temperature for the polymer. The DSC measurements were performed in air atmosphere at a heating rate of 10 °C/min.
40
Figure 2.3.2.2. TEM image of a representative Fe3O4-PE nanocomposite containing Fe3O4 NPs with an average diameter of 19 nm (a) and the comparison of their field-dependence magnetization measured at 5 K (b).
Figure 2.3.2.3. Thermogravimetric analysis (TGA) curves obtained for the Fe3O4-PE nanocomposite films fabricated with the Fe3O4 NPs of different sizes. The TGA measurements were performed by heating the samples from 25 to 800 °C at a heating rate of 10 °C/min under air atmosphere.
2.3.3. Magnetic Hyperthermia Properties
2.3.3.1 Effect of NP Size and Immobilization
The heating efficiency of the various sizes of Fe3O4 NPs in solution was compared to that of the Fe3O4 NPs immobilized in the Fe3O4-PE nanocomposite films. The solvent
41
p-xylene was intentionally used as a solvent for the Fe3O4 NPs and Fe3O4-PE
nanocomposites for two main reasons: 1) to facilitate the stable dispersion of the oleic acid-
capped nanoparticles in solution, and 2) to minimize frictional losses due to the low
viscosity of this solvent. The SAR values were calculated using equation 2 and represent
the power released per gram of Fe under alternating magnetic field excitation. The SAR
values for the Fe3O4 NPs in solution increased as the particle size increased to 19.8 nm,
after which a decrease in the SAR value is observed for the 24.3 nm sample (Figure
2.3.3.1.1). This may indicate that this sample is the closest to the optimal diameter for this
particular field strength, and 2 f is approximately equal to the NPs’ total relaxation time,
.9,31 On the other hand, the polymer𝜋𝜋 matrix effectively restricted nanoparticle motion
unlike𝜏𝜏 the Fe3O4 NPs dispersed in p-xylene, and a decrease in SAR values was observed for all nanoparticle sizes as shown in Figure 2.3.3.1.1 and Table 2.3.3.1.1.
.
Figure 2.3.3.1.1 . Plot comparing the calculated SAR values of the freely moving Fe3O4 NPs in solution and the immobilized nanoparticles in the Fe3O4-PE nanocomposite films as a function of NP size. All the hyperthermia measurements were performed at a concentration of 3.75 mg Fe/mL, H = 30 kA/m and f = 380 kHz, and the reported SAR values were averaged from three measurement trials; the details of the statistical analysis is available in Table 2.3.3.1.1.
42
Table 2.3.3.1.1. Table showing the correlation of the calculated SAR values (averaged out from triplicate magnetic hyperthermia measurements; [Fe]: 3.75 mg/mL; H = 30 kA/m; f
= 380 kHz) with the particle size for Fe3O4 NPs that are mobile in solution and immobilized in the Fe 3O4-PE nanocomposite films.
Size (nm) Fe3O4 NPs SAR, W/g Fe3O4-PE SAR, W/g 12.3 34.55 ± 0.51 9.36 ± 0.79 14.9 43.62 ± 0.18 12.15 ± 0.68 19.8 120.35 ± 0.96 22.19 ± 0.96 24.3 91.11 ± 1.98 39.85 ± 3.34
The differences in SAR values between the mobile and immobilized Fe3O4 NPs, were further investigated by looking at magnetic dipolar interactions such as the formation of chain-like assemblies of mobile NPs in solution, which has been attributed to enhanced
32 magnetic hyperthermia effects. Fe3O4 NPs with an average diameter of 19 nm were dispersed in p-xylene and deposited on a TEM Cu grid while allowing the solvent to evaporate in the presence of a magnetic field. As shown in Figure 2.3.3.1.2, the presence of a magnetic field, the Fe3O4 NPs in solution indeed form linear, chain-like assemblies.
500 nm
Figure 2.3.3.1.2. TEM image of Fe3O4 NPs deposited on a Cu grid in the presence of a magnetic field.
43
It has been demonstrated that the reduction or enhancement in the heating
efficiency of magnetic nanoparticles is due to the formation of nano-assemblies during
hyperthermia measurements. When exposed to an AC field, magnetic nanoparticles can
form linear aggregates of magnetically ordered structures such as chains.32 Several groups
have pointed out theoretically33,34 and have shown experimentally32,35–38 that chain-like
structures can form during AC field excitation, which can give rise to an increase in the
heating efficiency of nanoparticles. In order to maximize the heating efficiency it is
necessary to assemble nanoparticles into chains with a uniaxial anisotropy, which leads to
hysteretic losses that can improve the heat power dissipation process.33,38 This type of
assembly leads to an enhancement of the effective anisotropy of the nanoparticles due to
the unidirectional magnetization orientation as a result of the dipolar coupling along the
chain.38 These particles that pack together in a chain-like formation display an increased
ferromagnetic behavior with their moments locked in the direction of the magnetic field,
which leads to an experimentally observed increase in heating efficiency.32,39,40
On the other hand, formation of magnetic nanochains in the Fe3O4-PE
nanocomposites when exposed to a magnetic field is not possible due to the fact that Fe3O4
NPs mobility is restricted when they were embedded in the polymer. Thus, the increase in
heating efficiency due to the formation of nanochains is not possible for the Fe3O4-PE
nanocomposites, and results to a lower heating efficiency compared to the Fe3O4 NPs in
solution. The SAR values decreased by 25.2 and 31.5 W/g for Fe3O4 NPs with average
sizes of 12.3 and 14.9 nm, respectively, in the Fe3O4-PE nanocomposites. For 19.8 nm sized Fe3O4 NPs, immobilization in the Fe3O4-PE nanocomposite film gave a SAR value
of 98.2 W/g, which lowered the SAR 3x higher compared to the smaller sized samples.
44
The larger Fe3O4 NPs with an average diameter of 24.3 nm showed a lower decrease in the
SAR value, 51.3 W/g lower in the Fe3O4-PE nanocomposite compared to the Fe3O4 NPs in solution.
2.3.3.2 Effect of Varying the Alternating Current (AC) Magnetic Field
Amplitude
The field amplitude effect on the heating efficiencies of the Fe3O4 NPs in solution
and in the Fe3O4-PE nanocomposite was also investigated (Figures 2.3.3.2.1a and b). When
the field amplitude was increased from 15 to 60 kA/m, the heating efficiency increased
correspondingly, as indicated by higher SAR values for both Fe3O4 NPs in solution and the
Fe3O4-PE nanocomposite. For Fe3O4 NPs in solution, the maximum SAR value was
achieved for the 19.8 nm sample regardless of the applied field amplitude (Figure
2.3.3.2.2b). On the other hand, the Fe3O4-PE nanocomposite samples showed the highest
SAR value for the 24.3 nm Fe3O4 NPs independent of the amplitude as well. (Figure
2.3.3.2.2b). These results indicate that the excitation field amplitude does not considerably
affect the predominant heating mechanism in the samples studied.
Figure 2.3.3.2.1. Plots showing the relation of the calculated SAR values as a function of nanoparticles size measured at different amplitudes (15, 30, 45, 60 kA/m) for Fe3O4 NPs dispersed in p-xylene (a), and those embedded in the Fe3O4-PE nanocomposite films submerged in p-xylene (b). All the hyperthermia measurements were performed at a concentration of 3.75 mg Fe/mL and f = 380 kHz, and the reported SAR values were averaged from three measurement trials; the details of the statistical analysis is available in Table 2.3.3.2.1.
45
Table 2.3.3.2.1. Table showing the correlation of the calculated SAR values (averaged out from triplicate magnetic hyperthermia measurements in W/g) with the particle size for Fe3O4 NPs that are mobile in solution (a) and immobilized in the Fe3O4-PE nanocomposite films (b). The hyperthermia measurements were performed at a total iron concentration of 3.75 mg/mL, f = 380 kHz and varying field amplitudes (i.e. 15, 30, 45, 60 kA/m).
(a) Fe3O4 NPs H (kA/m) Size (nm) 15 30 45 60 12.3 14.37 ± 0.20 34.55 ± 0.51 72.39 ± 0.83 92.00 ± 2.62 14.9 19.21 ± 0.06 43.62 ± 0.18 89.84 ± 5.24 101.19 ± 2.05 19.8 56.05 ± 0.67 120.35 ± 0.96 262.95 ± 4.93 307.39 ± 6.21 24.3 25.56 ± 0.09 91.11 ± 1.98 203.28 ± 2.25 261.74 ± 6.39
(b) Fe3O4-PE H (kA/m) Fe3O4 NP Size (nm) 15 30 45 60 12.3 5.73 ± 0.24 9.36 ± 0.79 13.35 ± 1.03 22.79 ± 0.54 14.9 8.62 ± 0.21 12.15 ± 0.68 18.24 ± 1.52 26.42 ± 1.03 19.8 8.90 ± 0.42 22.19 ± 0.96 33.23 ± 0.83 50.25 ± 0.53 24.3 11.20 ± 1.18 39.85 ± 3.34 62.36 ± 1.13 81.76 ± 5.31
2.4. Conclusions
The hyperthermia properties of Fe3O4 magnetic nanoparticles in two matrices were
investigated by studying the behavior of the particles in solution or when they are
embedded in a fixed polymer matrix using ultra high molecular weight polyethylene. The
morphology and superparamagnetic properties of the Fe3O4 NPs in the nanocomposite
films were retained despite using high temperature and pressure in the fabrication process.
The Fe3O4 NPs in solution were able to freely move in solution and can form structures that can enhance the heat generation, such as chains, or form non-linear aggregates that
decrease the heat generation. On the other hand, the nanoparticles embedded in the polymer
matrix had very limited mobility and no hysteresis loop area modifying structures could be
formed under a magnetic field, resulting in lower SAR values compared to their freely
moving counterparts. In spite of these, the Fe3O4-PE nanocomposites still show significant
46
heat generation and can be used as a tool to evaluate and optimize nanoparticles that will
be placed in a bound state for hyperthermia applications.
2.5 References
(1) Dennis, C. L.; Ivkov, R. Int J Hyperth. 2013, 29 (8), 715.
(2) Mamiya, H. J. Nanomater. 2013, 2013, 1.
(3) Chuev, M. a; Hesse, J. J. Phys. Condens. Matter 2007, 19 (50), 506201.
(4) Rosensweig, R. E. E. J. Magn. Magn. Mater. 2002, 252 (0), 370.
(5) Shliomis, M. Sov. Phys. Uspekhi (Engl. transl.) 1974, 17 (2), 153.
(6) Carrey, J.; Mehdaoui, B.; Respaud, M. J. Appl. Phys. 2011, 109 (8).
(7) Ortega, D.; Pankhurst, Q. A. Nanosci. Vol. 1 Nanostructures through Chem. 2013,
1, 60.
(8) Hugounenq, P.; Levy, M.; Alloyeau, D.; Lartigue, L.; Dubois, E.; Cabuil, V.;
Ricolleau, C.; Roux, S.; Wilhelm, C.; Gazeau, F.; Bazzi, R. J. Phys. Chem. C 2012,
116 (29), 15702.
(9) Mehdaoui, B.; Meffre, A.; Carrey, J.; Lachaize, S.; Lacroix, L. M.; Gougeon, M.;
Chaudret, B.; Respaud, M. Adv. Funct. Mater. 2011, 21 (23), 4573.
(10) Lévy, M.; Gazeau, F.; Bacri, J.-C.; Wilhelm, C.; Devaud, M. Phys. Rev. B 2011, 84
(7), 75480.
(11) Bakoglidis, K. D.; Simeonidis, K.; Sakellari, D.; Stefanou, G.; Angelakeris, M.
IEEE Trans. Magn. 2012, 48 (4), 1320.
(12) Usov, N. A.; Liubimov, B. Y. J. Appl. Phys. 2012, 112 (2), 23901.
47
(13) Vallejo-Fernandez, G.; Whear, O.; Roca, A. G.; Hussain, S.; Timmis, J.; Patel, V.;
O’Grady, K. J. Phys. D. Appl. Phys. 2013, 46 (31), 312001.
(14) Arami, H.; Ferguson, R. M.; Khandhar, A. P.; Krishnan, K. M. Med. Phys. 2013, 40
(7), 71904.
(15) de la Presa, P.; Luengo, Y.; Multigner, M.; Costo, R.; Morales, M. P.; Rivero, G.;
Hernando, A. J. Phys. Chem. C 2012, 116 (48), 25602.
(16) Fortin, J. P.; Wilhelm, C.; Servais, J.; Ménager, C.; Bacri, J. C.; Gazeau, F. J. Am.
Chem. Soc. 2007, 129 (9), 2628.
(17) Mohr, R.; Kratz, K.; Weigel, T.; Lucka-Gabor, M.; Moneke, M.; Lendlein, A. Proc.
Natl. Acad. Sci. 2006, 103 (10), 3540.
(18) Puig, J.; Hoppe, C. E.; Fasce, L. A.; Pérez, C. J.; Piñeiro-Redondo, Y.; Bañobre-
López, M.; López-Quintela, M. A.; Rivas, J.; Williams, R. J. J. J. Phys. Chem. C
2012, 116 (24), 13421.
(19) Park, J.; An, K.; Hwang, Y.; Park, J.-G.; Noh, H.-J.; Kim, J.-Y.; Park, J.-H.; Hwang,
N.-M.; Hyeon, T. Nat. Mater. 2004, 3 (12), 891.
(20) Baalousha, M.; Lead, J. R. Nat. Nanotechnol. 2013, 8 (5), 308.
(21) McQueeney, R. J.; Yethiraj, M.; Chang, S.; Montfrooij, W.; Perring, T. G.; Honig,
J. M.; Metcalf, P. Phys. Rev. Lett. 2007, 99 (24), 246401.
(22) Kim, W.; Suh, C.-Y.; Cho, S.-W.; Roh, K.-M.; Kwon, H.; Song, K.; Shon, I.-J.
Talanta 2012, 94, 348.
(23) Cullity, B. D.; Graham, C. D. Introduction to Magnetic Materials, Second.; Wiley-
IEEE Press, 2011.
48
(24) Bate, G. Proc. IEEE 1986, 74 (11), 1513.
(25) Stoner, E. C.; Wohlfarth, E. P. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci. 1948,
240 (826), 599.
(26) Chen, Q.; Zhang, Z. J. Appl. Phys. Lett. 1998, 73 (21), 3156.
(27) Hyeon, T. Chem. Commun. 2003, No. 8, 927.
(28) Salas, G.; Camarero, J.; Cabrera, D.; Takacs, H.; Varela, M.; Ludwig, R.; D??hring,
H.; Hilger, I.; Miranda, R.; Morales, M. D. P.; Teran, F. J. J. Phys. Chem. C 2014,
118 (34), 19985.
(29) Bin, Y.; Yamanaka, A.; Chen, Q.; Xi, Y.; Jiang, X.; Matsuo, M. Polym. J. 2007, 39
(6), 598.
(30) Guoliang, P.; Qiang, G.; Aiguo, T.; Zhiqiang, H. Mater. Sci. Eng. A 2008, 492 (1–
2), 383.
(31) Mamiya, H. J. Nanomater. 2013, 2013, 1.
(32) Saville, S. L.; Qi, B.; Baker, J.; Stone, R.; Camley, R. E.; Livesey, K. L.; Ye, L.;
Crawford, T. M.; Thompson Mefford, O. J. Colloid Interface Sci. 2014, 424, 141.
(33) Mehdaoui, B.; Tan, R. P.; Meffre, A.; Carrey, J.; Lachaize, S.; Chaudret, B.;
Respaud, M. Phys. Rev. B - Condens. Matter Mater. Phys. 2013, 87 (17), 1.
(34) Fu, R.; Yan, Y. Y.; Roberts, C. AIP Adv. 2015, 5 (12), 127232.
(35) Martinez-Boubeta, C.; Simeonidis, K.; Makridis, A.; Angelakeris, M.; Iglesias, O.;
Guardia, P.; Cabot, A.; Yedra, L.; Estradé, S.; Peiró, F.; Saghi, Z.; Midgley, P. a;
Conde-Leborán, I.; Serantes, D.; Baldomir, D. Sci. Rep. 2013, 3, 1652.
49
(36) Alphandéry, E.; Faure, S.; Seksek, O.; Guyot, F.; Chebbi, I. ACS Nano 2011, 5 (8),
6279.
(37) Alphandéry, E.; Chebbi, I.; Guyot, F.; Durand-Dubief, M. Int. J. Hyperth. 2013, 29
(8), 801.
(38) Serantes, D.; Simeonidis, K.; Angelakeris, M.; Chubykalo-Fesenko, O.; Marciello,
M.; Del Puerto Morales, M.; Baldomir, D.; Martinez-Boubeta, C. J. Phys. Chem. C
2014, 118 (11), 5927.
(39) Serantes, D.; Baldomir, D.; Pereiro, M.; Hernando, B.; Prida, V. M.; Sánchez
Llamazares, J. L.; Zhukov, a; Ilyn, M.; González, J. J. Phys. D. Appl. Phys. 2009,
42 (21), 215003.
(40) Varón, M.; Beleggia, M.; Kasama, T.; Harrison, R. J.; Dunin-Borkowski, R. E.;
Puntes, V. F.; Frandsen, C. Sci. Rep. 2013, 3, 1234.
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Chapter 3: Thermoresponsive Magnetic Hydrogel Nanocomposite for Combined Thermal and D-Amino Acid Assisted Biofilm Disruption
3.1 Introduction
A biofilm is an aggregation of bacterial microorganisms attached to solid biological
or non-biological surfaces encased in a self-produced extracellular polymeric matrix
(EPM), which acts as a barrier and provides structural stability.1 Unlike planktonic bacteria, which are susceptible to antibiotics and can be eliminated by antibodies and phagocytes, bacteria present in biofilms show increased immunity and resistance to antimicrobials used in treatment of infectious diseases. The mechanism by which biofilm are inherently resistant to antimicrobial agents is not well understood but two mechanisms have been proposed. The first involves the inability of antimicrobial agents to penetrate the biofilm completely due to the presence of the EPM, which retards the diffusion of antibiotics rendering it less effective.2,3 The second mechanism involves the slow growth state that
bacteria in biofilms experience due to nutrient deficiency.2,3 These kinds of cells (slow
growing/non-growing) are not susceptible to antimicrobial agents.
Biofilm development can be divided into at least 4 stages: (1) initial adhesion
involving reversible-irreversible attachment to the host surface, (2) microcolony formation
or early biofilm formation, wherein bacteria produce extracellular polymeric substances
(EPS) that promote adhesion, (3) biofilm maturation involving formation of 3D structures
with the EPM serving as a protective scaffold, and (4) dispersal, wherein the cells leave
the biofilm ad re-enter the planktonic phase as a result of environmental cues triggering
dispersal mechanisms.4,5
Biofilm disruption can be achieved in many ways, such as physical
removal/degradation of the EPM, changing the pathogenic microenvironment (low pH or
51
hypoxia), elimination of dormant cells, remodeling the EPM, and activating dispersal
mechanisms.5 Varying the environment of biofilms by changing the nutrients available to
bacteria in the biofilm has been shown to induce dispersal such as limiting or enhancing
carbon,6,7 nitrogen,7 and oxygen sources,7,8 the addition of low levels of nitric oxide9–11
and lowering magnesium, calcium, and iron levels.12,13 Also, bacteria derived signals such
as quorum sensing N-acyl homoserine lactones,14 auto-inducing peptides,15 and fatty
acids16 are known to induce biofilm dispersion as well. Quorum sensing is the mechanism by which a population of bacteria receives input from neighboring cells and triggers a response in the process in order to survive by producing and releasing chemical signal molecules called autoinducers in response to changes in cell population density.17,18
In bacteria, biofilm dispersal is regulated by the intracellular secondary messenger
nucleotides cyclic di-guanosine monophosphate (c-di-GMP) and cyclic di-adenosine monophosphate (c-di-AMP).19 Diguanylate cyclases (DGCs) and phosphodiesterases
(PDEs) regulate the biosynthesis and degradation, respectively, of c-di-GMP.19 c-di-GMP regulates many processes such as biofilm formation, motility, virulence, differentiation, cell cycle, as well as the transition of bacteria from planktonic to the biofilm state.19
Moreover, signaling pathways dependent on c-di-GMP control how well bacteria attach to abiotic surfaces and interact with bacterial cells.19 Reduced levels of c-di-GMP promote biofilm disassembly while increased levels promote biofilm formation.20 Nitric oxide,
known to disperse biofilm, has been shown to stimulate PDEs, which degrades c-di-GMP,
showing a link between nitric oxide and c-di-GMP in triggering biofilm dispersal.10 Based on these studies several strategies developed to induce biofilm dispersal include targeting
52
the c-di-GMP pathway, bacteria derived quorum sensing signaling mechanisms, metabolic
interference, and targeting dormant cells.5,21
Bacterial infection is a major cause of certain diseases such as cystic fibrosis (CF),
which is the second most common genetic disease in the United States. CF is a result of
mutation in the cystic fibrosis transmembrane regulator (CFTR) gene, and is characterized
by bacterial infection of the lungs.22 CF infection has been shown to be a polymicrobial disease with both bacterial strains of Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa) being isolated from infected patients.22–26 In particular, S.
aureus is the most prevalent bacterial infection in young children with CF.23 While CF has
no cure, different treatments using antibiotics, anti-inflammatory medicines,
bronchodilators, or medicines to help clear the mucus may be prescribed by doctors.27 In
particular, antimicrobial therapies using a variety of antibiotics is still one of the main
strategies used to combat bacterial infection in CF.23 Antimicrobial resistance by bacterial
biofilms and the presence of different bacterial species is the major cause of difficulty in
treating CF.
It has been reported in literature that the use of amino acids interferes with the
metabolic pathway of the bacteria.5 Specifically, the use of various D-amino acids
(tyrosine, methionine, tryptophan, and leucine) have been reported to show biofilm
inhibition and disruption activities against several bacterial strains such as S. aureus and
P. aeruginosa.28 D-amino acids were found to disrupt biofilm by integrating into the cell
wall of the bacteria.28 Cells in biofilm are encased in an extracellular polymeric matrix that
is made of exopolysaccharide and amyloid fibers composed of the protein TasA. A
combination of D-amino acids (methionine, proline, and tryptophan) has also shown better
53
biofilm dispersal activity than their individual D-amino acids towards S. aureus.29 In
addition, antibiotics (clindamycin, cefazolin, oxacillin, rifampin, and vancomycin for S.
aureus and amikacin, colistin, ciprofloxacin, imipenem, and ceftazidime for P. aeruginosa)
have been reported to enhance the biofilm dispersal activity of a combination of equimolar
mixture of D-amino acids (methionine, phenyalanine and tryptophan).30 Likewise, D-
amino acids have been shown to inhibit biofilm formation but not new bone formation at
high D-amino acid mixture (methionine, proline, and phenylalanine) concentrations (200
mM).31
Recently, the use of magnetic hyperthermia for antibacterial applications has been
explored and it has been demonstrated to be an effective method in inactivating bacteria
both as planktonic cells and as biofilms.32–39 Magnetic hyperthermia using biocompatible
iron oxide nanoparticles that generate heat when exposed to an alternating current (AC)
magnetic field has been shown to enhance antibiotic efficacy as well as stimulate biofilm
dispersal.32,33,35,37–41 Magnetic hyperthermia has also demonstrated to be much more
harmful than direct heating on the cell membrane integrity, as well as promoting modification on bacterial cell surface and biofilm structure leading to eradication.34,35
Thermal inactivation of bacterial biofilms using magnetic hyperthermia is very attractive
because it does not rely on the use of antibiotics and could treat antibiotic-resistant bacteria.
In addition, magnetic hyperthermia has the advantage of being non-invasive, tissue specific, and able to perform localized heating for targeted biofilm eradication.42
Glycol chitin is a water-soluble thermoresponsive polymer derived from glycol
chitosan that changes from sol to gel phase at different temperatures by tuning the polymer
concentration and degree of N-acetylation.43 Polymers derived from natural
54
polysaccharides such as chitosan are ideal for biomedical applications because they have
low toxicity and are biodegradable and biocompatible as well.44,45 Gelation occurs for chitosan-based hydrogels via electrostatic, hydrophobic, and hydrogen bonding forces between the polymer chains and since formation of a network by this gel is based on physical attractive forces alone, gelation is reversible.45
In this study, we report on the fabrication of a magnetic hydrogel system made of
superparamagnetic iron oxide nanoparticles and glycol chitin hydrogel loaded with D-
amino acids (tyrosine, tryptophan, and phenylalanine) as a new treatment approach towards
biofilm disruption by taking advantage of the biofilm dispersal activity of D-amino acids and the magnetic hyperthermia effects of the iron oxide nanoparticles. We began by evaluating the biofilm disruption activities of individual antibiotic and D- and L-amino acids (tyrosine, tryptophan, phenylalanine, and methionine). It was observed that using a combination of three D-amino acids (tyrosine, tryptophan, and phenylalanine) at a higher total concentration performed better than the corresponding L-amino acid mixture and
individual D- and L-amino acids. However, these amino acid combinations were toxic to
HeLa cells even though they performed well in disrupting pre-formed biofilm. Based on
these observations, we performed a time-dependent biofilm disruption assay and
cytotoxicity assay using HeLa cells. It was demonstrated that a 2 h biofilm treatment using
the D-amino acid combination resulted in significant biofilm disruption (~85 %) while
being non-toxic as well. Finally, magnetic hyperthermia using iron oxide nanoparticles was
then applied as an adjuvant treatment in order to totally eradicate the biofilm.
55
3.2 Materials and Methods
3.2.1 Materials and Reagents
The following chemicals were purchased from Sigma-Aldrich and used as received:
D-tyrosine (99%), D-tryptophan (≥98.0%), D-phenylalanine (≥98%), L-tyrosine (≥98%),
L-tryptophan (≥98%), L-phenylalanine (≥98%), acetic acid (glacial), methanol (≥99.8%),
ethanol (70%), toluene (99.9%), acetic anhydride (≥99%), acetone (≥99.5%), glycol
chitosan (≥50%), iron (III) chloride hexahydrate (98%), iron (III) acetylacetonate (97%), oleic acid (90%), 4-biphenylcarboxylic acid (95%), benzyl ether (98%), triethylamine
(≥99%), and trimethylamine N-oxide (98%). Sodium oleate, sodium hydroxide, crystal
violet, Dulbecco’s Modified Eagle Medium (DMEM), fetal bovine serum, and
penicillin/streptomycin solution were purchased from Fisher Scientific. 3-
(triethoxysilyl)propyl succinic anhydride was purchased from Gelest, Inc.
3.2.2 Synthesis of Iron Oxide (Fe3O4) nanoparticles
3.2.2.1 Synthesis of Spherical Iron Oxide Nanoparticles
Spherical Fe3O4 nanoparticles were prepared in two steps. The first step involves
the thermal decomposition of an iron oleate complex to form wüstite (FeO) nanoparticles,
46 which were then subsequently converted to the Fe3O4 phase using a mild oxidation step.
The iron oleate precursor was prepared by dissolving iron (III) chloride hexahydrate
(FeCl3•6H2O, 40 mmol) and sodium oleate (120 mmol) in a flask containing deionized (DI)
water (60 mL), ethanol (80 mL), and hexane (140 mL). The mixture was then heated at
reflux for 4 h. After reflux, the organic layer containing the iron oleate precursor was
separated from the aqueous layer and washed several times with warm water to remove
56 salt by-products and excess reagents. The iron oleate mixture was then dried under vacuum for 72 h and stored for further use.
To synthesize 30 nm iron oxide nanoparticles, iron oleate (3.6 g) and oleic acid (15 mL) were vigorously stirred under argon (Ar) atmosphere. The solution was then heated to
380 °C (3 °C/min) and refluxed for an hour. The reaction mixture was then cooled to ambient temperature and FeO nanoparticles were precipitated via the addition of 1:1 toluene:ethanol solvent mixture (35 mL) and centrifuged at 7,000 rpm for 20 min. FeO nanoparticles were converted to Fe3O4 using trimethylamine N-oxide [(CH3)3NO)] as an oxidizing agent. Briefly, (CH3)3NO (0.1 mmol) was added to FeO nanoparticles (100 mg), oleic acid (0.5 mL), and 1-octadecene (20 mL). The reaction mixture was heated to 130 oC
(10 oC/min) for 2 h and the temperature was further raised to 280 oC (10 oC/min) and held at that temperature for 1 h. The nanoparticles were then cooled down to ambient temperature and transferred to a 50 mL centrifuge tube. The solution was added with 30 mL of 1:1 toluene:ethanol and centrifuged at 7,000 rpm for 20 min. The precipitate collected was dissolved in 10 mL of toluene, degassed with Ar, and stored.
3.2.2.2 Synthesis of Cubic Iron Oxide Nanoparticles
Cubic shaped magnetic nanoparticles were synthesized using a thermal decomposition approach. Iron (III) acetylacetonate (0.5 mmol), 4-biphenylcarboxylic acid
(0.5 mmol), oleic acid (1.90 mmol), benzyl ether (52.61 mmol) were placed in a 50 ml three-neck round-bottom flask. The mixture was heated for 30 minutes at 70 oC under an
Ar atmosphere, then again for 90 minutes at 300 oC. After it cooled down to 60 ºC, ethanol was added to the mixture and the nanoparticles were centrifuged, isolated, and dispersed in toluene.
57
3.2.3 Synthesis and Characterization of Glycol Chitin Hydrogel
A thermosetting hydrogel based on glycol chitosan was prepared according to a
previously reported procedure.43 Briefly, glycol chitosan was dissolved in a 1:1 mixture of water to methanol, acetic anhydride was added, and the solution was left to stir for 48 hours. The resulting reaction mixture was precipitated with acetone and centrifuged at 4°C.
The precipitate was dissolved in water and incubated in 1 M NaOH solution for 12 hours to remove undesired reaction by-products. The sample was then dialyzed in a dialysis membrane (molecular weight cut off: 2000 Da) for 3 days. The processed reaction was precipitated with acetone and centrifuged at 4°C to isolate the glycol chitin thermosetting hydrogel. Following lyophilization, the product was collected and stored for further use.
Glycol chitin was characterized via 1H NMR spectroscopy using a 500 MHz Bruker
Ascend Avance III HDTM. FTIR spectroscopy measurements were performed using a
Thermo Scientific Nexus 870 ATR-FTIR spectrometer at a range of 600–4000 cm-1.
Rheological studies were performed by measuring temperature dependent changes in
viscosity using a Thermo Scientific HAAKE MARS III Rheometer. Viscosity was
measured at a temperature range of 15–75 oC at a heating rate of 0.0575 oC/s and a shear rate of 0.1/s.
3.2.4 Preparation of Magnetic Hydrogels Using Water Soluble Fe3O4 Nanoparticles
Magnetic hydrogels were prepared by mixing water soluble Fe3O4 nanoparticles
with the glycol chitin-based hydrogel at the appropriate loading concentrations. Water
soluble Fe3O4 nanoparticles were prepared by modifying the surface of the as-prepared oleic acid coated nanoparticles with a carboxyl-terminated silane ligand using a ligand exchange process. A solution containing NH4OH in 1-butanol (1 M), triethylamine (1.4
58
mL), Millipore water (0.5 mL), and 3-(triethoxysilyl)propyl succinic anhydride (100 μL) was vortexed for 5 min. Fe3O4 nanoparticles in toluene (25 mg/mL) were added to the
previous solution and vortexed for another 5 min. The sample was allowed to sit for at least
1 h after which the Fe3O4 nanoparticles transferred from the organic to the water phase.
The solution mixture was centrifuged at 8,500 rpm for 30 min. The supernatant was
removed and the sample was re-dispersed in Millipore water.
3.2.5 Biofilm Formation and Dispersal Assays
S. aureus (ATCC 10832) were cultured overnight in agar plates or in lysogeny broth
(LB) with agitation (200 rpm) at 37 oC. Biofilm formation was evaluated under static
conditions using 12-well plates (Falcon, USA). Bacterial cultures made overnight were
5 diluted to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy
broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial
solution and incubated at 37 oC for 24 h. Biofilm dispersal activity was evaluated by
removing the culture media from the biofilms after 24 h and replacing with saline solution
(0.9 % NaCl) containing individual or a combination of amino acids at the specified
concentrations (pH = 7.4). After exposure to the different treatment methods, the plates
were gently washed with phosphate buffered saline (PBS, 1x) thrice and stained with 500
μL of 0.1% (w/v) crystal violet for 30 min. The wells were washed with PBS and the crystal
violet stain was solubilized with 30% acetic acid. The solution was diluted twenty times
and biofilm biomass was quantified by measuring the absorbance at 595 nm. All assays
were repeated in triplicates.
59
3.2.6 Magnetic Hyperthermia-Aided Biofilm Dispersal Assays
For samples treated with magnetic hyperthermia, biofilm was grown on individual
35x10 mm polystyrene wells. Magnetic nanoparticles were incorporated in the treatment solutions. The wells were placed in the middle of a water-cooled coil and exposed to an alternating current (AC) magnetic field with an amplitude (H) of 5 kA/m and a frequency
(f) of 380 kHz. All assays were repeated in triplicates.
3.2.7 Cell Viability Assays
HeLa (human cervical cancer) cells (CCL-2, ATCC, USA) were cultured in
Dulbecco’s Modified Eagle Medium (DMEM; Corning Cellgro, Fisher, USA) supplemented with 10% fetal bovine serum (FBS; Gibco, New Zealand) and 1%
o penicillin/streptomycin (Corning Cellgro, Fisher, USA) at 37 C in 5% CO2 in a humidified
atmosphere. The cells were seeded to 100% confluence in clear 96-well tissue culture
plates (Costar, Corning, NY, USA) for 24 h. The cells were then exposed to different
treatments in media and incubated for a specified time. After treatment, the cells were
washed in sterile PBS and re-suspended in 100 μL/well of DMEM (10% FBS, 1%
pen/strep). Cell viability was evaluated using the PrestoBlue assay (Invitrogen, USA) and
following the manufacturer’s instructions. Briefly, 10 μL of PrestoBlue assay was directly
added to each well and the plates were incubated at the cell culture incubator (37 oC and
5% CO2) for 2 h. The absorbance at 570 nm and 600 nm were measured using a SpectraMax i3 microplate reader (Molecular Device Inc., Sunnyvale, CA). All assays were performed in triplicates.
60
3.2.8 Statistical analyses
Statistical analyses were performed using one-way ANOVA with a Tukey test to determine statistical difference between the groups (p < 0.05).
3.3 Results and Discussion
3.3.1 Dose Dependent Effect of Antibiotics on S. Aureus Biofilm Disruption
S. aureus is one of the major causes of hospital- and community-based acquired infections resulting in formation of methicillin resistant S. aureus (MRSA), which is resistant to many antibiotics currently available.4 Two commonly used antibiotics in the
clinic, vancomycin and bacitracin, were evaluated for their biofilm dispersal activity
against S. aureus at relevant clinical concentrations (15 mg/kg (ppm) every 12 h for
vancomycin; 64 mg/L (ppm) per dosage and a total of 4 times per day for bacitracin) as
well as concentrations higher than that to establish if there is a concentration effect.47–49
Figure 3.3.1.1 shows that vancomycin does not disrupt pre-formed biofilm at concentrations ranging from 2 to 256 ppm. On the other hand, bacitracin shows increasing biofilm disruption activity with increasing concentration of antibiotic used but did not show total disruption at clinical concentration (64 ppm) and even at a very high concentration of
625 ppm (~40% biofilm still remaining compared to control) (Figure 3.3.1.2). These results are similar to previously reported clinical and in vitro studies regarding the minimal biofilm dispersal activity of antibiotics.30
61
Figure 3.3.1.1. Concentration dependent effects of vancomycin against biofilm disruption of S. aureus at concentrations ranging from 2 ppm to 256 ppm. Bacterial cultures made overnight were 5 diluted to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
Figure 3.3.1.2. Concentration dependent effects of bacitracin against biofilm disruption of S. aureus at concentrations ranging from 32 ppm to 625 ppm. Bacterial cultures made overnight were diluted 5 to OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absrbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
62
3.3.2 Concentration Dependent Effect of Amino Acids on S. Aureus Biofilm Disruption
Amino acids have been shown to interfere with the metabolic pathway of the
bacteria.5 Various D-amino acids (tyrosine (tyr), methionine (met), tryptophan (trp), and
leucine(leu)) have been reported to show biofilm inhibition and disruption activities against
several bacterial strains such as S. aureus and P. aeruginosa.28 In this regard, we first tested and evaluated the biofilm dispersal activity of individual D-amino acids (met, trp, tyr, and phenylalanine (phe)) at concentrations close to their maximum solubility. Except for D- met, all of these amino acids are aromatic. Among all the amino acids tested, D-phe showed the highest biofilm disruption activity (~80 %) while the rest have disruption activities less than 20 % (Figure 3.3.2.1). This could be due to the fact that a higher concentration of D-
phe was used (since it has a higher maximum solubility).
Due to the incomplete disruption provided by individual D-amino acids tested, we decided to test a combination of the three aromatic D-amino acids (tyr, trp, and phe) at higher total concentrations (100-200 mM) (Figure 3.3.2.2). From these results, we observed increasing disruption with increasing amino acid concentration and total biofilm disruption was observed at a concentration of 200 mM. Similar results were observed for a combination of similar amino acids but using the L- isomer (Figure 3.3.2.3). In order to evaluate whether addition of another amino acid can help to improve biofilm disruption, a combination of 4 D-amino acids (met, tyr, trp, and phe) were tested from a concentration range of 100 – 250 mM (Figure 3.3.2.4). However, results show similar behavior as that of the 3 amino acid combination with total disruption observed at concentrations greater than
200 mM. We decided to perform a similar biofilm disruption test on vancomycin at a concentration similar to the one that showed effectiveness on the amino acids (200 mM)
63
with the pH unadjusted (pH = 2) and adjusted (pH = 7) as well as on NeutroPhase (a typical
clinical wound cleansing agent made of 0.01 % hypochlorous acid, HClO). Unlike the
amino acids, vancomycin at both pH 2 and 7 did not show complete biofilm disruption
(~85 % activity) while NeutroPhase showed only ~40 % biofilm disruption activity (Figure
3.3.2.5). These tests revealed that the amino acids showed more potent biofilm disruption
activity on the preformed biofilms of S. aureus after 24 h treatment (Figure 3.3.2.2).
HeLa cell toxicity assays were performed on the amino acid and antibiotic treatments. Results showed that bacitracin (625 ppm) and vancomycin (256 ppm) were non-toxic at 24 h incubation while D- and L-amino acids as well as vancomycin at concentrations of 200 mM and NeutroPhase were toxic to HeLa cells (Figure 3.3.2.6).
From these results, we decided to perform a time dependent biofilm disruption assay using
200 mM D-amino acids (15 min to 24 h) in order to find a viable incubation time point that shows significant biofilm disruption as well as non-toxicity to HeLa cells. Figure 3.3.2.7A and B show that biofilm disruption starts to plateau after 2 h with ~85 % disruption activity.
Also, 2h incubation of 200 mM D-amino acids with HeLa cells demonstrates that it is non- toxic (Figure 3.3.2.6). D-amino acids were chosen over L-amino acids because D-amino acids show statistically higher disruption activity compared to L-amino acids at a concentration of 200 mM with 2 h treatment to preformed biofilms (Figure 3.3.2.8). A comparison with similar concentration of vancomycin (200 mM) at pH 2 and 7 showed similar activity (Figure 3.3.2.9) with the amino acids, though it is toxic (Figure 3.3.2.6).
NeutroPhase showed poorer disruption activity (Figure 3.3.2.9) compared to the amino acids and is toxic as well (Figure 3.3.2.6).
64
Figure 3.3.2.1. Concentration dependent effects of individual D-amino acids against biofilm disruption of S. aureus. Bacterial cultures made overnight were diluted to an OD595 of 0.1 and further diluted 100x (~105 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Biofilm dispersive activity of individual D-amino acids D-methionine (D-met) (D-50 mM), D- tryptophan (D-trp) (54 mM), D-tyrosine (D-tyr) (2.5 mM), and D-phenylalanine (D-phe) (143.5 mM) were evaluated. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
Figure 3.3.2.2. Concentration dependent effects of D-amino acid mixture of D-trp, D-tyr, and D-phe against biofilm disruption of S. aureus. Bacterial cultures made overnight were diluted 5 to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Dispersive activity of D-amino acid mixture was evaluated using a 1:22:57 molar ratio of D-tyr, D-trp, and D-phe, respectively. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
65
Figure 3.3.2.3. Concentration dependent effects of L-amino acid mixture of L-trp, L-tyr, and L-phe against biofilm disruption of S. aureus. Bacterial cultures made overnight were diluted 5 to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Dispersive activity of L-amino acid mixture was evaluated using a 1:22:57 molar ratio of L-tyr, L-trp, and L-phe, respectively. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
Figure 3.3.2.4. Concentration dependent effects of a mixture of 4 D-amino acids D-trp, D- tyr, D-phe, and D-met against biofilm disruption of S. aureus. Bacterial cultures made 5 overnight were diluted to an OD595 of 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted o bacterial solution and incubated at 37 C for 24 h. Dispersive activity of D-amino acid mixture was evaluated using a 1:20:22:57 molar ratio of D-tyr, D-met, D-trp, and D-phe, respectively. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
66
Figure 3.3.2.5. Biofilm disruption assay using 200 mM vancomycin (prepared at pH 2 and 7) and NeutroPhase (0.01 % HClO). Bacterial cultures made overnight were diluted to an OD595 of 0.1 and further diluted 100x (~105 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 24 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
Figure 3.3.2.6. Time dependent in vitro cytotoxicity assay demonstrates that D- and L- amino acid mixtures (D-tyr: D-trp: D-phe in 1:22:57 molar ratio, respectively) have limited toxicity up to 2 h exposure but are toxic at 24 h. On the other hand, vancomycin at a concentration of 200 mM and NeutroPhase show toxicity at both 2 and 24 h incubation. Viability of HeLa cells was determined by exposure to media supplemented D- or L-amino o acids for the specified time (2 and 24 h) at 37 C in 5% CO2. Cell viability was determined using the Presto Blue assay by measuring absorbance at 570 and 600 nm. The percent viability is represented relative to non-treated controls.
67
Figure 3.3.2.7. Time dependent biofilm disruption assay using 200 mM of the D-amino acid mixture of D- trp, D-tyr, and D-phe. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following treatment.
Figure 3.3.2.8. Combination treatments showed statistically higher biofilm disruption using a combination of D-amino acids compared to L-amino acids (tyr:trp:phe in 1:22:57 molar ratio, respectively). Bacterial cultures made overnight were diluted to an OD595 of 0.1 and further diluted 100x (~10 5 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 oC for 24 h. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 2 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05. 68
Figure 3.3.2.9. Biofilm disruption assay using 200 mM vancomycin (prepared at pH 2 and 7) and NeutroPhase (0.01 % HClO). Bacterial cultures made overnight were diluted to an OD595 of 5 0.1 and further diluted 100x (~10 CFU/mL) in modified tryptic soy broth (3% NaCl, 0.5% glucose) and each well was filled with 2 mL of the diluted bacterial solution and incubated at 37 o C for 24 h. Biofilm disruption assay was evaluated by comparison of the biofilm biomass relative to that of the positive control by measuring the absorbance of solubilized crystal violet stain at 595 nm following 2 h treatment. An asterisk (*) indicates that the difference of means is statistically significant at p < 0.05 while NS indicates that difference of means is not statistically significant at p < 0.05.
3.4 Magnetic Hyperthermia Performance Evaluation of Fe3O4 Spheres and Cube for Biofilm Disruption
Due to the incomplete disruption of biofilms by D-amino acids that showed non- toxicity towards HeLa cells after 2 h, we decided on evaluating the use of magnetic hyperthermia treatment involving iron oxide nanoparticles that release heat upon excitation with an AC field. To do this, we first synthesized Fe3O4 nanoparticles with spherical (~30
nm) and cubic (25 nm) shapes with similar magnetic volume (Figure 3.4.1A and D). PXRD
patterns of the samples show that both have the magnetite crystallographic phase (Figure
3.4.1B and E). DLS analyses showed that the as-synthesized oleic acid coated nanoparticles did not significantly change in size (based on the hydrodynamic diameter) after conversion to the water phase using a silane-based ligand (Figure 3.4.1C and F).
69
We then compared the magnetic hyperthermia properties of the as-prepared
samples (dispersed in toluene) and the silane-modified one (dispersed in water) for both
shapes at different concentration (250–750 μg Fe/mL) and amplitude (H) (1–5 kA/m).
Magnetic hyperthermia measurements show that cubic shaped samples show higher
heating efficiency (SAR) compared to spherical ones for samples dispersed in toluene and
water due to higher saturation magnetization for cubic shaped samples compared to spheres
(Figure 3.4.2A). Heating efficiency and maximum temperature reached also increased
with higher concentration and amplitude (Figure 3.4.2A and B). In addition, samples
dispersed in toluene showed higher SAR values compared to those dispersed in water
(Figure 3.4.2A and B) and also reached higher temperatures for those in toluene (Figure
3.4.3) compared to those in water (Figure 3.4.4), which is most likely due to the higher
heat capacity of water compared to toluene. HeLa cell toxicity assay shows that both Fe3O4
spheres and cubes are non-toxic at a concentration range of 250-750 μg Fe/mL (Figure
3.4.5).
Figure 3.4.1. TEM images of Fe3O4 spheres (A) and cubes (D) evaluated for magnetic hyperthermia aided disruption of biofilms. Corresponding PXRD patterns of Fe3O4 spheres (B) and cubes (E) showing similar PXRD peaks and pattern as the reference. DLS measurements of the Fe3O4 spheres (C) and cubes (F) before (oleic acid) and after phase conversion to aqueous phase (silane) showing a slight increase in the hydrodynamic diameter (DH) for both particle shapes. 70
Figure 3.4.2. Magnetic hyperthermia measurements were used to calculate for the heating efficiency using the parameter specific absorption rate (SAR in W/g) of the synthesized Fe3O4 spheres and cubes at different concentrations of Fe (250–750 μg Fe/mL) and variable amplitude (1 – 5 kA/m) at a fixed frequency of 380 kHz. (A) SAR values of Fe3O4 spheres and cubes dispersed in toluene showing the cubes having much higher heating efficiency compared to the sphere. (B) A similar observation was observed for Fe3O4 spheres and cubes dispersed in water with the spheres especially showing minimal rise in temperature.
Figure 3.4.3. Magnetic hyperthermia heating profiles of the synthesized Fe3O4 spheres (A– C) and cubes (D–F) dispersed in toluene at different concentrations of Fe (250–750 μg Fe/mL) and variable amplitude (H) (1 – 5 kA/m) at a fixed frequency (f) of 380 kHz. Higher temperatures were reached with increasing concentration and amplitude.
71
Figure 3.4.4. Magnetic hyperthermia heating profile of the synthesized Fe3O4 spheres (A–C) and cubes (D–F) dispersed in water at different concentrations of Fe (250–750 μg Fe/mL) and variable amplitude (H) (1 – 5 kA/m) at a fixed frequency (f) of 380 kHz. Higher temperatures were reached with increasing concentration and amplitude.
Figure 3.4.5. Time dependent in vitro cytotoxicity show that the synthesized Fe3O4 spheres and cubes are non-toxic at 2 h and 24 h treatment for a concentration range from 250 to 750 μg Fe/mL. Viability of HeLa cells was determined by exposure to media supplemented with the nanoparticles o for the specified time (2 and 24 h) at 37 C in 5% CO2. Cell viability was determined using the Presto Blue assay by measuring absorbance at 570 and 600 nm. The percent viability is represented relative to non-treated controls. 72
3.5 Preparation of Magnetic Thermoresponsive Glycol Chitin Hydrogel
Thermoresponsive polymers that undergo temperature dependent sol-gel transition in water are ideal for biomedical applications due to ease of preparation, site-specific drug/treatment delivery, and the absence of the need to use organic solvents.43 In this
regard, we prepared a magnetic thermoresponsive glycol chitin based hydrogel that is non-
toxic and has a gelation temperature that can be tuned by changing the concentration of
glycol chitin. Glycol chitin was prepared via acetylation of the glycol chitosan precursor.
Successful conversion of glycol chitosan to glycol chitin is shown via FT-IR spectroscopy
in Figure 3.5.1 wherein the product did not show an amine peak and has stronger peaks for
the carbonyl and amide groups. 1H NMR spectroscopy also reveal the loss of the amine
group following successful synthesis of glycol chitin (Figure 3.5.2). Concentration and
temperature dependent sol-gel transition at 37 oC is shown in Figures 3.5.3 and 3.5.4 as
indicated by the tube inversion method where gelation is characterized by absence of flow
for 30 s. Changes in viscosity as a function of concentration and temperature were also
evaluated using a rheometer and 5 % wt./vol glycol chitin solution showed an onset of increasing viscosity at a temperature of 37 oC while 1 and 3 % wt./vol glycol chitin solution barely showed minimal changes in viscosity (Figure 3.5.5A and B). Magnetic hyperthermia measurements (H = 5 kA/m; f = 380 kHz) were also performed on these magnetic hydrogels (750 μg Fe/mL; 5 % glycol chitin solution) and it can be observed that a lower increase in temperature was achieved by the sample in the glycol chitin solution (5
oC) compared to the one in water (25 oC), which is due to the viscous nature of the hydrogel
wherein Brownian rotation is inhibited leading to lower heat released (Figure 3.5.6). D-
73 amino acid release studies were also performed showing increasing release of the amino acid with time (Figure 3.5.7 A) and application of hyperthermia (Figure 3.5.7B).
Figure 3.5.1. FT-IR spectroscopy analyses showing the conversion of the precursor glycol chitosan to glycol chitin as revealed by the loss of the amine (-NH2) peak and the presence of stronger carbonyl (-C=O) and amide (-CONH2) peaks in glycol chitin.
1 Figure 3.5.2. H NMR spectra showing the successful conversion of (A) glycol chitosan to (B) glycol chitin as indicated by the absence of the amine (-NH2) peak in the spectrum for glycol chitin.
74
Figure 3.5.3. Sol-gel transition of magnetic nanoparticles incorporated in glycol chitin dissolved in saline (5 % wt in 0.9 % NaCl) solution showing a transition from sol to gel at 37 oC.
Figure 3.5.4. Concentration and temperature dependent sol-gel transition of magnetic nanoparticles incorporated in different glycol chitin concentrations (dissolved in saline, 0.9 %
NaCl) as quantified by the distance travelled by the gel. The maximum distance travelled is 28 mm, which is the length of L.
Figure 3.5.5. Concentration dependence on viscosity changes of glycol chitin solutions (1 – 5 %) as measured by rheometer in (A) over a temperature range of 15–75 oC and (B) comparison of viscosity with changing concentration at 37 oC.
75
Figure 3.5.6. Magnetic hyperthermia heating profile of the synthesized Fe3O4 cubes dispersed in (A) saline solution (0.9 % NaCl) and (B) in 5 % glycol chitin solution (hydrogel) at a concentration of 750 μg Fe/mL, an amplitude (H) of 5 kA/m at a frequency (f) of 380 kHz.
Figure 3.5.7. (A) Cumulative release of D-AA (200 mM) loaded in 5% glycol chitin solution (hydrogel) after 2 h and 24 h. (B) Cumulative release of D-AA (200 mM) loaded in 5% glycol chitin solution (hydrogel) after 2 h and 2 h plus an additional ten minutes of applied magnetic field application using 750 μg Fe/mL at an amplitude (H) of 5 kA/m and a frequency (f) of 380 kHz.
76
3.6 Biofilm Disruption Using D-AA Loaded Magnetic Thermoresponsive Glycol Chitin Hydrogel
The as-prepared D-AA mixture of tyr, trp, and phe (200 mM) loaded magnetic thermoresponsive glycol chitin hydrogel was prepared and evaluated for biofilm disruption activity. A combination of 2 h biofilm disruption and 10 min application of AMF
(hyperthermia at H = 5 kA/m; f = 380 kHz) showed total biofilm disruption and the appropriate controls are also measured for their biofilm disruption activity (Figures 3.6.1 and 3.6.2). Cell toxicity assay showed that the treatment procedure using the glycol chitin solution is non-toxic after 2 h exposure to HeLa cells (Figure 3.6.3). This demonstrates the potential viability of this treatment modality for biofilm disruption.
Figure 3.6.1. Biofilm disruption comparison of the different treatments used in this study. Combined D-AA amino acid (200 mM) treatment for 2 h and additional magnetic hyperthermia treatment for 10 min in a hydrogel matrix) at 750 μg Fe/mL at an amplitude (H) of 5 kA/m and a frequency (f) of 380 kHz showed total disruption.
77
Figure 3.6.2. Crystal violet stained samples of biofilm disruption using different treatments shown in Figure 3.6.1.
Figure 3.6.3. Time dependent in vitro cytotoxicity indicates that the treatments shown in Figure 3.6.1 are non-toxic to HeLa cells for 2 h while those incorporated with amino acids are toxic after 24 h exposure.
78
3.7 Conclusions
We have prepared a thermosetting magnetic hydrogel nanocomposite using a
glycol-chitin based hydrogel for combined thermal and D-amino acid assisted S. aureus
biofilm disruption. The prepared magnetic nanocomposite contains 200 mM D-amino acid mixture of D-tyr, D-trp, and D-phe and 750 μg Fe/mL of Fe3O4 cubic nanoparticles in a
5% glycol chitin solution. Complete disruption of the preformed biofilms was achieved by incubating the sample for 2 h followed by the application of an applied magnetic field
(hyperthermia) for 10 minutes at an amplitude (H) of 5 kA/m and a frequency (f) of 380 kHz. The prepared hydrogel nanocomposite was non-toxic toward HeLa cells when exposed for 2 h.
3.8 References
(1) Song, Z.; Borgwardt, L.; Høiby, N.; Wu, H.; Sørensen, T. S.; Borgwardt, A. Orthop.
Rev. (Pavia). 2013, 5 (2), 14.
(2) Gunn, J. S.; Bakaletz, L. O.; Wozniak, D. J. J. Biol. Chem. 2016, 291 (24), 12538.
(3) Costerton, J. W. Science (80-. ). 1999, 284 (5418), 1318.
(4) Lister, J. L.; Horswill, A. R. Front. Cell. Infect. Microbiol. 2014, 4 (December), 1.
(5) Koo, H.; Allan, R. N.; Howlin, R. P.; Stoodley, P.; Hall-Stoodley, L. Nat. Rev.
Microbiol. 2017.
(6) Sauer, K.; Cullen, M. C.; Rickard, a H.; Zeef, L. a H.; Gilbert, P.; Davies, D. G. J.
Bacteriol. 2004, 186 (21), 7312.
(7) Schleheck, D.; Barraud, N.; Klebensberger, J.; Webb, J. S.; McDougald, D.; Rice,
S. A.; Kjelleberg, S. PLoS One 2009, 4 (5).
(8) Thormann, K. M.; Saville, R. M.; Shukla, S.; Spormann, A. M. J. Bacteriol. 2005,
79
187 (3), 1014.
(9) Barraud, N.; Hassett, D. J.; Hwang, S. H.; Rice, S. A.; Kjelleberg, S.; Webb, J. S.
J. Bacteriol. 2006, 188 (21), 7344.
(10) Barraud, N.; Schleheck, D.; Klebensberger, J.; Webb, J. S.; Hassett, D. J.; Rice, S.
A.; Kjelleberg, S. J. Bacteriol. 2009, 191 (23), 7333.
(11) Barraud, N.; Kelso, M.; Rice, S.; Kjelleberg, S. Curr. Pharm. Des. 2014, 21 (1),
31.
(12) Banin, E.; Brady, K. M.; Greenberg, E. P. Appl. Environ. Microbiol. 2006, 72 (3),
2064.
(13) Musk, D. J.; Banko, D. A.; Hergenrother, P. J. Chem. Biol. 2005, 12 (7), 789.
(14) Rice, S. a; Koh, K. S.; Queck, S. Y.; Labbate, M.; Lam, K. W.; Kjelleberg, S. J.
Bacteriol. 2005, 187 (10), 3477.
(15) Boles, B. R.; Horswill, A. R. PLoS Pathog. 2008, 4 (4).
(16) Davies, D. G.; Marques, C. N. H. J. Bacteriol. 2009, 191 (5), 1393.
(17) Simonetti, O.; Cirioni, O.; Ghiselli, R.; Goteri, G.; Scalise, A.; Orlando, F.;
Silvestri, C.; Riva, A.; Saba, V.; Madanahally, K. D.; Offidani, A.; Balaban, N.;
Scalise, G.; Giacometti, A. Antimicrob. Agents Chemother. 2008, 52 (6), 2205.
(18) Miller, M. B.; Bassler, B. L. Annu. Rev. Microbiol. 2001, 55 (1), 165.
(19) Romling, U.; Galperin, M. Y.; Gomelsky, M. Microbiol. Mol. Biol. Rev. 2013, 77
(1), 1.
(20) McDougald, D.; Rice, S. A.; Barraud, N.; Steinberg, P. D.; Kjelleberg, S. Nat. Rev.
Microbiol. 2011, 10 (1), 39.
(21) Sadekuzzaman, M.; Yang, S.; Mizan, M. F. R.; Ha, S. D. Compr. Rev. Food Sci.
80
Food Saf. 2015, 14 (4), 491.
(22) McDaniel, C. T.; Panmanee, W.; Hassett, D. J. Cyst. Fibros. Light New Res. 2015,
171.
(23) Goss, C. H.; Muhlebach, M. S. J. Cyst. Fibros. 2011, 10 (5), 298.
(24) Vu-Thien, H.; Hormigos, K.; Corbineau, G.; Fauroux, B.; Corvol, H.; Moissenet,
D.; Vergnaud, G.; Pourcel, C. BMC Microbiol. 2010, 10 (Cc), 24.
(25) Lopes, S. P.; Azevedo, N. F.; Pereira, M. O. Biomed Res. Int. 2014, 2014.
(26) Hotterbeekx, A.; Kumar-Singh, S.; Goossens, H.; Malhotra-Kumar, S. Front. Cell.
Infect. Microbiol. 2017, 7 (April), 1.
(27) National Heart, Lung, and B. I. How is cystic fibrosis treated?
https://www.nhlbi.nih.gov/health/health-topics/topics/cf/treatment (accessed Nov
19, 2017).
(28) Kolodkin-Gal, I.; Romero, D.; Cao, S.; Clardy, J.; Kolter, R.; Losick, R. Science
(80-. ). 2010, 328 (5978), 627.
(29) Sanchez, C. J.; Prieto, E. M.; Krueger, C. A.; Zienkiewicz, K. J.; Romano, D. R.;
Ward, C. L.; Akers, K. S.; Guelcher, S. A.; Wenke, J. C. Biomaterials 2013, 34
(30), 7533.
(30) Sanchez, C. J.; Akers, K. S.; Romano, D. R.; Woodbury, R. L.; Hardy, S. K.;
Murray, C. K.; Wenke, J. C. Antimicrob. Agents Chemother. 2014, 58 (8), 4353.
(31) Harmata, A. J.; Ma, Y.; Sanchez, C. J.; Zienkiewicz, K. J.; Elefteriou, F.; Wenke,
J. C.; Guelcher, S. A. Clin. Orthop. Relat. Res. 2015, 473 (12), 3951.
(32) Park, H.; Park, H. J.; Kim, J. A.; Lee, S. H.; Kim, J. H.; Yoon, J.; Park, T. H. J.
Microbiol. Methods 2011, 84 (1), 41.
81
(33) Sturtevant, R. A.; Sharma, P.; Pavlovsky, L.; Stewart, E. J.; Solomon, M. J.;
Younger, J. G. Shock 2015, 44 (2), 121.
(34) Rodrigues, D.; Bañobre-López, M.; Espiña, B.; Rivas, J.; Azeredo, J. Biofouling
2013, 29 (10), 1225.
(35) Situ, S. F.; Cao, J.; Chen, C.; Abenojar, E. C.; Maia, J. M.; Samia, A. C. S.
Macromol. Mater. Eng. 2016, 301 (12), 1525.
(36) Nguyen, T.-K.; Duong, H. T. T.; Selvanayagam, R.; Boyer, C.; Barraud, N. Sci.
Rep. 2016, 5 (1), 18385.
(37) Chen, C.; Chen, L.; Yi, Y.; Chen, C.; Wu, L.; Song, T. Appl. Environ. Microbiol.
2016, 82 (7), 2219.
(38) Geilich, B. M.; Gelfat, I.; Sridhar, S.; van de Ven, A. L.; Webster, T. J. Biomaterials
2017, 119, 78.
(39) Fang, C.-H.; Tsai, P.-I.; Huang, S.-W.; Sun, J.-S.; Chang, J. Z.-C.; Shen, H.-H.;
Chen, S.-Y.; Lin, F. H.; Hsu, L.-T.; Chen, Y.-C. BMC Infect. Dis. 2017, 17 (1), 516.
(40) Ricker, E. B.; Nuxoll, E. Biofouling 2017, 7014 (October), 1.
(41) Nguyen, T.-K.; Duong, H. T. T.; Selvanayagam, R.; Boyer, C.; Barraud, N. Sci.
Rep. 2015, 5 (November), 18385.
(42) Kim, M. H. IEEE Trans. Nanobioscience 2016, 15 (3), 294.
(43) Li, Z.; Cho, S.; Kwon, I. C.; Janát-Amsbury, M. M.; Huh, K. M. Carbohydr. Polym.
2013, 92 (2), 2267.
(44) Muzzarelli, R. A. A.; Greco, F.; Busilacchi, A.; Sollazzo, V.; Gigante, A.
Carbohydr. Polym. 2012, 89 (3), 723.
(45) Bhattarai, N.; Gunn, J.; Zhang, M. Adv. Drug Deliv. Rev. 2010, 62 (1), 83.
82
(46) Park, J.; An, K.; Hwang, Y.; Park, J.-G.; Noh, H.-J.; Kim, J.-Y.; Park, J.-H.; Hwang,
N.-M.; Hyeon, T. Nat. Mater. 2004, 3 (12), 891.
(47) Benner, K.; Benner, K. W.; Worthington, M. A.; Kimberlin, D. W.; Hill, K.;
Buckley, K.; Tofil, N. M. JPPT J Pediatr Pharmacol Ther J Pediatr Pharmacol
Ther 2009, 8614 (2), 86.
(48) Science, H.; State, W.; Medical, A.; Pharmacology, C.; Israel, B.; Medical, D.;
Orleans, N.; Greene, W. L.; Ritchie, D. J.; Rowden, A. M.; Thompson, L. J.; Wynd,
M. a; Diseases, I. 82 Am J Heal. Pharm 2009, 66 (1), 82.
(49) Dudley, M. N.; Mclaughlin, J. C.; Carrington, G.; Frick, J.; Nightingale, C. H.;
Quintiliani, R. Arch Intern Med 1986, 146, 1101.
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Chapter 4: Tuning the Magnetic Relaxation Processes in Superparamagnetic Magnetite-Polyethylene Nanocomposites
4.1 Introduction
Magnetic particle imaging (MPI) is an emerging tracer-based imaging technology introduced by Gleich and Weizenecker in 2005, which allows the direct mapping of magnetic nanoparticle concentration with no depth attenuation or background signal interference.1 Fast scanning modes provide real-time quantitative visualization of the spatial distribution of magnetic nanoparticle tracers at high resolution.2,3 Moreover, MPI is a safe technique for routine analysis because it does not utilize ionizing radiation, unlike other imaging methods such as computed tomography (CT), positron emission tomography
(PET) and single photon emission computed tomography (SPECT).4 Since the inception of
MPI, several reports have been published on hardware improvement, image reconstruction
and tracer design,5–10 but in order to further advance MPI as a clinical imaging method, it
is critical to optimize magnetic nanoparticle tracers, which are key to obtaining good image
quality.11 The current gold standard for MPI studies is the commercial MRI contrast agent,
Resovist®. However, due to its poor size distribution and lack of a theoretical model, it is
challenging to use Resovist® as a tracer for in vivo application in MPI, which hinders the
technique from reaching its full potential in terms of spatial resolution and sensitivity.1,12
Indeed, there is an urgent need to design engineered MPI tracers that would enhance the
overall MPI performance. To date, only a few studies have been undertaken to optimize
nanoparticle size and dispersity, and the effects of magnetic relaxation on MPI are yet to
be fully investigated.2,8,11,12 In particular, it is necessary to understand the effect of particle
mobility on the MPI signal, which can be accomplished by suspending magnetic
nanoparticles in different matrices.
84
MPI has been proposed as a tool to study and characterize in situ wear debris
formation of magnetic polymers in chemical and biological environments using ultra high
molecular weight polyethylene (UHMWPE) as a model polymer.9 UHMWPE (MW of 3-
6 million g/mol) is a dense polymer commonly used in high stress applications and was
employed as a matrix to immobilize the magnetic nanoparticles and restrict their physical
rotation.13–18 UHMWPE is a material widely used as the acetabular component for
prosthetic implants (such as hip and knee joint replacements) due to its excellent wear
resistance and biocomtability.19–22 In spite of these excellent material properties,
UHMWPE wear debris particles, are reported to be the major cause of aseptic loosening in
total joint replacement. Aseptic loosening is bond failure between the implant and the bone
when there is no infection involved due to inadequate initial fixation, mechanical loss of
fixation over time, or biological loss of fixation due to particle-induced osteolysis.23,24 Two methods have been reported to reduce aseptic loosening, which include (1) improving the wear resistance to reduce the amount of wear debris and (2) improving biocompatibility and bioactivity to lower unwanted biological reactions.25 Wear debris particles have
variable sizes and shape as a result of chemical degradation and oxidation, which results in
variable cellular response.24 Thus, MPI can then be adapted for in situ monitoring of wear
debris to better understand the degradation process and find ways to minimize it.
In MPI tracer design, it is common to use magnetic particle spectroscopy (MPS) to
study the response of magnetic tracers when subjected to an alternating current (AC)
magnetic field. MPS is a zero dimensional (0D) analogue of MPI without spatial encoding
component.26 This allows faster evaluation, characterization, and optimization of the
properties of newly designed tracers. In this research work, the matrix-dependent MPI
85
performance of the nanoparticle tracers synthesized were studied using MPS in both system
matrix and x-space MPI formulations.4,12,26
In the system matrix MPI, when an external sinusoidal magnetic field is applied,
the magnetic moments of the nanoparticle tracers align along the applied field direction,
which results in a time-dependent magnetization that resembles a square wave and is
influenced by the nanoparticles' magnetic relaxation. A Fourier Transform of the time-
dependent magnetization will exhibit the transmit frequency and harmonics.4,26 The
collection of these harmonics is referred to as the MPS signal. On the other hand, x-space
MPI uses the point spread function (PSF) to describe the shape of the magnetization
reversal.7,27 The PSF is a product of the magnetization derivative, dm/dH, and the
instrument field gradient, dH/dx, where x is the distance.
Both system matrix and x-space reconstruction approaches are linked by the Fourier
transform. In both cases, the rate of magnetization change of the tracer should be
maximized for a given applied AC field to obtain the best tracer. In system matrix MPI,
this will translate to maximization of the number of harmonics and its intensity. For x- space MPI, faster changes in magnetization in response to an applied field means that the dm/dH height is higher (increased sensitivity) and the full width at half maximum (FWHM) of the PSF is minimized/narrower translating to better spatial resolution.
MPI signals are generated from/or by a combination of Néel relaxation, Brownian rotational diffusion, and hysteretic reversal mechanisms that results from the response of
SPIONs to the applied magnetic field. To optimize the MPS signal, it is essential to understand the nanoparticle's magnetic relaxation, which depends on Brownian ( , the
𝐵𝐵 physical rotation of the nanoparticle) and Néel ( , the internal re-alignment 𝜏𝜏of the
𝜏𝜏𝑁𝑁 86
magnetic moments) relaxation rates as described in equations 4.1.1 and 4.1.2,
respectively:28–31
3 = (4.1.1) 𝜂𝜂𝑉𝑉𝐻𝐻 𝜏𝜏𝐵𝐵 𝑘𝑘𝐵𝐵𝑇𝑇 = exp (4.1.2) 𝐾𝐾𝐴𝐴𝑉𝑉 𝜏𝜏𝑁𝑁 𝜏𝜏0 � � 𝑘𝑘𝐵𝐵𝑇𝑇 where, η is the fluid viscosity, VH is the hydrodynamic volume of the magnetic nanoparticle
core plus surfactant coating, kB is the Boltzmann constant, T is the absolute temperature of
the surrounding media, KA is the anisotropy constant, V is the volume of the magnetic core,
-10 30,31 and τ0 is a constant typically on the order of 10 s. Accordingly, the magnetic
relaxation of particles can be affected by the nanoparticle’s physical characteristics (i.e.,
magnetic core size, crystalline and shape anisotropy, etc.) and their local environment (i.e.,
viscosity of the surrounding matrix, temperature, particle-particle interactions, etc.).4 Using
a magnetite-polyethylene nanocomposite (MPE-NC) as a model system, the magnetic
relaxation of the nanoparticles when the Brownian relaxation was studied since the
particles cannot move when placed in a compressed polymer.
In this study, a novel magnetic nanoparticle loaded UHMWPE nanocomposite
material is used as a development platform for MPI tracer studies. By combining the unique magnetic signatures of the embedded nanoparticles with the robust mechanical and chemical properties of the UHMWPE matrix, we produced a novel magnetic polymer nanocomposite material that is not only suitable for MPI tracer studies, but also for other potential advanced materials applications in which the structural integrity of the nanocomposite can be monitored in real time via MPI.
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4.2 Experimental Section
4.2.1 Materials
Iron (III) chloride hexahydrate, (98%), iron (III) acetylacetonate (99%), oleic acid
(90%), 1-octadecene (90%), toluene, p-xylene, trimethylamine N-oxide (98%) and ultra- high molecular weight polyethylene (UHMWPE, MW 3,000,000 – 6,000,000 g/mol) were purchased from Sigma-Aldrich and used as received. Hexane, sodium oleate and ethanol were purchased from Fischer Scientific and used as received.
4.2.2 Characterization Methods
The size and morphology of the iron oxide nanoparticles were obtained using a
JEOL 1200CX Transmission Electron Microscope (TEM) operated at 80 kV. TEM samples were prepared by drying 5 μL dilute solution of the particles onto a 400 mesh
Formvar coated copper grid. A minimum of 100 particles were counted to evaluate the size distribution using the software ImageJ. Powder X-ray diffraction patterns of the particles were collected using a Rigaku MiniFlex X-ray powder diffractometer using Cu-Kα radiation (λ=0.154 nm). Static field-dependent magnetic properties (M-H curves) of the nanocomposites were measured at 5K and 300K using a Quantum Design MPMS SQUID
(Superconducting Quantum Interference Device) magnetometer. Thermal analysis was performed by heating samples from 25-800 °C at a heating rate of 10°C per minute using a Q500 Thermal Gravimetric Analyzer (TGA) from TA instruments. Attenuated total reflectance FT-Infrared Spectroscopy (ATR-FTIR) was obtained in the range of 600-
4000cm-1 using a Thermo Scientific Nexus 870 ATR-FTIR.
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4.2.3 Synthesis of Iron Oleate Precursor Compound
The iron oleate precursor compound was synthesized according to a previously
32 reported procedure. In a typical experiment, FeCl3•6H2O (20 mmol) and sodium oleate
(60 mmol) were dissolved in a mixture of deionized water (30 mL), ethanol (40 mL) and
hexane (70 mL). The reaction mixture was heated to reflux (58°C) for 4 hours under
vigorous stirring. The resultant product was cooled to room temperature and washed with
warm deionized water for a total of five times. To remove excess solvent from the iron
oleate product, the mixture was vacuum dried at 72 h prior to use.
4.2.4 Synthesis of Iron Oxide Nanoparticles
In a standard experiment for synthesizing 18 nm iron oxide nanoparticles, 18 g (20
mmol) of iron oleate complex, 21 mL (66 mmol) of oleic acid and 75 mL of 1-octadecene were combined in a 250-mL three-neck round bottom reaction flask. Under Ar atmosphere, the reactants were stirred at room temperature for 10 minutes, then heated to 100°C at a rate of 2°C/min. The solution was then heated to 320°C at the same heating rate and kept under reflux for 60 minutes. The reaction product was cooled down to room temperature, washed with toluene and ethanol, and centrifuged for 20 minutes at 7000 rpm. As-prepared, the resulting iron oxide nanoparticles have a wüstite (FeO) crystal structure. To obtain the desired magnetite (Fe3O4) phase, the sample was further oxidized using trimethylamine N-
oxide [(CH3)3NO]. In the oxidation step, 240 mg FeO (3.3 mmol) and 1.02 g (CH3)3NO
(13.6 mmol) were dissolved in 0.5 mL oleic acid (1.6 mmol) and 30 mL 1-octadecene and
mixed in a 100-mL three-neck round bottom reaction flask. The solution was stirred at
room temperature under Ar atmosphere, then heated to 130°C at a heating rate of 10°C/min.
The reaction was held at that temperature for 2 hours after which the temperature was raised
89
to 280°C and held for 1 hour. The reaction was cooled to room temperature, and the
resulting magnetite nanoparticles were washed with toluene and ethanol, and centrifuged
at 7000 rpm for 20 minutes. The washing step was repeated a total of three times and the
final magnetite nanoparticles were isolated and vacuum dried.
4.2.5 Preparation of Magnetite-Polyethylene Nanocomposites (MPE-NCs)
Polyethylene nanocomposites containing 7.5% w/w magnetite (Fe3O4) were
prepared by re-dispersing Fe3O4 nanoparticles (17.7 nm) in toluene in the presence of small amounts of oleic acid, and mixing with ultra-high molecular weight polyethylene
(UHMWPE, Sigma Aldrich MW range 3,000,000 to 6,000,000). The resulting slurry was mixed using a IKA-Vibrax-VXR electric motor for one hour before being placed in a vacuum oven to evaporate the solvent. The dried powder was further mixed using a high- speed blade mixer and compression molded between layers of PTFE sheets and iron steel plates in a Carver Model C laboratory press at 200°C under 7 metric ton of pressure.
4.2.6 Magnetic Particle Spectrometer (MPS) Design Details
A 10 kHz sinusoidal signal was fed to a commercial audio amplifier (QSC
PLX1804), which was then fed to a tuned transmit coil (Length=8 cm, Inner Diameter=2..8 cm, Number of turns=300, Q=9.3). The receive chain consisted of two coils (Receive and
Balance, Length=2 cm, Inner Diameter=1.5 cm, Number of turns =34, 36 respectively).
The samples under test were placed in the receive coil, and the balance coil was used to cancel the feed through signal induced by the transmit field, by feeding both coil signals to a differential amplifier. Before measuring samples, the gain of the balance coil was adjusted so the difference signal was minimized (typical cancellation was 40-45 dB); further fundamental attenuation was accomplished using resonant notch filters. Because
90
fundamental cancellation was imperfect and sources of interference were present (i.e.
fluorescent lighting), a background signal (with no sample in the pickup coil) was acquired
and subtracted from each sample’s signal. The raw time-dependent signal was sampled at
1 MHz using a Maple microprocessor board (LeafLabs Maple Rev5), and analyzed in
MATLAB. All spectra presented here are 10,000-point FFTs and NAverages=3. Total acquisition time for one sample was on the order of one minute.
4.2.7 Sample Preparation for MPS Measurements
Three types of magnetite nanoparticle (Fe3O4-NP) samples in different matrices
were evaluated for MPS studies: (1) freely suspended Fe3O4-NPs in p-xylene; (2) Fe3O4-
NPs embedded in a porous p-xylene-UHMWPE gel (ultra-high molecular weight polyethylene, UHMWPE, gel matrix formed by dissolving the nanoparticles and the
o polymer at 130 C in p-xylene); and (3) Fe3O4-NPs immobilized in a compression molded magnetite-polyethylene nanocomposites (MPE-NC) film, the MPE-NC film was further suspended in 0.5 mL p-xylene during measurement. For all three samples, the total iron concentration was kept at 3.75 mg Fe/mL. The samples were measured using NMR tubes with 7.5 mm internal diameter. For the sample in the porous UHMWPE matrix, the sample mixture was vortex mixed for 30 seconds after which the NMR tube was rapidly heated to
130 C using a heating mantle. As the solution reached 130 C, a gel formed and the NMR tube⁰ was removed from the heating mantle and tapped in ⁰order to remove air bubbles.
Moreover, the NMR tube containing the Fe3O4-NPs in the porous UHMWPE gel matrix was capped to prevent the evaporation of p-xylene from the formed gel. On the other hand,
to prepare the nanocomposites films for MPS measurements, the 7.5 % MPE-NC film were
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cut into small pieces and loaded at the same Fe concentrations into an NMR tube containing
0.5 mL of p-xylene.
4.3 Results and Discussions
4.3.1 Matrix-dependent MPS Behavior of Nanoparticles Using System Matrix Approach
In this work, spherical oleic acid-coated magnetite nanoparticles (Fe3O4-NPs) with
an average diameter of 17.7 ± 0.9 nm (Figure 4.3.1.1 A and B) were synthesized via a
thermal decomposition route adapted from a previously published procedure.32 To
facilitate good nanoparticle dispersion into the polymer matrix, a liquid-solid compounding
33,34 method was adapted. After compounding the Fe3O4-NPs with UHMWPE, a
magnetically responsive and mechanically robust magnetite-polyethylene nanocomposite film (MPE-NC) was fabricated through a compression molding process as illustrated in
Figure 4.3.1.1 C. To maintain the polymer’s molten state for the fabrication process while
ensuring minimal oxidative degradation, the optimal fabrication temperature was identified
to be 200°C. The fabrication temperature was determined by measuring the melting
temperature of UHMWPE using differential scanning calorimetry (DSC) and evaluating
the oxidative degradation of the polymer using attenuated total reflectance-Fourier
transform infrared (ATR-FTIR) spectroscopy (Figure 4.3.1.2). After fabrication,
thermogravimetric analysis (TGA) was performed to determine the resulting concentration
of the embedded Fe3O4 nanoparticles in the MPE-NC.
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Figure 4.3.1.1. (A) Transmission electron microscope (TEM) image of the synthesized oleic acid-coated iron oxide nanoparticles. (Inset: Histogram showing average diameter of 17.7 ± 0.9 nm); (B) Powder x-ray diffraction data obtained from the synthesized iron oxide nanoparticles confirming the magnetite (Fe3O4) crystal phase; (C) Fabrication process for the magnetite-polyethylene nanocomposite (MPE-NC) and interaction of the resulting nanocomposites with an external FeNdB bar magnet; (D) TEM image of a ball–milled magnetite-polyethylene nanocomposite (MPE-NC) sample showing good particle dispersion and preservation of nanoparticle morphology; (E) Hysteresis curves taken at 5 K for a sample containing 0.5% Fe3O4 nanoparticles mixed withpolyethylene powder (Fe3O4-NP + PE) and a corresponding magnetite-polyethylene nanocomposite (MPE-NC) film with the same amount of loaded magnetic nanoparticles. Both samples exhibit similar saturation magnetization indicating that there is no phase change during the compression molding fabrication (i.e. Ms values for the Fe3O4-NPs with PE powder and MPE-NC were measured to be 95.5 emu/g and 90.3 emu/g respectively. The inset shows measurements carried out at 300 K, where both samples demonstrate superparamagnetic behavior (coercivity, Hc = 0 Oe for both samples).
93
Figure 4.3.1.2. Attenuated total reflectance- FTIR (ATR-FTIR) spectra of UHMWPE compressed at different temperatures; highlighted area corresponds to the C=O stretching frequency (1710-1740 cm-1) – its presence is indicative of oxidative degradation of the polymer.
To ensure that no changes in nanoparticle morphology occurred during the
fabrication process, the resulting MPE-NC films were ball-milled and transmission electron microscopy (TEM) images of the obtained sub-micron sized particulate debris revealed that the embedded Fe3O4-NPs were not only well dispersed inside the polymer
matrix, but have also preserved particle size and spherical morphology (Figure 4.3.1.1 D).
Since the magnetic properties of the MPE-NC are crucial for signal generation in MPS, field-dependent magnetic properties (i.e., M-H hysteresis curves) of uncompressed (prior
to compression molding) and compression molded MPE-NCs containing 7.5 % Fe3O4-NPs
were measured using a superconducting quantum interference device (SQUID)
magnetometer. The saturation magnetization (Ms) of the uncompressed and compressed
nanocomposites were evaluated to be 95.5 emu/g and 90.3 emu/g, respectively, which are
in good agreement with the magnetization of bulk magnetite (92 emu/g) as reported by
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Cullity et al.(Figure 4.3.1.1 E).35 In addition, the magnetic hysteresis data obtained at 300
K reveal zero coercivity and remanence (inset, Figure 4.3.1.1 E), which demonstrates that
the magnetic nanoparticles are superparamagnetic at room temperature even after being
embedded in the UHMWPE. From these results, it can be inferred that the morphology,
chemical properties and superparamagnetic properties of the nanoparticles were preserved
in the nanocomposite films, indicative of the resiliency of the nanoparticles with regards
to high temperature and pressure conditions during the compression molding stage.
The MPE-NC serves as a model system in which the Brownian relaxation of the magnetic nanoparticles is fully restricted, allowing the study of the MPS signal and its dependence on particle mobility. All of the MPS measurements were made using a home- built magnetic particle spectrometer (MPS) as illustrated in Figure 4.3.1.3 A. The field amplitude (24.0 mT) used during the measurements was selected after excitation of the
MPE-NC samples with different field strengths (Figure 4.3.1.4 A). The MPS signal of the
MPE-NC sample was compared to free Fe3O4-NPs suspended in p-xylene, which are
mobile and capable of Brownian relaxation, and with Fe3O4-NPs embedded in a porous p-
xylene-UHMWPE gel, which enables partial nanoparticle mobility.
95
Figure 4.3.1.3. (A) Schematic diagram of the magnetic particle spectrometer (MPS) used in this study. (B) Comparison of the MPS signal of Fe3O4-NPs in three different media at a fixed concentration of 3.75 mg Fe/mL. (C) Measured intensity ratios of different harmonics, with signals normalized to the respective third harmonic of each sample.
Figure 4.3.1.4. (A) Field strength dependence of the MPS signal for a MPE-NC with 3.75 mg Fe/mL concentration. (B) Effect of Fe concentration on MPS signal of MPE-NCs; signals were normalized to their respective third harmonics.
The MPS results shown in Figure 4.3.1.3 B represent the full range of nanoparticle mobility. As the effective viscosity of the local environment is increased, the amplitude of the MPS signal is suppressed. Compared to the nanoparticles suspended in pure p-xylene solvent, embedding the nanoparticles in the porous polymeric UHMWPE gel caused a
96 signal-to-noise ratio (SNR; measured at the third harmonic) loss of 30.4%, while immobilizing the nanoparticles in the nanocomposite film caused an overall SNR loss of
48.1%. It is important to note that all three samples were prepared from the same batch of magnetite nanoparticles, and the total mass of iron was kept constant for comparison (3.75 mg Fe/mL). Figure 4.3.1.2 C shows the harmonic ratios of each sample; the ratio of the 5/3 harmonics can be used as a measure of the shape of the magnetization curve.36 Each sample’s signal was normalized to its respective third harmonic, thus, the change in slope of these curves indicates a change in the magnetization curve of each sample. The transition from p-xylene to polymeric gel caused the 5/3 harmonic ratio to drop by 11.5%, while the transition from p-xylene to nanocomposite caused a 22.7% drop in the 5/3 harmonic ratio. The nanoparticles in this comparison should have identical Néel relaxation; the samples were prepared from the same batch of nanoparticles (crystal, shape anisotropy and size distribution were constant) and Néel relaxation is independent of local environment (barring particle interactions, which we show are negligible at these concentrations (Figure 4.3.1.4 B). Thus, the change in magnetization can be attributed to changing the nanoparticle mobility and therefore, Brownian relaxation rate. Our data suggest that as the local viscosity is increased, the MPS signal is suppressed, which is consistent with previous MPS measurements of magnetic nanoparticles with modulated
Brownian relaxations prepared by suspending in high viscosity solvents or embedding in gels.36–38
In order to attribute the difference in MPS signals to varying Brownian relaxation capability, the possibility of a strong particle-particle interaction was investigated. Three
MPE-NC films containing different Fe3O4-NP concentrations (0.1 mg Fe/mL, 1.5 mg
97
Fe/mL and 3.75 mg Fe/mL) were measured. The magnetization responses of the samples
were identical indicating the absence of strong particle interactions (Figure 4.3.1.4 B).
4.3.2 Wear debris MPS behavior of MPE-NCs using x-space approach MPI
To test the feasibility of detection of wear debris via in situ MPI monitoring, MPE-
NCs were prepared and grounded to debris-like sizes (< 500 nm) via ball milling using zirconia balls (Figure 4.3.2.1 A). The debris prepared showed an average size of 326 ± 134 nm via SEM analyses (Figure 4.3.2.1 B and C) while DLS measurements showed an average size of 406 ± 41 nm (Figure 4.3.2.1 D). One common problem with the ball milling approach to generate debris is contamination with zirconia that is a result of the zirconia balls hitting each other during the ball milling process. In order to minimize this, the debris were purified via magnetic separation using a 1 T NdFeB permanent magnet. MPS measurements were then performed on pre-weighed debris (0.012 mg to 0.15 mg) using a micro-analytical balance.
Figure 4.3.2.1. (A) MPE-NC samples after ball milling and magnet separation. (B) SEM image of ball milled samples. (C) Size distribution of the samples based on SEM measurement. (D) DLS size measurement and distribution of debris.
98
MPS measurements of debris with different masses are shown in Figure 4.3.2.2 A
which shows the sensitivity of the MPS system using the x-space approach. The system used can clearly distinguish signals from debris samples down to 0.012 mg by weight. The peak signals from the PSF were then used in order to create a tracer linearity plot as shown in Figure 4.3.2.2 B. The plot clearly shows a linear relationship between MPS signal and the weight of debris. From the plot, the limit of detection (LOD) was calculated to be 9 μg and the limit of quantitation (LOQ) to be 30 μg. This clearly shows that MPS can detect and quantify very small amounts of debris. The ability to quantify small amount of debris demonstrates the possibility to perform in situ monitoring of UHMWPE degradation, which can potentially be used to further understand the mechanisms involved in material degradation as well as size dependent cellular response to the implants. One key cellular response is particle phagocytosis and it has been estimated that 0.2 to 10 μm in diameter samples go through phagocytosis by macrophages.24,39
Figure 4.3.2.2. (A) PSF distribution vs. excitation field plot of debris samples. (B) MPI signal vs. mass of debris show a linear relationship (r2 = 0.997).
99
4.4 Conclusions
In summary, magnetite-polyethylene nanocomposites were fabricated and used to
systematically study the MPS signal. By eliminating the capability for Brownian
relaxation, the nanocomposites served as a development platform for MPI tracer design by
studying the effects of particle mobility and Brownian relaxation on the observed MPI
signal. A significant loss in signal-to-noise-ratio was measured as the nanoparticles were
embedded in progressively more restrictive media. Given these results, the nanocomposites
may be especially useful for optimizing nanoparticles that are to be imaged in a bound
state. In addition, by embedding the Fe3O4 NPs within the polyethylene matrix, we
introduce a distinctive magnetic signature into the polymer material, which can be
exploited for targeted imaging using MPI and can be used to investigate the structural
integrity of the polymeric materials upon exposure to different types of chemical and
mechanical stresses. Furthermore, MPE-NC samples were broken down to the size of
debris (< 500 nm) using the ball milling approach. MPS measurements of these debris
showed that the instrument can be used to detect very small amounts of debris with the
LOD calculated to be 9 μg and the LOQ was 30 μg. This further supports the possibility of
using MPI for in situ wear debris monitoring that will help understand the mechanisms that
govern UHMWPE degradation.
4.5 References
(1) Gleich, B.; Weizenecker, J. Nature 2005, 435 (7046), 1214. (2) Borgert, J.; Schmidt, J. D.; Schmale, I.; Rahmer, J.; Bontus, C.; Gleich, B.; David, B.; Eckart, R.; Woywode, O.; Weizenecker, J.; Schnorr, J.; Taupitz, M.; Haegele, J.; Vogt, F. M.; Barkhausen, J. J. Cardiovasc. Comput. Tomogr. 2012, 6 (3), 149. (3) Krishnan, K. M. IEEE Trans. Magn. 2010, 46 (7), 2523. (4) Rahmer, J.; Weizenecker, J.; Gleich, B.; Borgert, J. BMC Med. Imaging 2009, 9 (1),
100
4.
(5) Gleich, B.; Weizenecker, J.; Borgert, J. Phys. Med. Biol. 2008, 53 (6), N81. (6) Goodwill, P. W.; Saritas, E. U.; Croft, L. R.; Kim, T. N.; Krishnan, K. M.; Schaffer, D. V.; Conolly, S. M. Adv. Mater. 2012, 24 (28), 3870. (7) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imaging 2010, 29 (11), 1851. (8) Khandhar, A. P.; Ferguson, R. M.; Arami, H.; Krishnan, K. M. Biomaterials 2013, 34 (15), 3837.
(9) Pablico-Lansigan, M. H.; Situ, S. F.; Samia, A. C. S. Nanoscale 2013, 5 (10), 4040. (10) Zhang, L.; Dong, W.-F.; Sun, H.-B. Nanoscale 2013, 5 (17), 7664. (11) Ferguson, R. M.; Khandhar, A. P.; Krishnan, K. M. J. Appl. Phys. 2012, 111 (7), 07B318.
(12) Ferguson, R. M.; Minard, K. R.; Khandhar, A. P.; Krishnan, K. M. Med. Phys. 2011, 38 (3), 1619. (13) Shoufan Cao; Hongtao Liu; Shirong Ge; Gaofeng Wu. J. Reinf. Plast. Compos. 2011, 30 (4), 347. (14) Delgado, A.; Addiego, F.; Ahzi, S.; Patlazhan, S.; Toniazzo, V.; Ruch, D. IOP Conf. Ser. Mater. Sci. Eng. 2012, 31, 12009. (15) Galetz, M. C.; Blaβ, T.; Ruckdäschel, H.; Sandler, J. K. W.; Altstädt, V.; Glatzel, U. J. Appl. Polym. Sci. 2007, 104 (6), 4173. (16) Wood, W. J.; Maguire, R. G.; Zhong, W. H. Compos. Part B Eng. 2011, 42 (3), 584. (17) Morley, K. S.; Webb, P. B.; Tokareva, N. V.; Krasnov, A. P.; Popov, V. K.; Zhang, J.; Roberts, C. J.; Howdle, S. M. Eur. Polym. J. 2007, 43 (2), 307. (18) Ingrosso, C.; Martin-Olmos, C.; Llobera, A.; Innocenti, C.; Sangregorio, C.; Striccoli, M.; Agostiano, A.; Voigt, A.; Gruetzner, G.; Brugger, J.; Perez-Murano, F.; Curri, M. L. Nanoscale 2011, 3 (11), 4632. (19) Kurtz, S. M. The UHMWPE Handbook: Ultra-high Molecular Weight Polyethylene in Total Joint Replacement; Elsevier Academic Press: San Diego, CA, 2004.
(20) Chakravarty, R.; Elmallah, R.; Cherian, J.; Kurtz, S.; Mont, M. J. Knee Surg. 2015, 28 (5), 370.
(21) Costa, L.; Jacobson, K.; Bracco, P.; Brach del Prever, E. M. Biomaterials 2002, 23 (7), 1613. (22) Gomez-Barrena, E.; Puertolas, J.-A.; Munuera, L.; Konttinen, Y. T. Acta Orthop. 2008, 79 (6), 832.
101
(23) Fang, W. H.; Hsu, S. M.; Sengers, J. V. NIST Spec. Publ. 1002 2003. (24) Abu-Amer, Y.; Darwech, I.; Clohisy, J. C. Arthritis Res. Ther. 2007, 9 (Suppl 1), S6.
(25) Liu, J.; Zhu, Y.; Wang, Q.; Ge, S. J. China Univ. Min. Technol. 2008, 18 (4), 606. (26) Biederer, S.; Knopp, T.; Sattel, T. F.; Lüdtke-Buzug, K.; Gleich, B.; Weizenecker, J.; Borgert, J.; Buzug, T. M. J. Phys. D. Appl. Phys. 2009, 42 (20), 205007. (27) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imaging 2010, 64 (5–6), 267. (28) Shliomis, M. Sov. Phys. Uspekhi (Engl. transl.) 1974, 17 (2), 153. (29) Martens, M. A.; Deissler, R. J.; Wu, Y.; Bauer, L.; Yao, Z.; Brown, R.; Griswold, M. Med. Phys. 2013, 40 (2), 22303. (30) Weaver, J. B.; Kuehlert, E. Med. Phys. 2012, 39 (5), 2765. (31) Brown, W. F. Phys. Rev. 1963, 130 (5), 1677. (32) Park, J.; An, K.; Hwang, Y.; Park, J.-G.; Noh, H.-J.; Kim, J.-Y.; Park, J.-H.; Hwang, N.-M.; Hyeon, T. Nat. Mater. 2004, 3 (12), 891. (33) Guoliang, P.; Qiang, G.; Aiguo, T.; Zhiqiang, H. Mater. Sci. Eng. A 2008, 492 (1– 2), 383.
(34) Bin, Y.; Yamanaka, A.; Chen, Q.; Xi, Y.; Jiang, X.; Matsuo, M. Polym. J. 2007, 39 (6), 598. (35) Cullity, B. D.; Graham, C. D. Introduction to Magnetic Materials, Second.; Wiley- IEEE Press, 2011.
(36) Rauwerdink, A. M.; Weaver, J. B. J. Magn. Magn. Mater. 2010, 322 (6), 609. (37) Rauwerdink, A. M.; Weaver, J. B. Appl. Phys. Lett. 2010, 96 (3), 33702. (38) Rauwerdink, A. M.; Weaver, J. B. Med. Phys. 2011, 38 (3), 1136. (39) Gelb, H.; Ralph Schumacher, H.; Cuckler, J.; Baker, D. G. J. Orthop. Res. 1994, 12 (1), 83.
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Chapter 5: Research Outlook
In 2004, the first clinical magnetic hyperthermia treatment system using iron oxide nanoparticles was developed at Charité – Medical University of Berlin1 and a few years later, Magforce® obtained European regulatory approval for its product NanoTherm®
therapy to treat patients with brain tumor using magnetic hyperthermia.2 There are ongoing
clinical trials by the same company for other types of cancer such as pancreatic, esophageal,
and prostate cancers and there are current plans in expanding this treatment therapy in
U.S.A.2 In spite of these developments, magnetic hyperthermia is still not widely utilized
in the clinic. There is still a need to develop optimized magnetic nanoparticles with higher
heating efficiency and better biocompatibility. In particular, improvements in
understanding the AC magnetic properties of the IONPs when they are in an immobilized
state, which is what happens in most clinical treatments of diseases, is important in order
to tailor the properties of the nanoparticles to the treatment procedure. It is also crucial to develop new surface chemistries to create highly stable magnetic nanoparticles in aqueous solutions. In addition, detailed toxicity profiles of these new surface coatings should also be addressed.3
Furthermore, the U.S.A has the second most number of magnetic resonance
imaging (MRI) units and the third highest clinical usage in the world.4 Gadolinium-based
contrast agents, widely used in MRI, have known toxicity issues which makes developing alternative contrast agents and imaging techniques imperative.5 While iron oxide-based
MRI contrast agents have been reported to be more biocompatible, a systematic study of
its toxicity profile when used in magnetic particle imaging still needs to be addressed since
103
it is used as a tracer, especially with the proposed development of an MPI guided
hyperthermia (hMPI) imaging and treatment modality.6,7
Overall, the use of iron oxide nanoparticles in clinical applications such as magnetic
hyperthermia and MPI is hinged on the continuing development of highly stable and
biocompatible nanoparticles and a full understanding of its AC magnetic field properties
when placed in different matrices.
5.1 References
(1) Gneveckow, U.; Jordan, A.; Scholz, R.; Brüss, V.; Waldöfner, N.; Ricke, J.;
Feussner, A.; Hildebrandt, B.; Rau, B.; Wust, P. Med. Phys. 2004, 31 (2004), 1444.
(2) Magforce®: The nanomedicine company
http://www.magforce.de/en/unternehmen/ueber-uns.html (accessed Nov 29, 2017).
(3) Janko, C.; Zaloga, J.; Pöttler, M.; Dürr, S.; Eberbeck, D.; Tietze, R.; Lyer, S.;
Alexiou, C. J. Magn. Magn. Mater. 2017, 431, 281.
(4) Health at a Glance 2017; Health at a Glance; OECD Publishing, 2017.
(5) Rogosnitzky, M.; Branch, S. BioMetals 2016, 29 (3), 365.
(6) Imam, S. Z.; Lantz-McPeak, S. M.; Cuevas, E.; Rosas-Hernandez, H.; Liachenko,
S.; Zhang, Y.; Sarkar, S.; Ramu, J.; Robinson, B. L.; Jones, Y.; Gough, B.; Paule,
M. G.; Ali, S. F.; Binienda, Z. K. Mol. Neurobiol. 2015, 52 (2), 913.
(7) Bauer, L. M.; Situ, S. F.; Griswold, M. A.; Samia, A. C. S. Nanoscale 2016, 8 (24),
12162.
104
Bibliography
(1) Abenojar, E. C.; Wickramasinghe, S.; Bas-Concepcion, J.; Samia, A. C. S. Prog.
Nat. Sci. Mater. Int. 2016, 26 (5), 440.
(2) Abu-Amer, Y.; Darwech, I.; Clohisy, J. C. Arthritis Res. Ther. 2007, 9 (Suppl 1),
S6.
(3) Alphandéry, E.; Chebbi, I.; Guyot, F.; Durand-Dubief, M. Int. J. Hyperth. 2013,
29 (8), 801.
(4) Alphandéry, E.; Faure, S.; Seksek, O.; Guyot, F.; Chebbi, I. ACS Nano 2011, 5
(8), 6279.
(5) Andreu, I.; Natividad, E. Int. J. Hyperthermia 2013, 29, 739.
(6) Arami, H.; Ferguson, R. M.; Khandhar, A. P.; Krishnan, K. M. Med. Phys. 2013,
40 (7), 71904.
(7) Atkinson, W. J.; Brezovich, I. A.; Chakraborty, D. P. IEEE Trans. Biomed. Eng.
1984, BME-31 (1), 70.
(8) Baalousha, M.; Lead, J. R. Nat. Nanotechnol. 2013, 8 (5), 308.
(9) Bakoglidis, K. D.; Simeonidis, K.; Sakellari, D.; Stefanou, G.; Angelakeris, M.
IEEE Trans. Magn. 2012, 48 (4), 1320.
(10) Banin, E.; Brady, K. M.; Greenberg, E. P. Appl. Environ. Microbiol. 2006, 72 (3),
2064.
(11) Barnes, M. D.; La Mer, V. K. J. Colloid Sci. 1946, 1 (1), 79.
(12) Barraud, N.; Hassett, D. J.; Hwang, S. H.; Rice, S. A.; Kjelleberg, S.; Webb, J. S.
J. Bacteriol. 2006, 188 (21), 7344.
105
(13) Barraud, N.; Kelso, M.; Rice, S.; Kjelleberg, S. Curr. Pharm. Des. 2014, 21 (1),
31.
(14) Barraud, N.; Schleheck, D.; Klebensberger, J.; Webb, J. S.; Hassett, D. J.; Rice, S.
A.; Kjelleberg, S. J. Bacteriol. 2009, 191 (23), 7333.
(15) Bate, G. Proc. IEEE 1986, 74 (11), 1513.
(16) Bauer, L. M.; Situ, S. F.; Griswold, M. A.; Samia, A. C. S. J. Phys. Chem. Lett.
2015, 6 (13), 2509.
(17) Bauer, L. M.; Situ, S. F.; Griswold, M. A.; Samia, A. C. S. Nanoscale 2016, 8
(24), 12162.
(18) Bean, C. P. J. Appl. Phys. 1955, 26 (11), 1381.
(19) Bean, C. P.; Livingston, J. D. J. Appl. Phys. 1959, 30 (4), S120.
(20) Benner, K.; Benner, K. W.; Worthington, M. A.; Kimberlin, D. W.; Hill, K.;
Buckley, K.; Tofil, N. M. JPPT J Pediatr Pharmacol Ther J Pediatr Pharmacol
Ther 2009, 8614 (2), 86.
(21) Bhattarai, N.; Gunn, J.; Zhang, M. Adv. Drug Deliv. Rev. 2010, 62 (1), 83.
(22) Biederer, S.; Knopp, T.; Sattel, T. F.; Lüdtke-Buzug, K.; Gleich, B.; Weizenecker,
J.; Borgert, J.; Buzug, T. M. J. Phys. D. Appl. Phys. 2009, 42 (20), 205007.
(23) Bin, Y.; Yamanaka, A.; Chen, Q.; Xi, Y.; Jiang, X.; Matsuo, M. Polym. J. 2007,
39 (6), 598.
(24) Boles, B. R.; Horswill, A. R. PLoS Pathog. 2008, 4 (4).
(25) Borgert, J.; Schmidt, J. D.; Schmale, I.; Rahmer, J.; Bontus, C.; Gleich, B.; David,
B.; Eckart, R.; Woywode, O.; Weizenecker, J.; Schnorr, J.; Taupitz, M.; Haegele,
J.; Vogt, F. M.; Barkhausen, J. J. Cardiovasc. Comput. Tomogr. 2012, 6 (3), 149.
106
(26) Brown, W. F. Phys. Rev. 1963, 130 (5), 1677.
(27) Bruice, P. Y. Organic Chemistry, 5th ed.; Prentice Hall, 2006.
(28) Carrey, J.; Mehdaoui, B.; Respaud, M. J. Appl. Phys. 2011, 109 (8).
(29) Chakravarty, R.; Elmallah, R.; Cherian, J.; Kurtz, S.; Mont, M. J. Knee Surg.
2015, 28 (5), 370.
(30) Chen, C.; Chen, L.; Yi, Y.; Chen, C.; Wu, L.; Song, T. Appl. Environ. Microbiol.
2016, 82 (7), 2219.
(31) Chen, Q.; Zhang, Z. J. Appl. Phys. Lett. 1998, 73 (21), 3156.
(32) Chuev, M. a; Hesse, J. J. Phys. Condens. Matter 2007, 19 (50), 506201.
(33) Connord, V.; Mehdaoui, B.; Tan, R.; Carrey, J.; Respaud, M. Rev. Sci. Instrum.
2014, 85 (9), 93904.
(34) Costa, L.; Jacobson, K.; Bracco, P.; Brach del Prever, E. M. Biomaterials 2002,
23 (7), 1613.
(35) Costerton, J. W. Science (80-. ). 1999, 284 (5418), 1318.
(36) Cullity, B. D.; Graham, C. D. Introduction to Magnetic Materials, Second.;
Wiley-IEEE Press, 2011.
(37) Das, R.; Alonso, J.; Nemati Porshokouh, Z.; Kalappattil, V.; Torres, D.; Phan, M.-
H.; Garaio, E.; Garcia, J. A.; Sánchez Llamazares, J. L.; Srikanth, H. J. Phys.
Chem. C 2016, acs. jpcc.6b02006.
(38) Davies, D. G.; Marques, C. N. H. J. Bacteriol. 2009, 191 (5), 1393.
(39) de la Presa, P.; Luengo, Y.; Multigner, M.; Costo, R.; Morales, M. P.; Rivero, G.;
Hernando, A. J. Phys. Chem. C 2012, 116 (48), 25602.
107
(40) Delgado, A.; Addiego, F.; Ahzi, S.; Patlazhan, S.; Toniazzo, V.; Ruch, D. IOP
Conf. Ser. Mater. Sci. Eng. 2012, 31, 12009.
(41) Dennis, C. L.; Ivkov, R. Int J Hyperth. 2013, 29 (8), 715.
(42) Dudley, M. N.; Mclaughlin, J. C.; Carrington, G.; Frick, J.; Nightingale, C. H.;
Quintiliani, R. Arch Intern Med 1986, 146, 1101.
(43) Fang, C.-H.; Tsai, P.-I.; Huang, S.-W.; Sun, J.-S.; Chang, J. Z.-C.; Shen, H.-H.;
Chen, S.-Y.; Lin, F. H.; Hsu, L.-T.; Chen, Y.-C. BMC Infect. Dis. 2017, 17 (1),
516.
(44) Fang, W. H.; Hsu, S. M.; Sengers, J. V. NIST Spec. Publ. 1002 2003.
(45) Ferguson, R. M.; Khandhar, A. P.; Krishnan, K. M. J. Appl. Phys. 2012, 111 (7),
07B318.
(46) Ferguson, R. M.; Minard, K. R.; Khandhar, A. P.; Krishnan, K. M. Med. Phys.
2011, 38 (3), 1619.
(47) Fiorillo, F. Characterization and Measurement of Magnetic Materials; 2004.
(48) Fortin, J. P.; Wilhelm, C.; Servais, J.; Ménager, C.; Bacri, J. C.; Gazeau, F. J. Am.
Chem. Soc. 2007, 129 (9), 2628.
(49) Frenkel, J.; Doefman, J. Nature 1930, 126 (3173), 274.
(50) Fu, R.; Yan, Y. Y.; Roberts, C. AIP Adv. 2015, 5 (12), 127232.
(51) Galetz, M. C.; Blaβ, T.; Ruckdäschel, H.; Sandler, J. K. W.; Altstädt, V.; Glatzel,
U. J. Appl. Polym. Sci. 2007, 104 (6), 4173.
(52) Geilich, B. M.; Gelfat, I.; Sridhar, S.; van de Ven, A. L.; Webster, T. J.
Biomaterials 2017, 119, 78.
108
(53) Gelb, H.; Ralph Schumacher, H.; Cuckler, J.; Baker, D. G. J. Orthop. Res. 1994,
12 (1), 83.
(54) Gilchrist, R. K.; Medal, R.; Shorey, W. D.; Hanselman, R. C.; Parrott, J. C.;
Taylor, C. B. Ann. Surg. 1957, 146 (4), 596.
(55) Giustini, A. J.; Petryk, A. A.; Casssim, S. M.; Tate, J. A.; Baker, I.; Hoopes, P. J.
Nano Life 2010, 1 (01n02), 17.
(56) Gleich, B.; Weizenecker, J. Nature 2005, 435 (7046), 1214.
(57) Gleich, B.; Weizenecker, J.; Borgert, J. Phys. Med. Biol. 2008, 53 (6), N81.
(58) Gneveckow, U.; Jordan, A.; Scholz, R.; Brüss, V.; Waldöfner, N.; Ricke, J.;
Feussner, A.; Hildebrandt, B.; Rau, B.; Wust, P. Med. Phys. 2004, 31 (2004),
1444.
(59) Gomez-Barrena, E.; Puertolas, J.-A.; Munuera, L.; Konttinen, Y. T. Acta Orthop.
2008, 79 (6), 832.
(60) Goodhew, P. J.; Humphreys, J.; Beanland, R. Electron Microscopy and Analysis,
3rd ed.; Taylor & Francis: New York, NY, 2001.
(61) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imag. 2011, 30 (9), 1581.
(62) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imaging 2010, 29 (11), 1851.
(63) Goodwill, P. W.; Conolly, S. M. IEEE Trans. Med. Imaging 2010, 64 (5–6), 267.
(64) Goodwill, P. W.; Saritas, E. U.; Croft, L. R.; Kim, T. N.; Krishnan, K. M.;
Schaffer, D. V.; Conolly, S. M. Adv. Mater. 2012, 24 (28), 3870.
(65) Gordon, R. T.; Hines, J. R.; Gordon, D. Med. Hypotheses 1979, 5 (1), 83.
(66) Goss, C. H.; Muhlebach, M. S. J. Cyst. Fibros. 2011, 10 (5), 298.
(67) Gunn, J. S.; Bakaletz, L. O.; Wozniak, D. J. J. Biol. Chem. 2016, 291 (24), 12538.
109
(68) Guoliang, P.; Qiang, G.; Aiguo, T.; Zhiqiang, H. Mater. Sci. Eng. A 2008, 492
(1–2), 383.
(69) Harmata, A. J.; Ma, Y.; Sanchez, C. J.; Zienkiewicz, K. J.; Elefteriou, F.; Wenke,
J. C.; Guelcher, S. A. Clin. Orthop. Relat. Res. 2015, 473 (12), 3951.
(70) Health at a Glance 2017; Health at a Glance; OECD Publishing, 2017.
(71) Hoo, C. M.; Starostin, N.; West, P.; Mecartney, M. L. J. Nanoparticle Res. 2008,
10 (S1), 89.
(72) Hotterbeekx, A.; Kumar-Singh, S.; Goossens, H.; Malhotra-Kumar, S. Front. Cell.
Infect. Microbiol. 2017, 7 (April), 1.
(73) Hugounenq, P.; Levy, M.; Alloyeau, D.; Lartigue, L.; Dubois, E.; Cabuil, V.;
Ricolleau, C.; Roux, S.; Wilhelm, C.; Gazeau, F.; Bazzi, R. J. Phys. Chem. C
2012, 116 (29), 15702.
(74) Hyeon, T. Chem. Commun. 2003, No. 8, 927.
(75) Imam, S. Z.; Lantz-McPeak, S. M.; Cuevas, E.; Rosas-Hernandez, H.; Liachenko,
S.; Zhang, Y.; Sarkar, S.; Ramu, J.; Robinson, B. L.; Jones, Y.; Gough, B.; Paule,
M. G.; Ali, S. F.; Binienda, Z. K. Mol. Neurobiol. 2015, 52 (2), 913.
(76) Ingrosso, C.; Martin-Olmos, C.; Llobera, A.; Innocenti, C.; Sangregorio, C.;
Striccoli, M.; Agostiano, A.; Voigt, A.; Gruetzner, G.; Brugger, J.; Perez-Murano,
F.; Curri, M. L. Nanoscale 2011, 3 (11), 4632.
(77) Jang, J. T.; Nah, H.; Lee, J. H.; Moon, S. H.; Kim, M. G.; Cheon, J. Angew.
Chemie - Int. Ed. 2009, 48 (7), 1234.
(78) Janko, C.; Zaloga, J.; Pöttler, M.; Dürr, S.; Eberbeck, D.; Tietze, R.; Lyer, S.;
Alexiou, C. J. Magn. Magn. Mater. 2017, 431, 281.
110
(79) Jordan, A.; Wust, P.; Fähling, H.; John, W.; Hinz, A.; Felix, R. Int. J. Hyperth.
2009, 25 (7), 499.
(80) Khandhar, A. P.; Ferguson, R. M.; Arami, H.; Krishnan, K. M. Biomaterials 2013,
34 (15), 3837.
(81) Khandhar, A. P.; Ferguson, R. M.; Krishnan, K. M. In Journal of Applied Physics;
2011; Vol. 109.
(82) Kim, M. H. IEEE Trans. Nanobioscience 2016, 15 (3), 294.
(83) Kim, W.; Suh, C.-Y.; Cho, S.-W.; Roh, K.-M.; Kwon, H.; Song, K.; Shon, I.-J.
Talanta 2012, 94, 348.
(84) Kittel, C. Phys. Rev. 1946, 70 (11–12), 965.
(85) Kolodkin-Gal, I.; Romero, D.; Cao, S.; Clardy, J.; Kolter, R.; Losick, R. Science
(80-. ). 2010, 328 (5978), 627.
(86) Kong, S. D.; Zhang, W.; Lee, J. H.; Brammer, K.; Lal, R.; Karin, M.; Jin, S. Nano
Lett. 2010, 10 (12), 5088.
(87) Koo, H.; Allan, R. N.; Howlin, R. P.; Stoodley, P.; Hall-Stoodley, L. Nat. Rev.
Microbiol. 2017.
(88) Krishnan, K. M. IEEE Trans. Magn. 2010, 46 (7), 2523.
(89) Kuboyabu, T.; Yabata, I.; Aoki, M.; Banura, N.; Nishimoto, K.; Mimura, A.;
Murase, K. Open J. Med. Imaging 2016, 6 (1), 1.
(90) Kurtz, S. M. The UHMWPE Handbook: Ultra-high Molecular Weight
Polyethylene in Total Joint Replacement; Elsevier Academic Press: San Diego,
CA, 2004.
(91) LaMer, V.; Dinegar, R. J. Am. Chem. … 1950, 72 (8), 4847.
111
(92) Lee, J.-H.; Huh, Y.-M.; Jun, Y.; Seo, J.; Jang, J.; Song, H.-T.; Kim, S.; Cho, E.-J.;
Yoon, H.-G.; Suh, J.-S.; Cheon, J. Nat. Med. 2007, 13 (1), 95.
(93) Lee, N.; Yoo, D.; Ling, D.; Cho, M. H.; Hyeon, T.; Cheon, J. Chem. Rev. 2015,
115 (19), 10637.
(94) Lévy, M.; Gazeau, F.; Bacri, J.-C.; Wilhelm, C.; Devaud, M. Phys. Rev. B 2011,
84 (7), 75480.
(95) Li, Z.; Cho, S.; Kwon, I. C.; Janát-Amsbury, M. M.; Huh, K. M. Carbohydr.
Polym. 2013, 92 (2), 2267.
(96) Liang, H.; Zhang, X. B.; Lv, Y.; Gong, L.; Wang, R.; Zhu, X.; Yang, R.; Tan, W.
Acc. Chem. Res. 2014, 47 (6), 1891.
(97) Ling, D.; Hyeon, T. Small 2013, 9 (9–10), 1450.
(98) Lister, J. L.; Horswill, A. R. Front. Cell. Infect. Microbiol. 2014, 4 (December), 1.
(99) Liu, G.; Gao, J.; Ai, H.; Chen, X. Small 2013, 9 (9–10), 1533.
(100) Liu, J.; Zhu, Y.; Wang, Q.; Ge, S. J. China Univ. Min. Technol. 2008, 18 (4), 606.
(101) Lopes, S. P.; Azevedo, N. F.; Pereira, M. O. Biomed Res. Int. 2014, 2014.
(102) Louër, D. In Encyclopedia of Spectroscopy and Spectrometry; Elsevier, 2017; pp
723–731.
(103) Lu, F.; Popa, A.; Zhou, S.; Zhu, J.-J.; Samia, A. C. S. Chem. Commun. (Camb).
2013, 49, 11436.
(104) Magforce®: The nanomedicine company
http://www.magforce.de/en/unternehmen/ueber-uns.html (accessed Nov 29,
2017).
(105) Mamiya, H. J. Nanomater. 2013, 2013, 1.
112
(106) Mamiya, H. J. Nanomater. 2013, 2013, 1.
(107) Martens, M. A.; Deissler, R. J.; Wu, Y.; Bauer, L.; Yao, Z.; Brown, R.; Griswold,
M. Med. Phys. 2013, 40 (2), 22303.
(108) Martinez-Boubeta, C.; Simeonidis, K.; Makridis, A.; Angelakeris, M.; Iglesias,
O.; Guardia, P.; Cabot, A.; Yedra, L.; Estradé, S.; Peiró, F.; Saghi, Z.; Midgley, P.
a; Conde-Leborán, I.; Serantes, D.; Baldomir, D. Sci. Rep. 2013, 3, 1652.
(109) McDaniel, C. T.; Panmanee, W.; Hassett, D. J. Cyst. Fibros. Light New Res.
2015, 171.
(110) McDougald, D.; Rice, S. A.; Barraud, N.; Steinberg, P. D.; Kjelleberg, S. Nat.
Rev. Microbiol. 2011, 10 (1), 39.
(111) McQueeney, R. J.; Yethiraj, M.; Chang, S.; Montfrooij, W.; Perring, T. G.;
Honig, J. M.; Metcalf, P. Phys. Rev. Lett. 2007, 99 (24), 246401.
(112) Mehdaoui, B.; Meffre, A.; Carrey, J.; Lachaize, S.; Lacroix, L. M.; Gougeon, M.;
Chaudret, B.; Respaud, M. Adv. Funct. Mater. 2011, 21 (23), 4573.
(113) Mehdaoui, B.; Tan, R. P.; Meffre, A.; Carrey, J.; Lachaize, S.; Chaudret, B.;
Respaud, M. Phys. Rev. B - Condens. Matter Mater. Phys. 2013, 87 (17), 1.
(114) Miller, M. B.; Bassler, B. L. Annu. Rev. Microbiol. 2001, 55 (1), 165.
(115) Mohr, R.; Kratz, K.; Weigel, T.; Lucka-Gabor, M.; Moneke, M.; Lendlein, A.
Proc. Natl. Acad. Sci. 2006, 103 (10), 3540.
(116) Morley, K. S.; Webb, P. B.; Tokareva, N. V.; Krasnov, A. P.; Popov, V. K.;
Zhang, J.; Roberts, C. J.; Howdle, S. M. Eur. Polym. J. 2007, 43 (2), 307.
(117) Murase, K.; Aoki, M.; Banura, N.; Nishimoto, K.; Mimura, A.; Kuboyabu, T.;
Yabata, I. Open J. Med. Imaging 2015, 5 (2), 85.
113
(118) Murase, K.; Takata, H.; Takeuchi, Y.; Saito, S. Phys. Medica 2013, 29 (6), 624.
(119) Musk, D. J.; Banko, D. A.; Hergenrother, P. J. Chem. Biol. 2005, 12 (7), 789.
(120) Muzzarelli, R. A. A.; Greco, F.; Busilacchi, A.; Sollazzo, V.; Gigante, A.
Carbohydr. Polym. 2012, 89 (3), 723.
(121) Nanotechnology & You: Benefits and Applications
https://www.nano.gov/you/nanotechnology-benefits (accessed Nov 23, 2017).
(122) National Heart, Lung, and B. I. How is cystic fibrosis treated?
https://www.nhlbi.nih.gov/health/health-topics/topics/cf/treatment (accessed Nov
19, 2017).
(123) Natividad, E.; Castro, M.; Mediano, A. Appl. Phys. Lett. 2008, 92.
(124) Natividad, E.; Castro, M.; Mediano, A. J. Magn. Magn. Mater. 2009, 321, 1497.
(125) Nguyen, T.-K.; Duong, H. T. T.; Selvanayagam, R.; Boyer, C.; Barraud, N. Sci.
Rep. 2016, 5 (1), 18385.
(126) Nguyen, T.-K.; Duong, H. T. T.; Selvanayagam, R.; Boyer, C.; Barraud, N. Sci.
Rep. 2015, 5 (November), 18385.
(127) Ortega, D.; Pankhurst, Q. A. Nanosci. Vol. 1 Nanostructures through Chem. 2013,
1, 60.
(128) Pablico-Lansigan, M. H.; Situ, S. F.; Samia, A. C. S. Nanoscale 2013, 5 (10),
4040.
(129) Panagiotopoulos, N.; Vogt, F.; Barkhausen, J.; Buzug, T. M.; Duschka, R. L.;
Lüdtke-Buzug, K.; Ahlborg, M.; Bringout, G.; Debbeler, C.; Gräser, M.;
Kaethner, C.; Stelzner, J.; Medimagh, H.; Haegele, J. Int. J. Nanomedicine 2015,
3097.
114
(130) Park, H.; Park, H. J.; Kim, J. A.; Lee, S. H.; Kim, J. H.; Yoon, J.; Park, T. H. J.
Microbiol. Methods 2011, 84 (1), 41.
(131) Park, J.; An, K.; Hwang, Y.; Park, J.-G.; Noh, H.-J.; Kim, J.-Y.; Park, J.-H.;
Hwang, N.-M.; Hyeon, T. Nat. Mater. 2004, 3 (12), 891.
(132) Pernia Leal, M.; Torti, A.; Riedinger, A.; La Fleur, R.; Petti, D.; Cingolani, R.;
Bertacco, R.; Pellegrino, T. ACS Nano 2012, 6 (12), 10535.
(133) Puig, J.; Hoppe, C. E.; Fasce, L. A.; Pérez, C. J.; Piñeiro-Redondo, Y.; Bañobre-
López, M.; López-Quintela, M. A.; Rivas, J.; Williams, R. J. J. J. Phys. Chem. C
2012, 116 (24), 13421.
(134) Rahmer, J.; Weizenecker, J.; Gleich, B.; Borgert, J. BMC Med. Imaging 2009, 9
(1), 4.
(135) Rauwerdink, A. M.; Weaver, J. B. Appl. Phys. Lett. 2010, 96 (3), 33702.
(136) Rauwerdink, A. M.; Weaver, J. B. J. Magn. Magn. Mater. 2010, 322 (6), 609.
(137) Rauwerdink, A. M.; Weaver, J. B. Med. Phys. 2011, 38 (3), 1136.
(138) Reddy, L. H.; Arias, J. L.; Nicolas, J.; Couvreur, P. Chem. Rev. 2012, 112, 5818.
(139) Rice, S. a; Koh, K. S.; Queck, S. Y.; Labbate, M.; Lam, K. W.; Kjelleberg, S. J.
Bacteriol. 2005, 187 (10), 3477.
(140) Ricker, E. B.; Nuxoll, E. Biofouling 2017, 7014 (October), 1.
(141) Riedinger, A.; Guardia, P.; Curcio, A.; Garcia, M. A.; Cingolani, R.; Manna, L.;
Pellegrino, T. Nano Lett. 2013, 13 (6), 2399.
(142) Rodrigues, D.; Bañobre-López, M.; Espiña, B.; Rivas, J.; Azeredo, J. Biofouling
2013, 29 (10), 1225.
(143) Rogosnitzky, M.; Branch, S. BioMetals 2016, 29 (3), 365.
115
(144) Romling, U.; Galperin, M. Y.; Gomelsky, M. Microbiol. Mol. Biol. Rev. 2013, 77
(1), 1.
(145) Rosensweig, R. E. E. J. Magn. Magn. Mater. 2002, 252 (0), 370.
(146) Sadekuzzaman, M.; Yang, S.; Mizan, M. F. R.; Ha, S. D. Compr. Rev. Food Sci.
Food Saf. 2015, 14 (4), 491.
(147) Salas, G.; Camarero, J.; Cabrera, D.; Takacs, H.; Varela, M.; Ludwig, R.;
D??hring, H.; Hilger, I.; Miranda, R.; Morales, M. D. P.; Teran, F. J. J. Phys.
Chem. C 2014, 118 (34), 19985.
(148) Sanchez, C. J.; Akers, K. S.; Romano, D. R.; Woodbury, R. L.; Hardy, S. K.;
Murray, C. K.; Wenke, J. C. Antimicrob. Agents Chemother. 2014, 58 (8), 4353.
(149) Sanchez, C. J.; Prieto, E. M.; Krueger, C. A.; Zienkiewicz, K. J.; Romano, D. R.;
Ward, C. L.; Akers, K. S.; Guelcher, S. A.; Wenke, J. C. Biomaterials 2013, 34
(30), 7533.
(150) Sauer, K.; Cullen, M. C.; Rickard, a H.; Zeef, L. a H.; Gilbert, P.; Davies, D. G.
J. Bacteriol. 2004, 186 (21), 7312.
(151) Saville, S. L.; Qi, B.; Baker, J.; Stone, R.; Camley, R. E.; Livesey, K. L.; Ye, L.;
Crawford, T. M.; Thompson Mefford, O. J. Colloid Interface Sci. 2014, 424, 141.
(152) Schleheck, D.; Barraud, N.; Klebensberger, J.; Webb, J. S.; McDougald, D.; Rice,
S. A.; Kjelleberg, S. PLoS One 2009, 4 (5).
(153) Science, H.; State, W.; Medical, A.; Pharmacology, C.; Israel, B.; Medical, D.;
Orleans, N.; Greene, W. L.; Ritchie, D. J.; Rowden, A. M.; Thompson, L. J.;
Wynd, M. a; Diseases, I. 82 Am J Heal. Pharm 2009, 66 (1), 82.
116
(154) Serantes, D.; Baldomir, D.; Pereiro, M.; Hernando, B.; Prida, V. M.; Sánchez
Llamazares, J. L.; Zhukov, a; Ilyn, M.; González, J. J. Phys. D. Appl. Phys. 2009,
42 (21), 215003.
(155) Serantes, D.; Simeonidis, K.; Angelakeris, M.; Chubykalo-Fesenko, O.;
Marciello, M.; Del Puerto Morales, M.; Baldomir, D.; Martinez-Boubeta, C. J.
Phys. Chem. C 2014, 118 (11), 5927.
(156) Shliomis, M. Sov. Phys. Uspekhi (Engl. transl.) 1974, 17 (2), 153.
(157) Shoufan Cao; Hongtao Liu; Shirong Ge; Gaofeng Wu. J. Reinf. Plast. Compos.
2011, 30 (4), 347.
(158) Simonetti, O.; Cirioni, O.; Ghiselli, R.; Goteri, G.; Scalise, A.; Orlando, F.;
Silvestri, C.; Riva, A.; Saba, V.; Madanahally, K. D.; Offidani, A.; Balaban, N.;
Scalise, G.; Giacometti, A. Antimicrob. Agents Chemother. 2008, 52 (6), 2205.
(159) Situ, S. F.; Cao, J.; Chen, C.; Abenojar, E. C.; Maia, J. M.; Samia, A. C. S.
Macromol. Mater. Eng. 2016, 301 (12), 1525.
(160) Skoog, D.; West, D.; Holler, F. J.; Crouch, S. Fundamentals of Analytical
Chemistry, 9th ed.; Brooks/Cole: Belmont, CA, 2014.
(161) Song, Z.; Borgwardt, L.; Høiby, N.; Wu, H.; Sørensen, T. S.; Borgwardt, A.
Orthop. Rev. (Pavia). 2013, 5 (2), 14.
(162) Stoner, E. C.; Wohlfarth, E. P. Philos. Trans. R. Soc. A Math. Phys. Eng. Sci.
1948, 240 (826), 599.
(163) Sturtevant, R. A.; Sharma, P.; Pavlovsky, L.; Stewart, E. J.; Solomon, M. J.;
Younger, J. G. Shock 2015, 44 (2), 121.
117
(164) Thormann, K. M.; Saville, R. M.; Shukla, S.; Spormann, A. M. J. Bacteriol. 2005,
187 (3), 1014.
(165) Usov, N. A.; Liubimov, B. Y. J. Appl. Phys. 2012, 112 (2), 23901.
(166) Vallejo-Fernandez, G.; Whear, O.; Roca, A. G.; Hussain, S.; Timmis, J.; Patel, V.;
O’Grady, K. J. Phys. D. Appl. Phys. 2013, 46 (31), 312001.
(167) Varón, M.; Beleggia, M.; Kasama, T.; Harrison, R. J.; Dunin-Borkowski, R. E.;
Puntes, V. F.; Frandsen, C. Sci. Rep. 2013, 3, 1234.
(168) Vu-Thien, H.; Hormigos, K.; Corbineau, G.; Fauroux, B.; Corvol, H.; Moissenet,
D.; Vergnaud, G.; Pourcel, C. BMC Microbiol. 2010, 10 (Cc), 24.
(169) Weaver, J. B.; Kuehlert, E. Med. Phys. 2012, 39 (5), 2765.
(170) Weizenecker, J.; Gleich, B.; Rahmer, J.; Dahnke, H.; Borgert, J. Phys. Med. Biol.
2009, 54 (5).
(171) Wildeboer, R. R.; Southern, P.; Pankhurst, Q. A. J. Phys. D. Appl. Phys. 2014, 47
(49), 495003.
(172) Wood, W. J.; Maguire, R. G.; Zhong, W. H. Compos. Part B Eng. 2011, 42 (3),
584.
(173) Wu, W.; He, Q.; Jiang, C. Nanoscale Res. Lett. 2008, 3 (11), 397.
(174) Yakacki, C.; Satarkar, N.; Gall, K.; Likos, R.; Hilt, Z. J. Appl. Polym. Sci. 2009,
112, 3166.
(175) Zhang, L.; Dong, W.-F.; Sun, H.-B. Nanoscale 2013, 5 (17), 7664.
(176) Zheng, B.; Vazin, T.; Goodwill, P. W.; Conway, A.; Verma, A.; Ulku Saritas, E.;
Schaffer, D.; Conolly, S. M. Sci. Rep. 2015, 5 (September), 1.
118
(177) Zheng, B.; Von See, M. P.; Yu, E.; Gunel, B.; Lu, K.; Vazin, T.; Schaffer, D. V.;
Goodwill, P. W.; Conolly, S. M. Theranostics 2016, 6 (3), 291.
119