GFORTRAN_1.4 not found

[stehvieh]

Hi!
I cannot run properly the example notebook AuAg_dimer, which gives me the following error:

Durign a first try, I got the error "ImportError: No module named 'py_gmm.gmm_py'", which I could fix by installing the openblas library. But I also find the following error while executing f2py.sh:

Do you have any suggesting how I can fix these problems?

Best,
Steffi

[gevero]

Hi Steffi

I am not 100% sure what is going on here, but I'll try a first guess. In the meantime it would help a lot if you could tell me which kind of platform you are using (i guess linux, but which version: try uname -r), and also which compiler (gfortran --version). Furthermore, the full output of the f2py.sh script would help a lot. You can link a gist to this issue if you want.

It looks like, from the error you reported when you are running f2py.sh, that your compiler is operating in fixed form, i.e. with files that must have fixed row length, etc..., while it should work in free form (no restrictions) since the files have a .f90 extension. In any case, if not precisely this, it really looks like that your compiler cannot parse the source code, which should be pretty standard anyway. As soon you provide me with more information, i hope we shall be able to work out a solution.

best

Giovanni

[stehvieh]

Hi Giovanni

Thanks for your quick respons! Yes, I am using linux, verion 3.13.0-24-generic. My compiler is GNU Fortran 4.8.4, I think this is the latest version being instlled via apt-get install gfortran.

Here is the complete output of the script: https://gist.github.com/stehvieh/88d0878bf564ec23cbe065d3a8d423be (i hope that works)

I hope maybe you now have an idea what I could change to get the code working.

best,
Steffi

[gevero]

Hi Steffi

So, in the end it looks like it is entirely an Anaconda problem, as listed here. It shows up on some systems, but for instance not on mine. So, resuming:

• The strange f2py messages like get_parameters: got "unexpected EOF while parsing (<string>, line 1)" on '0.0d0' are of no importance. I rebuilt py_gmm on my system, with the new conda, and I see them too, nevertheless the library builds successfully (gmm_py.cpython-35m-x86_64-linux-gnu.so) and I can run the examples.
• Also in your case the library seems to build succesfully, nevertheless when you try to load it, you cannot find the right symbol GFORTRAN_1.4 inside libgfortran.so.3, which is provided by Anaconda as you can see from the file path

At this point, we are left with a number of options, as listed in the issue tread that i linked above. In my opinion you could proceed as follows:

• Updating conda and then all the rest via: conda update conda followed by conda update --all. Then rebuild the library, kill all the jupyter notebooks and reload the example and see if it works. Since the library on my system works, and since it is provided by anaconda, It should be ok.
• If it does not work you could try this workaroud, also suggested in the linked issue: conda remove libgfortran followed by conda install libgcc --force
• If this still does not work you can bypass the conda libgfortran.so.3 and go straight to the system one, by doing export LD_PRELOAD=/path/to/libgfortran.so.3, and the system libgfortran.so.3 should be found under something like /usr/lib or /usr/lib64, depending on your distro. If it is ubuntu it should be something: /usr/lib/x86_64-linux-gnu/libgfortran.so.3

Hope this helps

Giovanni

[stehvieh]

Hi,
your first suggestion did not work; although there were some updates, the error was not fixed. I then removed the ligfortran and installed libgcc; but now the module 'joblib' is missing in the notebook:

How can I install that?

best,
Steffi

[gevero]

conda install joblib

[stehvieh]

Great, it's working now. Thanks so much for your support!

best,
Steffi

[gevero]

Happy everything worked out. Let me know if you need any assistance to setup a new calculation.

Best

Giovanni

[stehvieh]

Hi!
After setting up some calculations, I now tried to reproduce the spectra
I found in the publications you put on the github page. I guess I have
some trouble with choosing the correct dielectric function. Am I right
that the silver dielectric function you mention in the paper is not
given in py_gmm yet? I tried to reproduce the data by taking the data
from Johnson and Christy and substract the term with the mean free path
of the electrons in silver, but I was not successfull. Could you maybe
send me this data you used or tell me how exactly you calculated the
dielectric function?

best
Steffi

Am 05.04.2016 um 14:49 schrieb Giovanni Pellegrini:

Happy everything worked out. Let me know if you need any assistance to
setup a new calculation.

Best

Giovanni

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#4 (comment)

Stefanie Roese

TU Dortmund
Experimentelle Physik I
Raum: CP-E1-140

Otto-Hahn-Strasse 4
D-44227 Dortmund (Germany)

Tel: +49-(0)231-755-8714
Fax: +49-(0)231-755-3657

stefanie.roese@tu-dortmund.de

[gevero]

Hi. You want the AG Palik optical constant. It is included in the repo. Size correction must be applied. You should be able to find all the details in my PhD thesis: "modeling the optical properties of nanocluster based functional plasmonic materials". Just Google it.

Let my know if everything is OK.

Best

[stehvieh]

Hi,
I already tried this optical constant; in principle the spectrum I
calculate is correct, but the ratio of the three peaks (2 Ag
nanoparticles with 10 nm in diameter and a distance of 0.2 nm) is
different than in the paper and the peak position differs; my peaks are
shifted to higher wavelengths (ca. 10 nm). So I thought it maybe would
be a problem with the optical constant.

Best,
Steffi

Am 16.04.2016 um 15:48 schrieb Giovanni Pellegrini:

Hi. You want the AG Palik optical constant. It is included in the repo.

Let my know if everything is OK.

Best

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#4 (comment)

[gevero]

The discrepancies could originate from a couple of factor, and namely one could be the refractive index of the matrix (I used n=1.45 if I remember correctly) and the exact formula and parameters for the optical constant size correction. Which parameters and formula did you use?

[stehvieh]

I have found my mistake, thanks :) now everything is working properly.
While running larger calculations (e.g. a chain of nanoparticles with 5
particles), the jupyter notebook tells my "The kernel appears to have
died. it will restart automatically", but it doesn't. Do you know any
solution for this? Might it be a problem of the jupyter notebook? So
maybe it could set up the calculation without using it.

best
Steffi

Am 16.04.2016 um 19:52 schrieb Giovanni Pellegrini:

The discrepancies could originate from a couple of factor, and namely
one could be the refractive index of the matrix (I used n=1.45 if I
remember correctly) and the exact formula and parameters for the
optical constant size correction. Which parameters and formula did you
use?

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#4 (comment)

[gevero]

Hi

It should not be a jupyter problem. Usually, this The kernel appears to have died. it will restart automatically error happens when something has gone wrong with the fortran code. It could be many things: for instance it could be that you have intersecting spheres, or that the linear solver is not converging to a solution, you are going out of memory, etc...

In an ideal situation the fortran code should "talk" to the python wrapper and tell it what's wrong, but in reality the fortran code is still pretty rough around the edges, so when it fails, it fails on its own and all you see is the The kernel appears to have died. it will restart automatically error. Nevertheless, if you look at the jupyter notebook output at the shell, you should be able to some some output from the fortran code, to get an idea of what is wrong. 5 spheres should be handled pretty easily, even with a relatively large number of multipoles in the calculation (~20-25). If you cannot spot the problem feel free to send me your notebook and I will run it on my workstation to see what's wrong.

Best

[stehvieh]

Hi
I don't get any output at the shell, I attached the output of the shell
to this mail. I also send you the file I use; sometimes it works and
sometimes it doesn't. Maybe you have a solution?! Thanks for your great
support!
best
Steffi

Am 19.04.2016 um 10:58 schrieb Giovanni Pellegrini:

Hi

It should not be a jupyter problem. Usually, this |The kernel appears
to have died. it will restart automatically| error happens when
something has gone wrong with the fortran code. It could be many
things: for instance it could be that you have intersecting spheres,
or that the linear solver is not converging to a solution, you are
going out of memory, etc...

In an ideal situation the fortran code should "talk" to the python
wrapper and tell it what's wrong, but in reality the fortran code is
still pretty rough around the edges, so when it fails, it fails on its
own and all you see is the |The kernel appears to have died. it will
restart automatically| error. Nevertheless, if you look at the jupyter
notebook output at the shell, you should be able to some some output
from the fortran code, to get an idea of what is wrong. 5 spheres
should be handled pretty easily, even with a relatively large number
of multipoles in the calculation (~20-25). If you cannot spot the
problem feel free to send me your notebook and I will run it on my
workstation to see what's wrong.

Best

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#4 (comment)

{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
" # Stockman Silver Nanolens\n",
" In this notebook we calculate the the optical spectra and local-field enhancement of Silver Nanolenses as proposed in the following publication:\n",
" \n",
" Li K, Stockman MI, Bergman DJ (2003) Self-Similar Chain of Metal Nanospheres as an Efficient Nanolens. Phys Rev Lett 91:227402. doi:10.1103/PhysRevLett.91.227402\n",
"\n",
"The notebook is structured as follows:\n",
" * Importing the libraries: setup of useful settings and import of necessary libraries and databases.\n",
" * Setting up the inputs: definition of the inputs of the simulation.\n",
" * Performing the calculations: call to py_gmm to compute all the relevant far-field and local field quantities.\n",
" * Plotting the results: plot of the integral and differential cross-section for the far-field, and the field enhancement for the local-field."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Importing the libraries"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"code_folding": [
0
],
"collapsed": false
},
"outputs": [],
"source": [
"#------Library loading------\n",
"\n",
"# numpy for matrix computations\n",
"import numpy as np; import numpy.ma as ma\n",
"\n",
"# system libraries\n",
"import sys\n",
"\n",
"# plotting libraries\n",
"%matplotlib inline\n",
"import matplotlib.pylab as plt\n",
"from matplotlib.patches import Circle, Ellipse\n",
"\n",
"# Generalized Multiparticle Mie import\n",
"sys.path.append('../../')\n",
"import py_gmm\n",
"\n",
"# parallel computation\n",
"from joblib import Parallel, delayed\n",
"import multiprocessing"
]
},
{
"cell_type": "markdown",
"metadata": {
"code_folding": []
},
"source": [
"# Setting up the inputs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Target inputs (optical constant database, sphere coordinates, composition and size)"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"collapsed": false
},
"outputs": [],
"source": [
"# building the optical constant database\n",
"eps_db_out=py_gmm.mat.generate_eps_db('../../epsilon/',ext='.edb')\n",
"eps_files,eps_names,eps_db=eps_db_out['eps_files'],eps_db_out['eps_names'],eps_db_out['eps_db']\n",
"\n",
"# sphere position (in nm)\n",
"m_xyz = np.array([[ 0. , 0. , 0. ],\n",
" [2.05 , 0. , 0. ],\n",
" [4.1 , 0. , 0. ],\n",
" [6.15 , 0. , 0. ],\n",
" [8.2,0,0]])\n",
" \n",
"\n",
"# sphere radius (in nm)\n",
"v_r = np.array([ 1., 1., 1,1,1])\n",
"\n",
"# how many spheres in the target? We guess it from the length of the radius vector\n",
"ns = len(v_r)\n",
"\n",
"# sphere composition, calling the names contained in "eps_names", just populated above\n",
"target_comp= np.array(ns ['eps_corr']) # vector containing the optical constants names\n",
"\n",
"# refractive index of the environment\n",
"n_matrix = 1.45"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Plane wave incident field"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"# Euler angles: (alpha,beta,gamma)=(0,0,0) means a z-directed, x-polarized plane wave\n",
"alpha = 0.0 # azimuth\n",
"beta = 0.0 # polar\n",
"gamma = 0.0 # polarization\n",
"\n",
"# Wavelengths for the specrum computation\n",
"wl_min = 300\n",
"wl_max = 700\n",
"n_wl = 250\n",
"v_wl = np.linspace(wl_min,wl_max,n_wl)\n",
"\n",
"# Wavelength for the local field computation\n",
"wl_lf = 1240/3.25 # conversion from 3.37 eV"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Additional inputs for the simulation"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {
"code_folding": [
0
],
"collapsed": true
},
"outputs": [],
"source": [
"n_stop=20 # maximum multipolar expansion order\n",
"f_int=0.0; # interaction cutoff (normally set to zero to have full interactions)\n",
"lf_ratio=300; # plot sphere local field contribution up to a distance equal to d=lf_ratio_r_sphere\n",
"qs_flag='yes' # use the quasi-static approximation for the current simulation\n",
"n_E = 400 # local field plotting grid resolution"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Target plot"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"code_folding": [
0
],
"collapsed": false
},
"outputs": [
{
"data": {
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L67D0hx56CO12O/O8zR6KouD69es77GX69Ok0ao1CyLKMBQUFHfYyduxY5DiO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\nIAjC9FDBQxAEQRCE6aGChyAIgiAI0/P/hkKOVJthrpQAAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fc1ad8c6ba8>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# target plot\n",
"fig = plt.figure(num=1,figsize=(10,10)) # setting the figure size\n",
"ax = fig.add_subplot(1, 1, 1, aspect='equal') # creating the plotting axis\n",
"\n",
"# plot bounds and eliminating x and y ticks\n",
"plt.xlim(-1.1_(v_r[0]),1.1_(m_xyz[ns-1,0]+v_r[ns-1]))\n",
"plt.ylim(-1.1_(v_r[0]),1.1*(v_r[0]))\n",
"plt.xticks([])\n",
"plt.yticks([])\n",
"\n",
"# plotting the target\n",
"for c,r in zip(m_xyz,v_r):\n",
" c0=c[0];c1=c[1];c2=c[2]\n",
" ax.add_patch(Circle((c0,c1,c2),r,color='k'))\n",
" ax.eventson"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Performing the calculations"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Scattering cross sections and field expansion coefficients for the spectral computation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [
0
],
"collapsed": false,
"scrolled": true
},
"outputs": [],
"source": [
"# computing the expansion coefficients and cross sections with a loop\n",
"m_abcd_ext_sca_abs = [] # list to be filled with the output\n",
"for wl in v_wl:\n",
" \n",
" # retrieving optical constants at wl from the database\n",
" e_list=py_gmm.mat.db_to_eps(wl,eps_db,target_comp);\n",
" m_eps=np.column_stack((np.real(e_list),np.imag(e_list)));\n",
" \n",
" # solving the gmm problem (calculating the cross sections and the expansion coefficients)\n",
" out=py_gmm.gmm_py.gmm_f2py_module.expansion_coefficients(m_xyz, # target sphere position in nm\n",
" v_r, # target sphere radii in nm\n",
" m_eps, # e1 and e2 for each sphere\n",
" f_int, # interaction coefficient\n",
" n_matrix, # environment refractive index\n",
" wl, # computation wavelength\n",
" alpha,beta,gamma, # euler angles for the incident pw\n",
" n_stop, # maximum number for expansion coefficients\n",
" qs_flag) # quasi static approximation\n",
" m_abcd_ext_sca_abs.append(out)\n",
" \n",
"# extracting coefficients and cross section\n",
"v_coeff=[];v_cext=[];v_csca=[];v_cabs=[];\n",
"for out in m_abcd_ext_sca_abs:\n",
" v_coeff.append(out[0]);\n",
" v_cext.append(out[1]);\n",
" v_csca.append(out[2]);\n",
" v_cabs.append(out[3]);\n",
"\n",
"# converting the lists to numpy arrays\n",
"v_cext=np.array(v_cext)\n",
"v_csca=np.array(v_csca)\n",
"v_cabs=np.array(v_cabs)"
]
},
{
"cell_type": "markdown",
"metadata": {
"code_folding": [
3
]
},
"source": [
"### Local Field"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"collapsed": false
},
"outputs": [],
"source": []
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Plotting the results"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Extinction cross section"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [],
"collapsed": false
},
"outputs": [],
"source": [
"# cross section plot\n",
"f_size=25;\n",
"f_size_ticks=20;\n",
"plt.figure(1,figsize=(15,10));\n",
"#plt.plot(v_wl,np.sum(v_cext,axis=1),'k',linewidth=3.0);\n",
"plt.plot(v_wl,v_cext[:,0],'k')\n",
" # v_wl,v_cext[:,1],'k--');\n",
"\n",
"# plt title\n",
"plt.title('Full nanolens',fontsize=f_size)\n",
"\n",
"# axes labels\n",
"plt.xlabel(r'wavelength (nm)', fontsize=f_size)\n",
"plt.ylabel(r'Cext', fontsize=f_size)\n",
"\n",
"# ticks\n",
"#plt.xlim(2.8,4.0)\n",
"plt.xticks(fontsize=f_size_ticks)\n",
"plt.yticks(fontsize=f_size_ticks)\n",
"\n",
"# legend\n",
"plt.legend(r'Sphere 2 C$_{ext}$',frameon=False,fontsize=f_size-5)\n",
"\n",
"# layout\n",
"plt.tight_layout()\n",
"\n",
"#speichern?\n",
"#f = open("/media/sf_vbox_share/2nm Var Abstand/lambda005nm","w")\n",
"#f.write("\n".join(str(x) for x in v_wl))\n",
"#f.close()\n",
"\n",
"g = open("/media/sf_vbox_share/Kette 0,05 nm/5 Elemente","w")\n",
"g.write("\r\n".join(str(x) for x in v_cext[:,0]))\n",
"g.close()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Local field enhancement"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"code_folding": [
0
],
"collapsed": false
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.1"
}
},
"nbformat": 4,
"nbformat_minor": 0
}

[gevero]

Hi Steffi

I took a look at the notebook you sent me. Basically the linear solver is not converging for two reasons:

• The spheres are extremely small
• the gap is even smaller (0.1nm)

Please keep in mind that with Mie theory won't give meaningful results for spheres and gap this small.

Best

[stehvieh]

Sorry, I forgot to attach it. In eps_corr I took the data from Johnson
and Christy and corrected them optically (2 nm Ag particles). Thanks for
your advise to save the data; I did not use python before, so I will use
this in the future.

best
Steffi

Am 24.04.2016 um 17:58 schrieb Giovanni Pellegrini:

Hi Steffie

I took a look at the notebook you sent me. I miss a key element in
order to run your calculation, i.e. the optical constant |eps_corr|,
which I guess you added to the folder as a file |eps_corr.edb|. In the
meantime I strongly advise you to use np.loadtxt and np.savetxt to
read from and write to text file, especially if10547627 they are
multi-column ascii files: it is so much simpler.
Best

—
You are receiving this because you authored the thread.
Reply to this email directly or view it on GitHub
#4 (comment)

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[gevero]

Hi @stehvieh

As you can see I updated my previous answer. I ran your example with a standard optical constant. I could run it with your optical constant, but the conclusion would not change. My conclusions are as follows

• The py_gmm linear solver is not converging for your input as shown by the string Problem in bicgstab 1 at the jupyter shell. The technical reason is that for extremely small spacings and small sphere size the linear system matrix is extremely ill conditioned (roughly meaning that the matrix has coefficients that are very different in size), and therefore, in spite of jacobi preconditioning the solver has trouble converging. I will look deeper into it, but you can verify that for larger spacings (0.5nm for instance) the solution will converge.
• It is my opinion that a Generalized Mie approach is not of much use for the sphere size (2nm) and sphere spacing (0.1nm) you are using. The optical constant of such a small sphere (a few hundreds atoms at most) cannot be reliably modeled just by correcting the bulk one, and you would need an ab initio calculation. Furthermore, for such a small spacing, you would have to take into account quantum effects such as electron spillout, etc... Are you really interested in such particles sizes and spacing, or where you just trying out the code? If the latter is your answer, i suggest you to move towards larger sphere sizes (tens of nanometers) and more realistic spacings (everything above 1 or 2 nm should be fine)

Best

Giovanni

[stehvieh]

Hi!

Thanks for your answer. In fact, I need to investigate 2nm spheres since
this is the particle size my cluster source is produing. I have measured
time dependent extinction spectra and wanted to investigate how the
aggregation process works for my sample system. So I would like to
perform calculations for different setups of spheres to find out how
they influence the shape of the spectra. I assumed almost touching
spheres, this is why I chose 0.1 nm as a distance but I will try 0.5 nm
as well.

From the literature I found that basically the generalized mie theory
could be applied in all cases where the distance among spherical
particles is less than five times the mean diameter.

So I will try the 0.5 nm distance and see if that works for me since the
spectra I obtained for chains of 3 and 4 particles were quite promising.

best

Steffi

Am 25.04.2016 um 12:23 schrieb Giovanni Pellegrini:

Hi @stehvieh https://github.com/stehvieh

As you can see I updated my previous answer. I ran your example with a
standard optical constant. I could run it with your optical constant,
but the conclusion would not change. My conclusions are as follows

• The py_gmm linear solver is not converging for your input as shown
  by the string |Problem in bicgstab 1| at the jupyter shell. The
  technical reason is that for extremely small spacings and small
  sphere size the linear system matrix is extremely ill conditioned
  (roughly meaning that the matrix has coefficients that are very
  different in size), and therefore, in spite of jacobi
  preconditioning
  https://en.wikipedia.org/wiki/Preconditioner#Jacobi_.28or_diagonal.29_preconditioner
  the solver has trouble converging. I will look deeper into it, but
  you can verify that for larger spacings (0.5nm for instance) the
  solution will converge.
• It is my opinion that a Generalized Mie approach is not of much
  use for the sphere size (2nm) and sphere spacing (0.1nm) you are
  using. The optical constant of such a small sphere (a few hundreds
  atoms at most) cannot be reliably modeled just by correcting the
  bulk one, and you would need an ab initio calculation.
  Furthermore, for such a small spacing, you would have to take into
  account quantum effects such as electron spillout, etc... Are you
  really interested in such particles sizes and spacing, or where
  you just trying out the code? If the latter is your answer, i
  suggest you to move towards larger sphere sizes (tens of
  nanometers) and more realistic spacings (everything above 1 or 2
  nm should be fine)

Best

Giovanni

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#4 (comment)

Stefanie Roese

TU Dortmund
Experimentelle Physik I
Raum: CP-E1-140

Otto-Hahn-Strasse 4
D-44227 Dortmund (Germany)

Tel: +49-(0)231-755-8714
Fax: +49-(0)231-755-3657

stefanie.roese@tu-dortmund.de