run_MC_CW_IRSL_TUN {RLumCarlo}

Run Monte-Carlo Simulation for CW-IRSL (tunnelling transitions)

Description

Runs a Monte-Carlo (MC) simulation of continuous wave infrared stimulated luminescence (CW-IRSL) using the model for tunnelling transitions. Tunnelling refers to quantum mechanical tunnelling processes from the excited state of the trap, into a recombination centre.

Usage

run_MC_CW_IRSL_TUN(
  A,
  rho,
  times,
  clusters = 10,
  r_c = 0,
  delta.r = 0.1,
  N_e = 200,
  method = "seq",
  output = "signal",
  ...
)

Arguments

A

numeric (required): The effective optical excitation rate for the tunnelling process (s^-1).

rho

numeric (required): The density of recombination centres (defined as \rho' in Huntley 2006) (dimensionless).

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 n_filled indicate absolute numbers of a system.

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 r_c have already recombined.

delta.r

numeric (with default): Increments of the dimensionless distance parameter r'

N_e

numeric (width default): The total number of electron traps available (dimensionless). Can be a vector of length(clusters), shorter values are recycled.

method

character (with default): Sequential 'seq' or parallel 'par'processing. In the parallel mode the function tries to run the simulation on multiple CPU cores (if available) with a positive effect on the computation time.

output

character (with default): output is either the 'signal' (the default) or 'remaining_e' (the remaining charges/electrons in the trap)

...

further arguments, such as cores to control the number of used CPU cores or verbose to silence the terminal

Details

The model

I_{TUN}(r',t) = -dn/dt = A * exp(-(\rho')^{-1/3} * r')* n (r',t)

Where in the function:
A := effective optical excitation rate for the tunnelling process (s^-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' at time t

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

0.2.0

How to cite

Friedrich, J., Kreutzer, S., 2022. run_MC_CW_IRSL_TUN(): Run Monte-Carlo Simulation for CW-IRSL (tunnelling transitions). Function version 0.2.0. 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

Huntley, D.J., 2006. An explanation of the power-law decay of luminescence. Journal of Physics: Condensed Matter, 18(4), 1359.

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

Further reading

Aitken, M.J., 1985. Thermoluminescence dating. Academic Press.

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.

Chen, R., McKeever, S.W.S., 1997. Theory of Thermoluminescence and Related Phenomena. WORLD SCIENTIFIC. doi:10.1142/2781

Examples

run_MC_CW_IRSL_TUN(
 A = 0.8,
 rho = 1e-4,
 times = 0:50,
 r_c = 0.05,
 delta.r = 0.1,
 method = "seq",
 clusters = 10,
  output = "signal") %>%
 plot_RLumCarlo(norm = TRUE, legend = TRUE)