run_MC_ISO_TUN {RLumCarlo} | R Documentation |
Monte-Carlo Simulation for ISO-TL (tunnelling transitions)
Description
Runs a Monte-Carlo (MC) simulation of isothermally stimulated luminescence (ISO-TL or ITL) using the tunnelling (TUN) model. Tunnelling refers to quantum mechanical tunnelling processes from the excited state of the trapped charge, into the recombination centre.
Usage
run_MC_ISO_TUN(
E,
s,
T = 200,
rho,
times,
clusters = 10,
r_c = 0,
delta.r = 0.1,
N_e = 200,
method = "par",
output = "signal",
...
)
Arguments
E |
numeric (required): Thermal activation energy of the trap (eV). |
s |
numeric (required): The effective frequency factor for the
tunnelling process ( |
T |
numeric (with default): Constant stimulation temperature (°C). |
rho |
numeric (required): The dimensionless density of recombination centres
(defined as |
times |
numeric (required): The sequence of time steps within the simulation (s). |
clusters |
numeric (with default): The number of created clusters for the MC runs. The input can be the output of create_ClusterSystem. In that case |
r_c |
numeric (with default): Critical distance (>0) that must be provided if the
sample has been thermally and/or optically pretreated. This parameter expresses the fact
that electron-hole pairs within a critical radius |
delta.r |
numeric (with default): Fractional change of the dimensionless distance of nearest recombination centres (r') |
N_e |
numeric (width default): The total number of electron traps available (dimensionless).
Can be a vector of |
method |
character (with default): Sequential |
output |
character (with default): output is either the |
... |
further arguments, such as |
Details
The model
I_{TUN}(r',t) = -dn/dt = (s * exp(-E/(k_{B}*T_{ISO}))) * exp(-(\rho')^{-1/3} * r') * n (r',t)
Where in the function:
E := thermal activation energy (eV)
s := the effective frequency factor for the tunnelling process (s^-1)
T_{ISO}
:= the temperature of the isothermal experiment (°C)
k_{B}
:= Boltzmann constant (8.617 x 10^-5 eV K^-1)
r' := the dimensionless tunnelling radius
\rho
' := rho
the dimensionless density of recombination centres see Huntley (2006)
t := time (s)
n := the instantaneous number of electrons corresponding to the radius r'
Value
This function returns an object of class RLumCarlo_Model_Output
which
is a list consisting of an array with dimension length(times) x length(r) x clusters
and a numeric time vector.
Function version
0.1.0
How to cite
Friedrich, J., Kreutzer, S., 2022. run_MC_ISO_TUN(): Monte-Carlo Simulation for ISO-TL (tunnelling transitions). Function version 0.1.0. In: Friedrich, J., Kreutzer, S., Pagonis, V., Schmidt, C., 2022. RLumCarlo: Monte-Carlo Methods for Simulating Luminescence Phenomena. R package version 0.1.9. https://CRAN.R-project.org/package=RLumCarlo
Author(s)
Johannes Friedrich, University of Bayreuth (Germany), Sebastian Kreutzer, Institute of Geography, Heidelberg University (Germany)
References
Pagonis, V. and Kulp, C., 2017. Monte Carlo simulations of tunneling phenomena and nearest neighbor hopping mechanism in feldspars. Journal of Luminescence 181, 114–120. doi:10.1016/j.jlumin.2016.09.014
Further reading Aitken, M.J., 1985. Thermoluminescence dating. Academic Press.
Huntley, D.J., 2006. An explanation of the power-law decay of luminescence. Journal of Physics: Condensed Matter, 18(4), 1359.
Jain, M., Guralnik, B., Andersen, M.T., 2012. Stimulated luminescence emission from localized recombination in randomly distributed defects. Journal of Physics: Condensed Matter 24, 385402.
Pagonis, V., Friedrich, J., Discher, M., Müller-Kirschbaum, A., Schlosser, V., Kreutzer, S., Chen, R. and Schmidt, C., 2019. Excited state luminescence signals from a random distribution of defects: A new Monte Carlo simulation approach for feldspar. Journal of Luminescence 207, 266–272. doi:10.1016/j.jlumin.2018.11.024
Examples
## short example
run_MC_ISO_TUN(
E = .8,
s = 1e16,
T = 50,
rho = 1e-4,
times = 0:100,
clusters = 10,
N_e = 100,
r_c = 0.2,
delta.r = 0.5,
method = "seq") %>%
plot_RLumCarlo(legend = TRUE)
## Not run:
## long (meaningful) example
results <- run_MC_ISO_TUN(
E = .8,
s = 1e16,
T = 50,
rho = 1e-4,
times = 0:100,
clusters = 1000,
N_e = 200,
r_c = 0.1,
delta.r = 0.05,
method = "par")
plot_RLumCarlo(results, legend = TRUE)
## End(Not run)