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Diffusion battery particle sizing system based on the MSA data conversion algorithm: Experimental examination
J. Aerosol $ci., Vol. 26. Suppl 1, pp. S747--$748, 1995 Elsevier Science Ltd Printed in Gre~t Britain 0021-,8502/95 $9.50 + 0.00
Pergamon
Diffusion Battery Particle Sizing System Based on the MSA Data Conversion Algorithm: Experimental Examination S.I.Eremenko t, R.Caldow $, A.M.Baklanov t, M.Havlicek $ and G.Sem $ t - - Institute of Chemical Kinetics and Combustion, 630090, Novosibirsk, Russia :~ - - TSI Incorporated, P.O. Box 64394,St. Paul, MN 55164-0394, USA We have developed a commercial Microsoft Windows software application using the multiple solutions averaging (MSA) algorithm [1] to invert data from a TSI 3040/3042 diffusion battery (DB) and 3022 or 3025 condensation particle counter (CPC). We examined the reliability and resolution of the software in a joint workshop held at TSI Incorporated in April of 1994. In the experiments, the CPC and DB were connected to a computer via the serial port and operated automatically under software control as a diffusional particle sizing (DPS) system. The software supported multi-scan data accumulation to provide improved counting statistics under low particle concentration conditions. For comparison, a TSI scanning mobility particle sizer (SMPS) consisting of a 3071 DMA, 3025 CPC and SMPS software was used. Two aerosol generators developed at Novosibirsk [2] produced aerosols of controlled particle size and concentration. The generators produced particles with a near log-normal size distribution with a geometric standard deviation (GSD) of 1.3 to 1.8 and mean diameter of 3 to 100nm. To obtain monodisperse aerosols with a GSD of 1.1, a TSI 3071 classifier was used downstream of each of the generators. Four types of particle size distribution were used for the tests: (1) Monodisperse aerosol, (GSD less than 1.15); (2) Wide near-log-normal distribution; (3) Superposition of two wide distributions; (4) Mixture of two monodisperse aerosols of different size ratio. Aerosols were analyzed simultaneously with both DPS and SMPS systems. A single DPS run took about 3 minutes to collect raw data and an additional minute to invert the data to a particle size distribution. Distributions obtained by both methods were in good agreement. The Diffusion battery system was shown to give reliable results for wide distributions (Figs. A, D) as well as for narrow peaks (Fig. B). The DPS can discriminate between two aerosols if their mean diameter ratio is more than 2.5 (Fig. B) but at a ratio of 2.5, the resolution becomes marginal (Fig. F). Although the SMPS can discriminate a mean diameter ratio of about 1.25, its performance degrades for relatively small particles (less than 20nm) and low particle concentrations (Fig. C). Reducing the signal-to-noise ratio by reducing the particle concentration caused
$747
$748
S.I. EREMENKOet al.
broadening of the resulting distribution and thus lowered resolution. However, it did not produce false peaks.
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Figure 1: Particle size distributions obtained with SMPS (upper in each pair) and DPS systems. Total count concentrations indicated inside pictures. The DPS system was shown to be effective for the size region 3-200nm and for concentrations from 102 to lOScm-3. The results also show that the computer-controlled DB system is optimum for wide aerosol distributions but that it can resolve monodisperse peaks with a diameter ratio of greater than 2.5. This makes the system useful for atmospheric aerosol studies where natural aerosol size distributions are usually rather wide and high size resolution is generally not a significant limitation. References. 1. Eremenko S. and Ankilov A.(1995) Conversion of the diffusion battery data to particle size distribution: Multiple solutions averaging algorithm (MSA). European Aerosol Conference. 2. Baklanov A.M and Dubtsov S.N.(1993) An experimental setup for aerosol spectrometers calibration. J.Aeros.Sci, 24, , $237.
Pergamon
Diffusion Battery Particle Sizing System Based on the MSA Data Conversion Algorithm: Experimental Examination S.I.Eremenko t, R.Caldow $, A.M.Baklanov t, M.Havlicek $ and G.Sem $ t - - Institute of Chemical Kinetics and Combustion, 630090, Novosibirsk, Russia :~ - - TSI Incorporated, P.O. Box 64394,St. Paul, MN 55164-0394, USA We have developed a commercial Microsoft Windows software application using the multiple solutions averaging (MSA) algorithm [1] to invert data from a TSI 3040/3042 diffusion battery (DB) and 3022 or 3025 condensation particle counter (CPC). We examined the reliability and resolution of the software in a joint workshop held at TSI Incorporated in April of 1994. In the experiments, the CPC and DB were connected to a computer via the serial port and operated automatically under software control as a diffusional particle sizing (DPS) system. The software supported multi-scan data accumulation to provide improved counting statistics under low particle concentration conditions. For comparison, a TSI scanning mobility particle sizer (SMPS) consisting of a 3071 DMA, 3025 CPC and SMPS software was used. Two aerosol generators developed at Novosibirsk [2] produced aerosols of controlled particle size and concentration. The generators produced particles with a near log-normal size distribution with a geometric standard deviation (GSD) of 1.3 to 1.8 and mean diameter of 3 to 100nm. To obtain monodisperse aerosols with a GSD of 1.1, a TSI 3071 classifier was used downstream of each of the generators. Four types of particle size distribution were used for the tests: (1) Monodisperse aerosol, (GSD less than 1.15); (2) Wide near-log-normal distribution; (3) Superposition of two wide distributions; (4) Mixture of two monodisperse aerosols of different size ratio. Aerosols were analyzed simultaneously with both DPS and SMPS systems. A single DPS run took about 3 minutes to collect raw data and an additional minute to invert the data to a particle size distribution. Distributions obtained by both methods were in good agreement. The Diffusion battery system was shown to give reliable results for wide distributions (Figs. A, D) as well as for narrow peaks (Fig. B). The DPS can discriminate between two aerosols if their mean diameter ratio is more than 2.5 (Fig. B) but at a ratio of 2.5, the resolution becomes marginal (Fig. F). Although the SMPS can discriminate a mean diameter ratio of about 1.25, its performance degrades for relatively small particles (less than 20nm) and low particle concentrations (Fig. C). Reducing the signal-to-noise ratio by reducing the particle concentration caused
$747
$748
S.I. EREMENKOet al.
broadening of the resulting distribution and thus lowered resolution. However, it did not produce false peaks.
B~
2.2.1dcnl~
IAjJlJildpl iJnJ;,i,. iluln
5nm
10
20
D .I.
10
SO
100
. . . ~ . , ,~
200
5nm
10
7.0,1dcm ~
20
IIii:iii
....
20
.
~0
3.3.10
100
¢m
50
100
200
5nm
10
20
~0
Onm 20
SO
F 7.3"10'¢n~
100
]
,I ,I
,,lJlltllh, ,Jk 5nm
C
100
lOnm 20
50
100
Figure 1: Particle size distributions obtained with SMPS (upper in each pair) and DPS systems. Total count concentrations indicated inside pictures. The DPS system was shown to be effective for the size region 3-200nm and for concentrations from 102 to lOScm-3. The results also show that the computer-controlled DB system is optimum for wide aerosol distributions but that it can resolve monodisperse peaks with a diameter ratio of greater than 2.5. This makes the system useful for atmospheric aerosol studies where natural aerosol size distributions are usually rather wide and high size resolution is generally not a significant limitation. References. 1. Eremenko S. and Ankilov A.(1995) Conversion of the diffusion battery data to particle size distribution: Multiple solutions averaging algorithm (MSA). European Aerosol Conference. 2. Baklanov A.M and Dubtsov S.N.(1993) An experimental setup for aerosol spectrometers calibration. J.Aeros.Sci, 24, , $237.