optimisation of properties for magnetic hyperthermia ... · aims: identification of optimum...
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Optimisation of properties for magnetic hyperthermia beyond LRT
S. Ruta1, E. Rannala1, D. Serantes1,2 , O. Hovorka3 , R. Chantrell1
1Department of Physics, University of York, York YO10 5DD, U.K.2IIT and Appl. Phys. Dept., Universidade de Santiago de Compostela, 15703, Spain.
3Faculty of Engineering and the Environment, University of Southampton, U.K.
15 July 2019, Santiago de Compostela
Outline
1. Magnetic nanoparticle hyperthermia (MNH)1. Motivation2. Basic ideas3. Problems/Aims
2. LRT and kMC model1. How to calculate the heating
2. Limitation of LRT
3. Beyond LRT1. High efficiency of transition region
2. Using kMC to extrapolate beyond LRT
4. Conclusions
Magnetic hyperthermia
Magnetic particles will heat up
Apply an AC magnetic field
ΔU = Q − L
∫ M⃗⋅⃗dH
SAR (Specific absorption rate)=Energytime⋅mass
Magnetic particles will heat up
Cancer cells are more sensitive to heat
Possibility of developing a non-invasive cancer treatment
Biomedical limitation [1]:
f Hmax<6⋅107Oe / s
[1] Hergt, R., & Dutz, S. (2007). Magnetic particle hyperthermia—biophysical limitations of a visionary tumour therapy. Journal of Magnetism and Magnetic Materials, 311(1), 187–192.
Magnetic hyperthermia
ΔU = Q − L
∫ M⃗⋅⃗dH
SAR (Specific absorption rate)=Energytime⋅mass
Apply an AC magnetic field
Motivation – cancer treatment● Magnetic hyperthermia is a promising methodology for cancer treatment.
Clinical application since 2013 glioblastoma multiforme
Basic ideas
Clinical requisites: Accurate ΔT: Ttreatment ~ 43º - 47ºC Biocompatibility (composition; coating; dose) Size ~ 10 -100 nm
Limited HAC(f*Hmax
< 6*107 Oe/s): Hmax~[5-200] Oe; f~[0.1-1] MHz
Characterization: Specific Absorption Rate (SAR)
Basic ideas
• Experiment: • Theory: SAR= HL∙fSAR= cpΔT/Δt
M/M
S
H/HA
Clinical requisites: Accurate ΔT: Ttreatment ~ 43º - 47ºC Biocompatibility (composition; coating; dose) Size ~ 10 -100 nm
Limited HAC(f*Hmax
< 6*107 Oe/s): Hmax~[5-200] Oe; f~[0.1-1] MHz
Characterization: Specific Absorption Rate (SAR)
Aims: Identification of optimum condition
● Magnetic hyperthermia is a promising methodology for cancer treatment.
● The ability to predict particle heating is crucial for:● Controlling the heating inside the human body.● synthesizing the particles with optimal properties.
● Study of:● Intrinsic properties and their distribution
(particle size, anisotropy value, easy axis orientation).
● Extrinsic properties (AC magnetic field amplitude, AC field frequency).
● The role of dipole interactions.● Environment effects (heat difuzion, Brownian
rotation, change of particle properties). (not considered here)
Outline
1. Magnetic nanoparticle hyperthermia (MNH)1. Motivation2. Basic ideas3. Problems/Aims
2. LRT and kMC model1. How to calculate the heating
2. Limitation of LRT
3. Beyond LRT1. High efficiency of transition region
2. Using kMC to extrapolate beyond LRT
4. Conclusions
How to calculate the heating?
Properties LRT kMC Other(Metropolis MC, LLG)
Intrinsic properties and their distribution (particle size, anisotropy value, easy axis orientation)
Yes Yes Compromise
Extrinsic properties (AC magnetic field amplitude, AC field frequency)
No Yes Compromise
The role of dipole interactions No Yes Compromise
Calculate the heating: Master equation
[2] E. Stoner and E. Wohlfarth, “A mechanism of magnetic hysteresis in heterogeneous alloys,” Philosophical Transactions of the Royal Society of London. Series A, Mathematical and Physical Sciences, vol. 240, no. 826, pp. 599–642, 1948.
E tot=−KVcos2 (θ )−M sVH ap cos (θ−θ0 )
Anisotropy energy Zeeman energy
● Consider SW theory for mono-domain particle with uniaxial anisotropy at 0K [2].
Hap
is the applied field
M is the magnetization vector
e.a is the easy axis
θ is the angle between magnetization vector and easy axis
Φ is the angle between the field
direction and easy axis
Calculate the heating: Master equation
Angle between easy axis and magnetic moment
dP1
dt=−W 12 P1+W 21P2
dP2
dt=−W 21P2+W 12P1
dP1
dt=
1τ (W 21 τ−P1)
dM ( t)dt
=1τ (M 0(t )−M ( t) )
P1+P2=1
W 12 ,W 21=f 0e
−ΔE1,2
K B T
τ=1
(W 12+W 21)
Calculate the heating: LRT
dM ( t)dt
=1τ (M 0(t )−M ( t) )
● Power dissipation:
● Imaginary susceptibility:
● Neel Relaxation time:
● Equilibrium susceptibility:
P=∫MdH=f H 02 2π f∫0
1/ f
χ' ' sin2
(2π f t )dt
χ' '=χ0
1+(2π f τ)22π f τ
τ=1
2 f 0
eKVK bT
χ0=[ M s L(α)
H ]H=0
f 0=109Hz
α=M sV H
K bT
P=πχ' ' f Hmax
2
Linear Response Theory
SAR=P
mass
Hmax
=300 Oe
For a particular set of parameters (f,H,K,Ms,V);
L
f*Hmax< 6*107 Oe/s
Limitation of LRT
τ=1
2 f 0
eKVKbT
χ0= [ dL(α)
dH ]H=0
α=M sV H
KbT
● Neel Relaxation time:
● Equilibrium susceptibility (*):
W 12(H (t )= 1f 0
exp (−KVkbT
(1+H (t )H k
)n
))
W 21(H (t ))=1f 0
exp(−KVk bT
(1−H (t)H k
)n
)
τ (H ( t ))=1
W 12(H (t ))+W 21(H ( t ))
H /Hk≪1
χ' '=
χ0
1+(2 π f τ)2 2π f τ Where
L(α)=1/3.0 α−1/45α3+2/945α
5+... χ0=1 /3.0
α<0.5
H <0.5KbT
M sV
Calculate the heating: LRT and kMC
LRT-approximation Exact behaviour
Imaginary susceptibility
Néel Relaxation time
Equilibrium susceptibility
Power dissipation
χ' '(τ ,χ0 )
τ(K ,V ,T )
χ0(M s ,V ,T )
χ' ' (τ (H (t )) ,χ0(H (t )))
τ(K ,V ,T , H (t ))
χ0(M s ,V ,T ,H (t ))
P=πχ ' ' f H 02
P=?
H=Hmaxcos (2π f t)
M=H max[ χ' cos(2π f t )+χ
' ' sin (2π f t)]
dM ( t)dt
=1τ (M 0(t )−M ( t) )
H /Hk≪1
H <0.5KbT
M sV
Conditions for LRT
Calculate the heating: LRT and kMC
dM ( t)dt
=1τ (M 0(t )−M ( t) )
Kinetic Monte Carlo (kMC)
LRT-approximation Exact behaviour
Imaginary susceptibility
Néel Relaxation time
Equilibrium susceptibility
Power dissipation
χ' '(τ ,χ0 )
τ(K ,V ,T )
χ0(M s ,V ,T )
χ' ' ( τ (H (t )) ,χ0(H (t )))
τ(K ,V ,T , H (t ))
χ0(M s ,V ,T ,H (t ))
P=πχ ' ' f H 02
P=?
Calculate the heating: kMC
The model [3] includes all the complexity of a real system:
Distributions of particle volumes. Distributions of particle anisotropy value. Random distributions of uniaxial
anisotropy vectors. Thermal activation is included in the
Kinetic Monte-Carlo model, allowing capturing both superparamagnetic and hysteretic regimes.
Inter-particle interaction are modelled as dipole-dipole interactions.
Various spatial arrangements of nanoparticles are considered.
M. L. Etheridge, K. R. Hurley, J. Zhang, S. Jeon, H. L. Ring, C. Hogan, C. L. Haynes, M. Garwood, and J. C. Bischof, “Accounting for biological aggregation in heating and imaging of magnetic nanoparticles.,” Technology, vol. 2, no. 3, pp. 214–228, Sep. 2014.
● We analyze the optimum conditions:
– The maximum SAR and
– The diameter corresponding to the maximum SAR.
L
Deviation from LRT
LRT LRT
● For small magnetic field the kMC is in agreements with RT.
● With increasing H the RT overestimates the maximum SAR and the optimal particle size.
● SAR vs H is quadratic ( in agreement with RT) in small fields, but becomes linear with increasing H.
L
Deviation from LRT
LRT LRT
Investigated system
● Spherical magnetite nanoparticles
● Ms=400 emu/cm3
● K=(0.5-3) x105 erg/cm3
● f=105 Hz; H0=300 Oe
● A real system will have:
– Distribution of easy axis
– distribution of particle size and anisotropy
– Magnetostatic interaction
K=1.5
K=0.5
K=3.0
K=3.0 K=1.5 K=0.5
3 main regions
3 magnetic regions:
1) Low field (linear approximation) regime: H/Hk<<1.
2) Intermediate (transition) regime.
3) Large field ( full hysteretic) regime.
3
2
1
123 123
High efficiency of transition regime
3 magnetic regions:
1) Low field (linear approximation) regime: H/Hk<<1.
2) Intermediate (transition) regime.
3) Large field ( full hysteretic) regime.
3
21
13 13 2
Transition regime close to fully hysteretic regime is ideal for magnetic hyperthermia
Transition regime close to fully hysteretic regime is ideal for magnetic hyperthermia
Transition regime close to fully hysteretic regime is ideal for magnetic hyperthermia
0.9 0.8 0.75
Transition regime close to fully hysteretic regime is ideal for magnetic hyperthermia
0.9 0.8 0.75
For SAR>0.9 of max SARD: >5nm tolerance
For SAR: >0.9 of max SARD: <2nm tolerance
Effect of interactions
Effect of interactions
Conclusions (part 1)● Magnetic behaviour can be categorize in
3 regions in terms of the applied field:
– a) low field region: linear approximation theory can be used.
– b) large field region: where full hysteresis models are applicable.
– c) transition region: ideal for magnetic hyperthermia: large SAR, less sensitive to size.
● Inter-particle interaction must be also considered.
Outline
1. Magnetic nanoparticle hyperthermia (MNH)1. Motivation2. Basic ideas3. Problems/Aims
2. LRT and kMC model1. How to calculate the heating
2. Limitation of LRT
3. Beyond LRT1. High efficiency of transition region
2. Using kMC to extrapolate beyond LRT
4. Conclusions
P=πχ ' ' f Hmax2
Linear Response Theory
SAR=P
mass
Hmax
=300 Oe
For a particular set of parameters (f,H,K,Ms,V);
L
f*Hmax< 6*107 Oe/s
● Magnetic hyperthermia is a promising methodology for cancer treatment.
● The ability to predict particle heating is crucial for:
– synthesizing the particles with optimal properties.
● Linear Response Theory (LRT) is used for prediction of SAR.
Can we extend the LRT prediction?
P=πχ' ' f H max
2
Optimum condition
L
Optimum SAR=?Optimum D=?
Beyond LRT
● LRT is limited to small field for which SAR is also small;
● kMC can be used to predict SAR beyond LRT limit;
● Disadvantage of kMC is that it depends on a large set of parameters (f,H
max,K,M
s,V);
HmaxHmax
Beyond LRT
● LRT is limited to small field for which SAR is also small;
● kMC can be used to predict SAR beyond LRT limit;
● Disadvantage of kMC is that it depends on a large set of parameters (f,H
max,K,M
s,V);
Hmax
Normalise parameters
Etot=−KV [ cos2( θ )+2
HapHK
cos (θ−θ0 ) ]
E tot=−KVcos2 (θ )−M sVH apcos (θ−θ0 )
For a particular set of parameters (f,H,K,Ms,V):
SAR→SAR
SAR (KV )
D→KVkBT
H→HH K
General picture
SAR→SAR
SAR(KV )
D→KVk B T
H→HH K
General picture
H max
H K
For a particular value of frequency (f);
Extrapolation of optimum SAR from LRT
SARSAR (KV )
∝[ H /Hk ]2
SARSAR (KV )
∝[ H /Hk ]
SAR=SAR( LRT )∝ [H /Hk ]2, H / Hk< 0.1
SARSAR (KV )
=SAR(LRT ) HHK
=0.1+g2 ( f ) [H /Hk−0.1 ] ,H /Hk∈[0.1,0 .3 ]
Extrapolation of optimum SAR from LRT
KVkBT
=KVkBT
(LRT )+ g1( f ) [ H /Hk ]2
Prediction beyond LRT
L
KVkBT
=8.22+47 [ H /Hk ]2⇒D=13.85nm
SAR=SAR(KV ) [0.2+3.89 (H /Hk−0.1 ) ]⇒ SAR=676W / g
Hmax
=300 Oe
Prediction beyond LRT
L
KVkBT
=8.22+47 [ H /Hk ]2⇒D=13.85nm
SAR=SAR(KV ) [0.2+3.89 (H /Hk−0.1 ) ]⇒ SAR=676W / g
Conclusion (part 2)
Can we extend the LRT prediction? Yes
● kMC can be used to predict SAR beyond LRT limit;
● For a given frequency:● We have a general picture● The optimum condition can be analytical predicted:
KV ∝ [H /Hk ]2
SAR∝ [H /Hk ] 2 ,H /Hk<0.1
SAR∝ [H /Hk ] ,H /Hk∈[0.1,0 .3 ]
H max
H K