def btnGenCV(self):
self.Et_type = "CV" # For the plot simuation button to know what to plot
self.multSR = False
# Sweep parameters:
Eini = float(self.box_Eini.text())
Efin = float(self.box_Efin.text())
dE = float(self.box_dE.text())
ns = int(self.box_ns.text())
tini = 0
# Generate Et:
self.statusBar().showMessage("Generating potential waveform for CV")
# Separates list of scan rates
if self.check_Mult_sr.isChecked():
srList = self.box_Mult_sr.toPlainText()
srArray = np.asarray(srList.split("\n"))
self.sr = np.zeros(np.size(srArray))
nt = int(abs(Efin - Eini) / dE)
self.t = np.zeros([nt * ns, np.size(self.sr)])
for i in range(0, np.size(srArray)):
self.sr[i] = float(srArray[i])
t, self.E = wf.sweep(Eini, Efin, self.sr[i], dE, ns, tini)
self.t[:, i] = t
self.multSR = True
else:
self.multSR = False
sr = float(self.box_sr.text())
self.t, self.E = wf.sweep(Eini, Efin, sr, dE, ns, tini)
self.btn_plotSweep.setEnabled(True)
self.btn_plotStep.setEnabled(False)
self.btn_plotSCV.setEnabled(False)
self.btnSimulate.setEnabled(True)
self.btnPlot.setEnabled(False)
self.btnAnimate.setEnabled(False)
self.btnSimulate.setText("Simulate CV")
self.btnPlot.setText("Plot CV")
self.btnAnimate.setText("Animate CV")
self.menuSave.setTitle("Save CV")
self.statusBar().showMessage("Potential waveform for CV generated.")
Q0 = 210e-6 # C/cm2, charge density for one monolayer
D = 1e-5 # cm2/s, diffusion coefficient of R
Ageo = 1 # cm2, geometrical area
r = np.sqrt(Ageo/np.pi) # cm, radius of electrode
ks = 1e0 # cm/s, standard rate constant
alpha = 0.5 # transfer coefficient
# Potential waveform
E0 = 0 # V, standard potential
Eini = -0.5 # V, initial potential
Efin = 0.5 # V, final potential vertex
sr = 1 # V/s, scan rate
ns = 2 # number of sweeps
dE = 0.0001 # V, potential increment. This value has to be small for BI to approximate the circuit properly
t, E = wf.sweep(Eini=Eini, Efin=Efin, dE=dE, sr=sr, ns=ns) # Creates waveform
g0 = Q0/F # mol/cm2, maximum coverage for 1 monolayer
eps = n*FRT*(E-E0)
kf = ks*np.exp(alpha*eps)
kb = ks*np.exp(-(1-alpha)*eps)
# Simulation parameters
nt = np.size(t)
dt = t[1]
Th = np.ones(nt)
#%% Simulation
for j in range(1,nt):
DR = 1e-5
ks = 1e8
alpha = 0.5
DY = 1e-5
k1 = 1e-8 # less tan 1e-1 to converge
# Potential waveform
E0 = 0 # V, standard potential
Eini = -0.5 # V, initial potential
Efin = 0.5 # V, final potential vertex
sr = 1 # V/s, scan rate
ns = 2 # number of sweeps
dE = 0.005 # V, potential increment. This value has to be small for BI to approximate the circuit properly
t, E = wf.sweep(Eini=Eini, Efin=Efin, dE=dE, sr=sr, ns=ns)
DOR = DO / DR
DYR = DY / DR
#%% Simulation parameters
nT = np.size(t) # number of time elements
dT = 1 / nT # adimensional step time
lamb = 0.45 # For the algorithm to be stable, lamb = dT/dX^2 < 0.5
Xmax = 6 * np.sqrt(nT * lamb) # Infinite distance
dX = np.sqrt(dT / lamb) # distance increment
nX = int(Xmax / dX) # number of distance elements
## Discretisation of variables and initialisation
if cRb == 0: # In case only O present in solution
CR = np.zeros([nT, nX])
