def main():
from materials import CZTS, Cu2S_low, ZnS_zincblende, SnS, S2
import numpy as np
# Surface model from ab initio calcs and NIST data
T = np.linspace(573.15, 873.15, 100) # K
P = np.array(np.logspace(-8, 2, 100), ndmin=2).transpose() # Pa
D_mu = CZTS.mu_kJ(T, P) - (Cu2S_low.mu_kJ(T, P) + ZnS_zincblende.mu_kJ(
T, P) + SnS.mu_kJ(T, P) + 0.5 * S2.mu_kJ(T, P))
D_mu_label = '$\Delta G_f$ / kJ mol$^{-1}$'
scale_range = [-50, 70]
# Stability lines from figure 5, J. J. Scragg et al., Chem. Mater. (2011) 23 4625-4633
kinetic_data = np.genfromtxt('jscragg_2011.csv',
delimiter=',',
skip_header=1)
# Convert log pressure to absolute pressure in mbar
kinetic_data[:, 1:] = np.power(10, kinetic_data[:, 1:])
# Columnwise conversion to SI units from deg C and mbar
kinetic_data_si = (kinetic_data + [273.15, 0., 0.]) * [1., 100., 100.]
plot_potential(T,
P,
D_mu,
D_mu_label,
scale_range,
filename='plots/DG_CZTS_SnS_Scragg.png',
T_units='K',
P_units='Pa',
overlay=kinetic_data_si)
def main():
from materials import CZTS, Cu2S_low, ZnS_zincblende, SnS, S2
import numpy as np
# Surface model from ab initio calcs and NIST data
T = np.linspace(573.15,873.15,100) # K
P = np.array( np.logspace(-8,2,100),ndmin=2).transpose() # Pa
D_mu = CZTS.mu_kJ(T,P) - (Cu2S_low.mu_kJ(T,P) +
ZnS_zincblende.mu_kJ(T,P) +
SnS.mu_kJ(T,P) +
0.5*S2.mu_kJ(T,P) )
D_mu_label = '$\Delta G_f$ / kJ mol$^{-1}$'
scale_range = [-50,70]
# Stability lines from figure 5, J. J. Scragg et al., Chem. Mater. (2011) 23 4625-4633
kinetic_data = np.genfromtxt('jscragg_2011.csv',delimiter=',',skip_header=1)
# Convert log pressure to absolute pressure in mbar
kinetic_data[:,1:] = np.power(10,kinetic_data[:,1:])
# Columnwise conversion to SI units from deg C and mbar
kinetic_data_si = (kinetic_data + [273.15, 0., 0.]) * [1., 100., 100.]
plot_potential(T,P,D_mu,D_mu_label,scale_range, filename='plots/DG_CZTS_SnS_Scragg.png',
T_units='K', P_units='Pa', overlay=kinetic_data_si)
def main():
from materials import CZTS, Cu, Zn, Sn, S8
import numpy as np
T = np.linspace(100,1500,100) # K
P = np.array( np.logspace(1,7,100),ndmin=2).transpose() # Pa
D_mu = CZTS.mu_kJ(T,P) - (2*Cu.mu_kJ(T,P) +
Zn.mu_kJ(T,P) +
Sn.mu_kJ(T,P) +
0.5*S8.mu_kJ(T,P)
)
D_mu_label = '$\Delta G_f$ / kJ mol$^{-1}$'
scale_range = [-380,-240]
plot_potential(T,P,D_mu,D_mu_label,scale_range, filename='plots/DG_CZTS_S8.png')
def main():
from materials import CZTS, Cu2S_low, ZnS_zincblende, SnS2
import numpy as np
from DG_CZTS_S8 import plot_potential
T = np.linspace(100,1500,100) # K
P = np.array( np.logspace(1,7,100),ndmin=2).transpose() # Pa
D_mu = CZTS.mu_kJ(T,P) - (Cu2S_low.mu_kJ(T,P) +
ZnS_zincblende.mu_kJ(T,P) +
SnS2.mu_kJ(T,P)
)
D_mu_label = '$\Delta G_f$ / kJ mol$^{-1}$'
scale_range = [-50,-40]
plot_potential(T,P,D_mu,D_mu_label,scale_range, filename='plots/DG_CZTS_binaries.png', precision="%.1f")
# #
# This program is distributed in the hope that it will be useful, #
# but WITHOUT ANY WARRANTY; without even the implied warranty of #
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the #
# GNU General Public License for more details. #
# #
# You should have received a copy of the GNU General Public License #
# along with this program. If not, see <http://www.gnu.org/licenses/>. #
################################################################################
from materials import CZTS, Cu, Zn, Sn, alpha_S, Cu2S_low as Cu2S, SnS2, ZnS_zincblende as ZnS, SnS_pcma as SnS
T = 298.15 # K
P = 1E5 # Pa
DH_f_CZTS_eV = CZTS.H_eV(T, P) - (2. * Cu.H_eV(T, P) + Zn.H_eV(T, P) +
Sn.H_eV(T, P) + 4. * alpha_S.H_eV(T, P))
DH_f_CZTS_kJ = CZTS.H_kJ(T, P) - (2. * Cu.H_kJ(T, P) + Zn.H_kJ(T, P) +
Sn.H_kJ(T, P) + 4. * alpha_S.H_kJ(T, P))
DE_f_CZTS_eV = CZTS.pbesol_energy_eV / CZTS.fu_cell - (
2. * Cu.pbesol_energy_eV / Cu.fu_cell + Zn.pbesol_energy_eV / Zn.fu_cell +
Sn.pbesol_energy_eV / Sn.fu_cell +
4. * alpha_S.pbesol_energy_eV / alpha_S.fu_cell)
print('Formation enthalpy of kesterite CZTS: ' +
'{0:3.2f} eV / formula unit'.format(DH_f_CZTS_eV))
print(' ' +
'{0:3.2f} kJ / mol'.format(DH_f_CZTS_kJ))
