Single integrating sphere measurements
Scott Prahl
Dec 2021
[1]:
import numpy as np
import matplotlib.pyplot as plt
import iadpython.sphere
%config InlineBackend.figure_format='retina'
Substitution vs Replacement Measurements
Integrating spheres are most easily used to make relative (rather than absolute) measurements. The total reflected light from a sample is compared to a standard or to the light hitting the sphere wall directly.
This section will calculate the measured relative value for a particular experiment.
Definitions
If we look at a cross-section of an integrating sphere used in to measure transmission (left) and reflection (right) then we can see the diameters of the sphere and each of the ports.
The natural thing would be to use subscripts and define the relative area of the sample as \(a_\mathrm{sample}\). Relative area parameters are
Other relative areas are the area of the detector \(a_\mathrm{detector}\), the area of the entrance port \(a_\mathrm{entrance}\), and the area of the sphere wall \(a_\mathrm{wall}\)
The total sphere surface includes the sphere has wall and ports (sample, detector, and entrance). Thus
or in terms of dimensionless relative areas
One thing to note is that for the sphere on the left (the transmission experiment) the entrance port does not exist and therefore has zero area. The fraction of the sphere walls that is covered by ports is greater in the experiment on the left than the one on the right. Thus the relative wall area \(a_\mathrm{wall}\) for the two experiments will be different.
Simple sphere model with sample and detector ports
The reflectance of each the ports is not usually 0%. Of course, the entrance port is remains empty and still has no reflectance. However, there is nearly always a sample and a detector. If we include these when calculating the total light then we get slightly different equations.
We start with a power \(P_0\) entering the sphere.
The value of r₀ varies with the experiment. If the light is collimated, it might hit the sphere wall or a sample or it might pass through the a sample and emerge diffuse. It is also possible that the light enters the sphere fully diffuse. Some possible cases are
r₀ |
Description |
---|---|
1 |
fully diffuse light enters the sphere |
r\(_w\) |
light hits the walls of the sphere first |
UR1 |
collimated light hits sample and is diffusely reflected into sphere |
UT1 |
collimated light hits sample and is transmitted diffusely into sphere |
URU |
diffuse light hits sample and is diffusely reflected into sphere |
UTU |
diffuse light hits sample and is transmitted diffusely into sphere |
This light \(r_0 P_0\) is assumed to be completely diffuse and reach all ports and walls in the sphere. The light reflected for the second bounce will be
The second bounce is also totally diffuse and the third bounce will be
Adding everything together, we get the total power on the walls
And so the sphere multiplier \(M\) above is modified to become
This means that the sample reflectance has a non-linear relationship to the reflectance. This gets worse when more internal bounces take place (i.e., as the port sizes get smaller for a constant wall reflectivity or as the reflectivity increases and the port sizes remain the same.) These effects are shown in the two graphs below.
Effect of sample reflectance on relative radiance in sphere with 98% reflectivity
If the radiance on the walls of the sphere is linearly related to the reflectance (or transmittance) of a sample then we can just determine the sample reflectance with a few measurements \(M\)
However, as we can see below, as the sample size (i.e., port) gets larger and larger, this simple relationship breaks down.
[2]:
r_sample = np.linspace(0.0,1,50)
s = iadpython.sphere.Sphere(25.4*8, 25.4, r_wall=0.98)
plt.figure(figsize=(8,6))
s.d_sample = 10
M = s.multiplier(UR1=r_sample, URU=r_sample)
plt.plot(r_sample, M/np.max(M), color='black')
plt.text(0.5,0.60,'%gmm'%(s.d_sample), color='black')
s.d_sample=25
M = s.multiplier(UR1=r_sample, URU=r_sample)
plt.plot(r_sample, M/np.max(M), color='red')
plt.text(0.55,0.5,'%gmm'%(s.d_sample), color='red')
s.d_sample=50
M = s.multiplier(UR1=r_sample, URU=r_sample)
label='$d_{sample}$=%.2f, %.0fmm sphere'%(s.a_wall,s.d_sphere)
plt.plot(r_sample, M/np.max(M), color='blue')
plt.text(0.6,0.43,'%gmm sample port diameter'%(s.d_sample), color='blue')
plt.xlabel('Sample Reflectance')
plt.ylabel('Relative radiance (to 100% sample reflection)')
plt.title('Sample port size influence on relative radiance')
plt.text(0.05,0.95,"Sphere Diameter = %g mm"%s.d_sphere)
plt.text(0.05,0.90,"Wall Reflectance = %g%%"%(100*s.r_wall))
plt.show()
Effect of wall reflectivity on relative radiance with 25mm sample port
Here we see the slightly perplexing result that higher wall reflectivities increase non-linear response. This is follows from the fact that in whiter spheres, the light bounces around more times. The light has more chances to interact with the sample and therefore the sample ends up having a larger non-linear effect.
[36]:
r_sample = np.linspace(0.0,1,50)
s = iadpython.sphere.Sphere(250, 25, d_entrance=25)
plt.figure(figsize=(8,6))
r_wall=0.99
M = s.multiplier(UR1=r_sample, URU=r_sample, r_wall=r_wall)
M100 = M[-1]
plt.plot(r_sample, M/M100, color='black')
plt.text(0.68,0.60,'%g%% wall reflectance'%(100*r_wall), color='black', ha='left')
r_wall=1.00
M = s.multiplier(UR1=r_sample, URU=r_sample, r_wall=r_wall)
plt.plot(r_sample, M/np.max(M), color='blue')
plt.text(0.65,0.45,'%g%% wall reflectance'%(100*r_wall), color='blue', ha='left')
plt.plot([0,1],[0,1],':k')
plt.xlabel('Sample Reflectance')
plt.ylabel('Relative radiance (to 100% sample reflection)')
plt.title('Wall reflectivity influence on linearity')
plt.text(0.05,0.95,"Sphere Diameter = %g mm"%s.d_sphere)
plt.text(0.05,0.90,"Sample Diameter = %g mm"%(s.d_sample))
plt.text(0.05,0.85,"Entrance Diameter = %g mm"%(s.d_entrance))
plt.text(0.05,0.80,"Detector Diameter = %g mm"%(s.d_detector))
plt.text(0.05,0.70,"Port fraction = %.2g%%"%(100-100*s.a_wall))
plt.show()
[ 0. 1.36863847 2.74669864 4.13427815 5.53147596 6.93839244
8.35512934 9.78178985 11.21847858 12.66530166 14.12236669 15.58978281
17.06766072 18.5561127 20.05525263 21.56519606 23.08606018 24.61796391
26.16102787 27.71537449 29.28112794 30.85841427 32.44736137 34.04809902
35.66075896 37.28547486 38.92238244 40.57161944 42.23332569 43.90764312
45.59471587 47.29469024 49.00771479 50.73394038 52.47352019 54.2266098
55.99336719 57.77395282 59.56852968 61.37726334 63.20032197 65.03787642
66.89010028 68.75716992 70.63926454 72.53656623 74.44926005 76.37753408
78.32157945 80.28159045]
what is gain
what is f
multiport analysis for UR1
multiport analysis for UT1
calculation of b1 and b2
calculation of sphere wall reflectivity
How much of an effect do these equations have??
Sphere model that tracks reflected sepa
Assume that a sphere is illuminated with diffuse light having a power P. Typically, the source of this diffuse light is light reflected by (or transmitted through) the sample.
We assume that this diffuse light can reach all parts of sphere — specifically, that light from this source is not blocked by a baffle. Multiple reflections within the sphere will increase the power falling on non-white areas in the sphere (e.g., the sample, detector, and entrance).
The total power entering the sphere is divided between the fractions hitting the wall, the sample port, the detector port, and the entrance port
First incidence in reflection experiment
Consider a reflection experiment. In this case the light reflected by the sample P will only fall on the walls and the entrance port. It cannot hit the detector (because of a baffle) and it cannot hit the sample (because that is where it is leaving).
or (after dividing numerator and denominators in the fractions by \(A\)
We see that \(P=P_\mathrm{wall}^{(1)}+P_\mathrm{entrance}^{(1)}\). The light reflected
First incidence in transmission experiment
Consider a transmission experiment. In this case the power transmitted by the sample P will only fall on the walls. It cannot hit the detector (because of a baffle), it cannot hit the entrance port (because there is none), and it cannot hit the sample (because that is where it is leaving). Thus all the light hits the walls
The light that bounces off the sphere parts is then
Second incidence in reflection experiment
The distribution of power for the second incidence for each part can only arise from the first incidence on the walls
The second bounce from each part is then
Third incidence in reflection experiment
The distribution of power for the third incidence for each part now arises from the first incidence on the walls
The second bounce from each part is then
The light that hits the wall after \(k\) bounces has the same form as above
Since the light falling on the sample and detector must come from the wall
Therefore,
The total power falling on the wall is just
The total power falling the detector is
The gain \(G(r_\mathrm{diffuse})\) on the irradiance on the detector (relative to a black sphere),
in terms of the sphere parameters
The gain for a detector in a transmission sphere is similar, but with primed parameters to designate a second potential sphere that is used. For a black sphere the gain \(G(0) = 1\), which is easily verified by setting \(r_\mathrm{wall}=0\), \(r_\mathrm{diffuse}=0\), and \(r_\mathrm{detector}=0\). Conversely, when the sphere wall and sample are perfectly white, the irradiance at the entrance port, the sample port, and the detector port must increase so that the total power leaving via these ports is equal to the incident diffuse power \(P\).
Thus the gain should be the ratio of the sphere wall area over the area of the ports through which light leaves or \(G(1)=A/(A_\mathrm{entrance}+A_\mathrm{detector})\) which follows immediately from the gain formula with \(r_\mathrm{wall}=1\), \(r_\mathrm{diffuse}=1\), and \(r_\mathrm{detector}=0\).
The gain \(G(r_\mathrm{sample})\) on the irradiance on the detector (relative to a black sphere),
in terms of the sphere parameters
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