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Investigations of nanoparticle generation during surface decontamination by laser ablation at low fluence
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Aerosol Science 35 (2004) 1513 – 1526 www.elsevier.com/locate/jaerosci
Investigations of nanoparticle generation during surface decontamination by laser ablation at low fluence Doh-Won Leea,∗ , Meng-Dawn Chengb a Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831-0117, USA b Environmental Sciences Division, Oak Ridge National Laboratory (ORNL), 1 Bethel Valley Road,
Building 1505, MS 6038, Oak Ridge, TN 37831-6038, USA Received 17 June 2004; accepted 7 July 2004
Abstract Through laser ablation processes, significant amounts of particles can be generated from a surface of cement, stainless steel, or alumina. The minimal laser fluence (mJ cm−2 ), or threshold energy, required to produce a detectable amount of particles (100 particles cm−3 ) was investigated experimentally. The threshold energy was wavelengthdependent and was found to be the greatest for a pure material, alumina, then for a complex mixture, cement, and least for a simple mixture, stainless steel. The threshold energy requirement for three tested materials was found to be significantly higher for the IR (1064-nm) laser; it was 2.4–10.1 times higher than for the UV (266-nm) laser and 9.1–15.2 times higher than for the Vis (532-nm) laser. Interestingly, the UV laser has a higher threshold energy (1.5–4.0 times higher) than the Vis does. A log–log linear model was found to correlate particle production with the laser fluence of all three wavelengths. Of the three materials tested, stainless steel produced the most particles at a given fluence while alumina produced the fewest. Hypotheses of the particle generation mechanisms based upon the observations are also given here. 䉷 2004 Elsevier Ltd. All rights reserved. Keywords: Nanoparticle; Laser ablation; Low fluence; Cement; Stainless steel; Alumina
∗ Corresponding author. Tel.: +1-865-241-5918; fax: +1-865-576-8646.
E-mail addresses: [email protected] (D.-W. Lee), [email protected] (M.-D. Cheng). 0021-8502/$ - see front matter 䉷 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaerosci.2004.07.003
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1. Introduction Laser ablation is defined as the removal of material by laser irradiation. The interaction of high-power lasers with solid materials causes material removal through laser vaporization, desorption, sputtering, ejection, etching, spallation, plasma generation, etc. There have been continuous efforts to understand the ablation mechanisms (e.g., Caruso & Gratton, 1968; Austin, Michaud, Guenther, & Putman, 1973; Dreyfus, McDonald, & Von Gutfeld, 1987; Wood, Leboeuf, Chen, Geohegan, & Puretzky, 1998; Yilbas, Shuja, Arif, & Gondal, 2003; Zhigilei, 2003, and references therein), but there remain many unknowns. Simultaneously, there have been a number of reports on laser-ablation applications in industries such as medical surgery, materials processing (Houriet, Vacassy, & Hofmann, 1999; Korte et al., 1999), micromachining (Schäfer, Ihlemann, Marowsky, & Herman, 2001; Strgar & Možina, 2002), and decontamination (Schmidt, Li, & Spencer, 2001; Li, 2002; Minami, Lawrence, Li, Edwards, & Gale, 2002). Decontamination and decommissioning (D&D) of a large number of nuclear facilities is a major effort at US Department of Energy (DOE) complexes across the United States. The use of laser plasma for surface decontamination and cleaning is a new and effective technique. A large quantity of very small particles can be produced during the laser-decontamination process. Effective production of particles is critical in determining the surface cleaning efficiency of the process. However, the particles could contain contaminants like toxic heavy metals (e.g., Cr, Hg, Pb, and Ni), radionuclides (e.g., Th, Cs, and U), and hazardous organic solvents, all of which might cause health concerns. Therefore, it is imperative to understand particle generation from laser decontamination. In this paper, we describe experimental results of nanoparticle formation during laser decontamination processes using laboratory-prepared target surfaces made from Portland cement (cement), stainless steel 316, and pure alumina. The first two materials are commonly found in DOE installations. Our experiments were conducted to determine the threshold energy needed to remove particles from the surface of materials, an important factor in understanding the cleaning efficiency of laser techniques and in understanding laser-material interactions at the material surface. Another objective of this study was to characterize the generated particle size distribution with respect to the characteristics of laser energy during the decontamination processes.
2. Materials and method Unlike cement and stainless steel 316, alumina (Al2 O3 ) is a pure substance and has very favorable surface properties for studies of surface morphology, surface electronic structure, and laser ablation processes (Chase, 1994). Shown in Table 1 are the analytically identified compounds and their relative weight proportions for cement and stainless steel. Cement consists of several oxides and some trace elements; CaO is the major oxide (45%), with SiO2 second (16.7%). For our study we prepared Portland cement specimens of ∼ 5-cm-diameter circular blocks without aggregate, cured them under moist conditions for a minimum of 30 days, and then air-dried them for a minimum of 1 week before use (ASTM Standard Methods 1995, 1996). Stainless steel 316 consists of Fe, Cr, and Ni with weight percentages of 70, 19, and 11, respectively. The experimental setup is shown in Fig. 1. An airtight chamber with a volume of ∼ 14.5 l was used to house a sample 5-cm-diameter disc on a rotating mount. The target disc was rotated at about a fixed speed of 14.1 rotations per minute (rpm). The disc was subjected to laser treatment by which particles were
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Table 1 Chemical composition of targets used in this study Cement
Weight %
Stainless steel
Weight %
CaO SiO2 Al2 O3 Fe2 O3 MgO K2 O TiO2 Na2 O MnO2 SrO Loss-on-ignition (1400 ◦ C) Sum of oxides
44.79 16.65 3.35 2.66 2.01 0.49 0.21 0.14 0.06 0.04 28.53 98.95
Fe Cr Ni
70 19 11
Fig. 1. Experimental setup for surface cleaning and particle generation.
generated and characterized. Before a prepared disc sample/target was placed on the mount, the target surface was cleaned by wiping the surface gently using Kimwipes䉸 with ethanol to remove possible impurities such as residual grease and debris which may have remained on the surface from target disc manufacturing, cutting, handling, and so on. The target was mounted after ensuring there were no wet spots on the surface of the target. The target was then placed in the chamber filled with particle-free airflow for at least 10 min that also helped dry the surface somewhat. An Nd:YAG laser was used as the energy source for ablation and particle generation; the laser emits a 1064-, 532-, or 266-nm wavelength depending on the needs of a specific experiment. The maximum
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energy for each wavelength was as follows: 1064-nm wavelength 532-nm wavelength 266-nm wavelength
∼195(±2%) mJ per pulse, ∼95(±3.5%) mJ per pulse, ∼25(±7%) mJ per pulse.
The pulse width is 3–5 ns as in (the full-width half-maximum). The laser was fired at a fixed 10-Hz repetition rate. The circular beam diameter at the material surfaces was about 5 mm. The laser energy was monitored by splitting a small amount of the beam to an energy meter (Molectron EM 500 Single-Channel Joulemeter) located at a 90-degree-angle location (see Fig. 1). Depending on the number of particles generated and the purpose of a specific experiment, we employed a suite of instruments consisting of a scanning mobility particle sizer (SMPS; TSI SMPS 3936) and an ultrafine condensation particle counter (UCPC; TSI UCPC 3025A). A diluter was used in some instances, particularly when the UCPC was used alone because the maximum number concentration that the UCPC could measure is 105 particles cm−3 . The UCPC does not provide size distribution data, but it yields reliable counts of particles 3-nm in diameter and larger (Stolzenburg & McMurry, 1991; Stolzenburg, McDermott, & Schwartz, 1988). The SMPS provides the size distribution of particles, but does not provide reliable results if the particle number concentration is low—around 103 particles cm−3 (or less), for example—because it divides the number into 64 bin sizes, resulting in a small count for each bin and leading to a large Poisson counting uncertainty. The Poisson counting uncertainty is proportional to the square root of the total counts; i.e., for a count of 100, there is a 10% counting uncertainty. The higher the count, the lower the uncertainty will be. In order to examine whether any large particles were produced during the ablation processes, we used an aerodynamic particle sizer (APS; TSI model 3320), which is capable of detection of particles larger than 0.3 m. Throughout the whole set of experiments, almost no particles were detected by the APS. The highest concentration of particles larger than 0.3 m detected by the APS for all of the experimental conditions was about 2 particles cm−3 . The APS data confirm that almost no materials were removed from the surfaces in the form of particles larger than 0.3 m in diameter. The sampling rate of the APS was 5.0 l min−1 while that of the UCPC was 1.5 l min−1 . The total sampling rate was 6.5 l min−1 .
3. Results Data from a number of sets of experiments are reported in this paper. An experiment could run for as long as 8 h or for as short as 5 min, depending on the objective of the experiment. With a 5-s measurement rate for the UCPC, a very large volume of data was generated for each experiment. Under normal circumstances, 120 data points could be generated for analysis in 10 min. The maximum number of data points for a given experimental condition, collected in about 1 h, was 822. The size of the data points was more than sufficient to calculate the statistics. 3.1. Ablation by the Vis (532-nm) laser The experimental results obtained with the 532-nm laser are shown in Fig. 2. Note that both axes are on the log scale. The three regression lines are virtually parallel to each other. After the data were processed and quality-checked, the mean value and standard deviation were computed. Each data point on the plot
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1e+5
concentration (#/cm3)
cement stainless steel alumina regression lines
1e+4
1e+3
1e+2 0.01
0.1
1
10
2
fluence (J/cm )
Fig. 2. Observed log–log linear relationship between the generated particle concentration (# cm−3 ) and the 532-nm wavelength laser fluence (mJ cm−2 ).
is a result of at least 60 data points. The range of data variation on particle concentration, shown by the error bar associated with each data point, is small (coefficients of variation are in 10.9–14.6% range). Throughout the experiments, laser energy variation was between 1.3% and 4.5%. A higher irradiated energy leads to smaller variation. The goodness-of-fit indicated by the R 2 value was 0.96 for the alumina curve, 0.98 for cement, and 0.99 for stainless steel. These values indicate a strong linear relationship between particle concentration and laser fluence on a log–log scale. Based on the data, the particle concentrations produced, on a log–log scale, are linear functions of laser fluence energy at the 532-nm wavelength.
3.2. Ablation by the UV (266-nm) laser The results obtained with the 266-nm laser are shown in Fig. 3, which is also a log–log plot of total particle concentration vs the fluence of the 266-nm laser. The linear relationships for all three materials are strong, with R 2 values of 0.99 for cement and alumina and 0.98 for stainless steel. Again, the experimental data variations were small (coefficients of variation range from 10.6% to 15.6%). Each point and the error bar were obtained with at least 105 data points. The variation in laser energy was between 4.5% and 7.5%. The cement and stainless-steel regression lines are almost parallel to each other; however, the alumina line is steeper than the other two, with a slope that is almost 1.5 times that of the other two. One possible reason is the lack of data at fluences below 1.2 J cm−2 . No particles were reliably measured the alumina target when the irradiated laser energy was below a fluence of 1.2 J cm−2 . With the fluence ranging from 1.2 to 1.35 J cm−2 , as shown in Fig. 3, alumina particle number concentrations are still small (within 100–1000 particles cm−3 ), while cement and stainless steel have 103 –106 particles cm−3 . The results of 266-nm laser ablation also indicate that the total number concentration of particles generated is log–log linearly proportional to the applied UV laser fluence during surface treatments.
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concentration (#/cm3)
cement stainless steel alumina regression lines
1e+5
1e+4
1e+3
1e+2 0.1
1
10
fluence (J/cm2)
Fig. 3. Observed log–log linear relationship between the generated particle concentration (# cm−3 ) and the 266-nm wavelength laser fluence (mJ cm−2 ).
1e+6
concentration (#/cm3)
cement stainless steel alumina regression lines
1e+5
1e+4
1e+3
1e+2 1
10 fluence (J/cm2)
Fig. 4. Observed log–log linear relationship between the generated particle concentration (# cm−3 ) and the 1064-nm wavelength laser fluence (mJ cm−2 ).
3.3. Ablation by the IR (1064-nm) laser The results obtained with the 1064-nm laser are shown in Fig. 4, again on a log–log scale for both axes. Similar to the cases of 532- and 266-nm, data from the 1064-nm laser show strong linear relationships between the generated particle concentration and the applied fluence for all three materials. The R 2 values were 0.99 for stainless steel and alumina and 0.98 for cement. Again, variations in the experimental data were small (coefficients of variation are 10.9–17.2%). Each point and the error bar were obtained with 78 data points or more. The variation in laser energy was less than 1.0%. The regression line for cement has the steepest slope—about 1.7 times steeper than that of the alumina, which has the least slope. The
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Fig. 5. Particle size distributions for (a) 266-nm laser ablation of stainless steel at a fluence of 1240 mJ cm−2 , (b) 1064-nm laser ablation of stainless steel at a fluence of 5080 mJ cm−2 , (c) 266-nm laser ablation of cement at a fluence of 1220 mJ cm−2 .
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Table 2 Conditions and data presented in Fig. 5 Conditions
Fig. 5(a)
Fig. 5(b)
Fig. 5(c)
Laser wavelength and material Fluence, mJ cm−2 Total no. concentration, cm−3 Geometric mean, nm Geometric std. deviation
266-nm on stainless steel 1240 7.2 × 105 28 1.8
1064-nm on stainless steel 5080 1.3 × 105 34 2.0
266-nm on cement 1220 6.6 × 104 30 2.1
slope of the stainless-steel line is 1.3 times that of the alumina. For alumina, lack of data (less than one order of magnitude range of particle number concentrations) at fluences below 6.3 J cm−2 may play a role in the slope difference, as with the data for 266-nm laser: no particles were reliably measured from the alumina target when laser irradiation was below a fluence of 6.3 J cm−2 . Even though in the case of the 266-nm laser, the regression lines for the three materials seem not to be parallel, as they were with the other two wavelengths, the observed results still suggest that the total concentration of particle generated is log–log linearly proportional to laser fluence of the IR wavelength. The R 2 was 0.99 for stainless steel and alumina and 0.98 for cement. 3.4. Size distributions of the generated particles For all experiments, generated particles rarely exceeded 200-nm based on the SMPS data, which agrees with the results from APS measurements mentioned earlier that almost no particles larger than 0.3 m were detected. Examples of SMPS measurements are shown in Fig. 5. The conditions and data represented in Fig. 5 are summarized in Table 2. The ranges of variation in the particle concentration data are shown by the error bars associated with the data; the coefficient of variation for the number concentrations across the entire size distributions are from 18% to about 200%. Since linear scales are used for the concentration axes, the error bars around the modes are noticeably larger than those at both ends of the size distributions. From all SMPS data, the mode diameters were about 20-nm with the geometric mean diameters of around 30-nm for all three wavelengths and all materials tested. Regardless of the materials and laser wavelengths used, the size distributions of the generated particles did not change significantly for the given fluence range, as shown in Fig. 5, either.
4. Discussion 4.1. Generated particles The numerical values of the regression lines shown in Figs. 2–4 are summarized in Table 3. The particle concentrations (Y; particles cm−3 ) are observed to relate to the laser fluence (X; mJ cm−2 ) linearly on the log–log scale shown in these three figures. Although Liu, Mao, Yoo, and Russo (1999) used log–log linear regression for correlating the electron number density (not particle number concentration) with
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Table 3 Log–log linear regression of total particle concentration (Y; particles cm−3 ) vs fluence (X; mJ cm−2 )[log(Y )=log(A)+B log(X)] Bb
R2
532-nm Wavelength
Aa
Cement Stainless steel Alumina
4.70E−7 1.68E−4 3.57E−7
3.33 2.94 3.05
0.98 0.99 0.96
266-nm Wavelength Cement Stainless steel Alumina
2.11E−17 8.17E−18 2.55E−32
6.97 7.43 11.0
0.99 0.98 0.99
1064-nm Wavelength Cement Stainless steel Alumina
6.14E−21 7.86E−12 9.00E−12
6.03 4.43 3.50
0.98 0.99 0.99
a A = background particle concentration, particles cm −3 . b B = particle generation capacity (), particles cm −3 per mJ cm −2 .
laser irradiance, we believe our results are the first to correlate the produced particle concentration with laser fluence (distinguished by laser wavelength) by the following equation: Y = AX B .
(1)
The log–log function is given as follows: log(Y ) = log(A) + B log(X).
(2)
The slope B in the regression equation (2) describes the particle generation capacity () in particles cm−3 per mJ cm−2 , while the parameter A describes the background particle concentration in particles cm−3 . Calculated values for particle generation capability () were as follows: Wavelength (nm)
Cement
Stainless steel
Alumina
266 532 1064
6.97 3.33 6.03
7.43 2.94 4.43
11.0 3.05 3.50
For a given material, the 266-nm wavelength gives the greatest , 1064-nm gives the next, and 532-nm gives the least, as shown above and also in Table 3. The results indicate that the 266-nm laser wavelength has a higher particle generation capacity at a given laser energy than the 1064- and the 532-nm. Similar results were reported by Dupont, Caminat, Bournot, and Gauchon (1995). Laser absorptivity is likely to play a major role. The absorptivity of light on a material’s surface depends both on laser wavelength and laser energy. The absorptivity increases as the wavelength of the incident laser decreases and the fluence increases (Dreyfus et al., 1987). As shown in Figs. 2 and 3, the materials absorb the shorter 266-nm laser
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Table 4 Estimated threshold fluence (mJ cm−2 ) of particle production for the three materials (estimated using a threshold value of 100 particles cm−3 )
532-nm 266-nm 1064-nm
Alumina
Cement
Stainless steel
588 1132 5338
317 478 4826
92 371 908
energy more effectively than the 532-nm. Consequently, more particles are generated with the 266-nm laser than with the 532-nm. Since the 532-nm wavelength is shorter than the 1064-nm, a material surface should absorb the 532-nm laser more than the 1064-nm under the same conditions. The applied fluence range of the 1064-nm laser is approximately one order of magnitude higher than that of the 532-nm. Because of its higher energy, the 1064-nm laser would raise the surface temperature higher than would the 532-nm laser. Schmidt, Li, Spencer, and Key (1999) showed that absorptivity also increases as the surface temperature increases. We believe that the absorptivity increase due to a higher fluence, leading to higher surface temperature, enables the material to absorb more energy from the 1064-nm laser than from the 532-nm laser. Therefore, more particles were generated with the 1064-nm than with the 532-nm laser at the given fluences. The background particle concentrations as shown in Table 3 (in particles cm−3 , expressed as A in Eq. (2)) for all the materials tested were found to be virtually zero. The values of A are neither dependent on laser wavelength nor the material chosen for the target because, theoretically, A is only a function of the chamber condition prior to an experiment that is supposed to be reasonably clean, particle-free. The regression values for A reflect the clean conditions. 4.2. Threshold fluence To perform a reliable assessment of the threshold energy, one has to consider the uncertainty in counting “one particle” with the condensation particle counter. As an acceptable counting error, we chose 10%, which is the counting error for a particle concentration of 100 particles cm−3 according to the Poisson counting statistics. The threshold energy is conveniently defined as the minimal energy needed to remove material in the form of ablated particles (with the amount to be reliably detected by the UCPC) from a surface during a laser cleaning process. In the following analysis, we used 100 cm−3 as the lower quantifiable limit of a reliable particle number concentration. Corresponding to this limit, the threshold energy () is obtained by substituting 100 for the particle concentration (Y) into Eq. (2). The results (shown in Table 4) show that is dependent on both the laser wavelength and the material properties. At a given wavelength, alumina has the highest threshold energy; cement has the next, and stainless steel the lowest. The absorption coefficient of stainless steel is higher than the coefficients of cement and alumina. For example, the absorption coefficients of stainless steel, cement, and alumina are 6 × 105 , 3 × 104 , and 3 × 104 cm−1 , respectively, with respect to the 1064-nm wavelength (Yilbas et al., 2003; Schmidt & Li, 2002; Atanasov, Eugenieva, & Nedialkov, 2001). From a given amount of laser energy, stainless steel absorbs more energy than cement and alumina. That is, in order to absorb the same amount
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of energy, cement and alumina need higher incident laser energy than stainless steel. Therefore, the higher absorption coefficient is probably responsible for stainless steel having the lowest threshold energy. Although cement and alumina have the same absorption coefficients, cement has a lower threshold than alumina. Some of the components of cement can produce particles at temperatures as low as 400 ◦ C. Minami et al. (2002) found that decomposition and expansion of the cement materials commenced at about 400 ◦ C. This indicates that particles can be generated when the applied laser energy “heats” the cement surface above this temperature. If the energy is enough to raise the temperature above 1050 ◦ C, particle generation by further decomposition and fusion of other compounds in cement, such as silica, takes place (Minami et al., 2002). Alumina, on the other hand, is pure Al2 O3 having a melting point of 2054 ◦ C and needs to be heated to higher temperature to produce particles. Therefore, alumina has a higher threshold than cement has. 4.3. Particle generation rate Based on the experimental results presented in Sections 3.1–3.3 we calculated the particle generation rates, R, in units of # of particles per unit area per laser pulse from the following equation: AX B Q 1000 cm3 1 min R= , × rrep airradiated 1l 60 s
(3)
where A, X and B are the same as in Eq. (1), Q is the sampling flow rates (6.5 lpm), rrep is the repetition rate (10 pulses s−1 ), and the conversion factor for Q is shown in the bracket. The irradiated surface area, airradiated , is calculated as ( × [0.5 cm/2]2 ), where “0.5 cm” denotes the circular beam diameter. For a given material and a wavelength, R can be calculated at a fluence using A and B values in Table 3 for the given condition of material and wavelength. The calculated particle generation rates along with the measured concentration for all the conditions are shown in Table 5. Results show that stainless steel generated the most particles by laser ablation under the given laser fluence conditions used in this study; cement was next best; and alumina produced the least.
5. Conclusions The threshold energies required for producing particles from alumina, cement, and stainless-steel surface were estimated based on experimental data. The threshold energy is dependent upon the laser wavelength, fluence, and the material of the surface. We found that the threshold energy was greatest for alumina, then for cement, and least for stainless steel at a given laser wavelength. For a given material, the 532-nm laser required the least amount of energy. The energy requirement for the 1064-nm laser was higher than for the 266-nm laser. For the given fluence range (∼ 0.1–10 J cm−2 ), the 266-nm laser has the greatest particle generation capacity, with the 1064-nm next, and the 532-nm least, for all three materials (cement, stainless steel, and alumina). Throughout all sets of experiments, stainless steel produced the greatest amount of particles by laser ablation at a given laser fluence, cement was next, and alumina was last. A log–log linear model was found to provide a good correlation between particle production and laser fluence.
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Table 5 Calculated particle generation rates (R; particles cm−2 per pulse) with respect to laser fluence (mJ cm−2 ) for all of materials and laser wavelengths Wavelength (nm)
Material
Fluence
R
Concentration (particles/cm3 )
532
Stainless steel
1.06E+2 1.59E+2 2.38E+2 4.39E+2 7.99E+2
5.91E+3 3.17E+4 1.20E+5 5.80E+5 2.50E+6
1.07E+2 5.74E+2 2.18E+3 1.05E+4 4.54E+4
Cement
3.64E+2 4.89E+2 1.15E+3 1.98E+3
6.46E+3 3.54E+4 3.22E+5 2.60E+6
1.17E+2 6.42E+2 5.83E+3 4.71E+4
Alumina
7.74E+2 1.16E+3 2.05E+3 3.11E+3 4.14E+3 4.89E+3
7.01E+3 6.31E+4 4.28E+5 1.12E+6 1.86E+6 2.25E+6
1.27E+2 1.14E+3 7.76E+3 2.04E+4 3.37E+4 4.08E+4
Stainless steel
4.85E+2 5.03E+2 7.41E+2 1.01E+3 1.21E+3 1.28E+3
2.61E+4 5.05E+4 2.12E+6 8.52E+6 3.40E+7 4.00E+7
4.74E+2 9.16E+2 3.85E+4 1.55E+5 6.17E+5 7.24E+5
Cement
7.08E+2 8.22E+2 8.25E+2 9.45E+2 1.06E+3 1.14E+3 1.21E+3 1.22E+3 1.22E+3
6.91E+4 2.57E+5 2.97E+5 7.34E+5 1.50E+6 2.53E+6 3.33E+6 3.65E+6 3.50E+6
1.25E+3 4.65E+3 5.39E+3 1.33E+4 2.72E+4 4.59E+4 6.03E+4 6.61E+4 6.34E+4
Alumina
1.21E+3 1.25E+3 1.34E+3 1.35E+3
9.40E+3 1.53E+4 2.84E+4 3.32E+4
1.70E+2 2.76E+2 5.15E+2 6.01E+2
Stainless steel
1.21E+3 1.59E+3 1.85E+3 3.24E+3 4.74E+3
1.70E+4 6.89E+4 1.70E+5 1.23E+6 8.92E+6
3.07E+2 1.25E+3 3.08E+3 2.23E+4 1.62E+5
266
1064
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Table 5 (continued) Wavelength (nm)
Material
Fluence
R
Concentration (particles/cm3 )
Cement
4.65E+3 6.25E+3 6.88E+3 8.20E+3 9.21E+3 9.81E+3
5.68E+3 2.60E+4 3.88E+4 1.02E+5 2.87E+5 5.59E+5
1.03E+2 4.71E+2 7.03E+2 1.85E+3 5.20E+3 1.01E+4
Alumina
6.33E+3 7.86E+3 8.97E+3 9.76E+3 1.00E+4
1.07E+4 1.90E+4 3.28E+4 4.75E+4 5.10E+4
1.93E+2 3.45E+2 5.95E+2 8.62E+2 9.25E+2
Acknowledgements This work was performed by researchers at Oak Ridge National Laboratory (ORNL) for a project funded by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Environmental Science Management Program (EMSP Project # 82,807). ORNL is managed by UT-Battelle, LLC, for the US Department of Energy under Contract DE-AC05-00OR22725. D.-W. Lee was supported in part by an appointment to the ORNL Postdoctoral Research Associates Program administered jointly by ORNL and the Oak Ridge Institute for Science and Education. Baohua Gu of the Environmental Sciences Division at ORNL is acknowledged for providing and preparing the samples used in this study. We express our appreciation to the anonymous reviewers for their thorough and constructive comments.
References Atanasov, P.A., Eugenieva, E.D., & Nedialkov, N.N. (2001). Laser drilling of silicon nitride and alumina ceramics: A numerical and experimental study. Journal of Applied Physics, 89, 2013. Austin, R.R., Michaud, R., Guenther, A.H., & Putman, J. (1973). Effects of structure, composition, and stress on the laser damage threshold of homogeneous and inhomogeneous single films and multilayers. Applied Optics, 12, 665. Caruso, A., & Gratton, R. (1968). Some properties of the plasmas produced by irradiating light solids by laser pulses. Plasma Physics, 10, 867. Chase, L.L., 1994. Laser ablation and optical surface damage. In J.C. Miller (Ed.), Laser ablation: Principles and applications. Springer Series in Materials Science (Vol. 28, pp. 53–84). NY: Springer. Dreyfus, R.W., McDonald, F.A., & Von Gutfeld, R.J. (1987). Energy deposition at insulator surfaces below the ultraviolet photoablation threshold. Journal of Vacuum Science and Technology B, 5, 1521. Dupont, P., Caminat, P., Bournot, P., & Gauchon, J.P. (1995). Enhancement of material ablation using 248, 308, 532, 1064-nm laser pulse with a water film on the treated surface. Journal of Applied Physics, 78, 2022. Houriet, R., Vacassy, R., & Hofmann, H. (1999). Synthesis of powders and films using a new laser ablation technique. NanoStructured Materials, 11, 1155. Korte, F., Nolte, S., Chichkov, B.N., Bauer, T., Kamlage, G., Wagner, T., Fallnich, C., & Welling, H. (1999). Far-field and near-field material processing with femtosecond laser pulses. Applied Physics A, 69, S7.
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Li, L. (2002). The potential role of high-power lasers in nuclear decommissioning. Nuclear Energy-Journal of British Nuclear Energy Society, 41, 397. Liu, H.C., Mao, X.L., Yoo, J.H., & Russo, R.E. (1999). Early phase laser induced plasma diagnostic and mass removal during single-pulse laser ablation of silicon. Spectrochimica Acta B, 54, 1607. Minami, K., Lawrence, J., Li, L., Edwards, R.E., & Gale, A.W. (2002). Comparison of CO2 , Nd:YAG and high power diode lasers for the ablation of tile grout. Applied Surface Science, 186, 256. Schäfer, D., Ihlemann, J., Marowsky, G., & Herman, P.R. (2001). F2 -laser ablation patterning of dielectric layers. Applied Physics A, 72, 377. Schmidt, M.J.J., & Li, L. (2002). Surface modification of OPC-based cement using a frequency doubled Nd:YAG laser system. Applied Surface Science, 186, 75. Schmidt, M.J.J., Li, L., & Spencer, J.T. (2001). Removal of chlorinated rubber coatings from concrete surfaces using an RF excited CO2 laser. Journal of Materials Processing Technology, 114, 139. Schmidt, M.J.J., Li, L., Spencer, J.T., & Key, P.H. (1999). A comparative study of laser wavelength on laser removal of chlorinated rubber. Applied Surface Science, 138–9, 418. Stolzenburg, M.R., McDermott, W.T., & Schwartz, A. (1988). Performance studies of ultrafine efficiency membrane filters using a 3.5-nm particle detector. Journal of Aerosol Science, 19, 1015. Stolzenburg, M.R., & McMurry, P.H. (1991). An ultrafine aerosol condensation nucleus counter. Aerosol Science and Technology, 14, 48. Strgar, S., & Možina, J. (2002). An optodynamic determination of the depth of laser-drilled holes by the simultaneous detection of ultrasonic waves in the air and in the workpiece. Ultrasonics, 40, 791. Wood, R.F., Leboeuf, J.N., Chen, K.R., Geohegan, D.B., & Puretzky, A.A. (1998). Dynamics of plume propagation, splitting, and nanoparticle formation during pulsed-laser ablation. Applied Surface Science, 127–9, 151. Yilbas, B.S., Shuja, S.Z., Arif, A., & Gondal, M.A. (2003). Laser-shock processing of steel. Journal of Materials Processing Technology, 135, 6. Zhigilei, L.V. (2003). Dynamics of the plume formation and parameters of the ejected clusters in short-pulse laser ablation. Applied Physics A, 76, 339.
Aerosol Science 35 (2004) 1513 – 1526 www.elsevier.com/locate/jaerosci
Investigations of nanoparticle generation during surface decontamination by laser ablation at low fluence Doh-Won Leea,∗ , Meng-Dawn Chengb a Oak Ridge Institute for Science and Education, Oak Ridge, TN 37831-0117, USA b Environmental Sciences Division, Oak Ridge National Laboratory (ORNL), 1 Bethel Valley Road,
Building 1505, MS 6038, Oak Ridge, TN 37831-6038, USA Received 17 June 2004; accepted 7 July 2004
Abstract Through laser ablation processes, significant amounts of particles can be generated from a surface of cement, stainless steel, or alumina. The minimal laser fluence (mJ cm−2 ), or threshold energy, required to produce a detectable amount of particles (100 particles cm−3 ) was investigated experimentally. The threshold energy was wavelengthdependent and was found to be the greatest for a pure material, alumina, then for a complex mixture, cement, and least for a simple mixture, stainless steel. The threshold energy requirement for three tested materials was found to be significantly higher for the IR (1064-nm) laser; it was 2.4–10.1 times higher than for the UV (266-nm) laser and 9.1–15.2 times higher than for the Vis (532-nm) laser. Interestingly, the UV laser has a higher threshold energy (1.5–4.0 times higher) than the Vis does. A log–log linear model was found to correlate particle production with the laser fluence of all three wavelengths. Of the three materials tested, stainless steel produced the most particles at a given fluence while alumina produced the fewest. Hypotheses of the particle generation mechanisms based upon the observations are also given here. 䉷 2004 Elsevier Ltd. All rights reserved. Keywords: Nanoparticle; Laser ablation; Low fluence; Cement; Stainless steel; Alumina
∗ Corresponding author. Tel.: +1-865-241-5918; fax: +1-865-576-8646.
E-mail addresses: [email protected] (D.-W. Lee), [email protected] (M.-D. Cheng). 0021-8502/$ - see front matter 䉷 2004 Elsevier Ltd. All rights reserved. doi:10.1016/j.jaerosci.2004.07.003
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1. Introduction Laser ablation is defined as the removal of material by laser irradiation. The interaction of high-power lasers with solid materials causes material removal through laser vaporization, desorption, sputtering, ejection, etching, spallation, plasma generation, etc. There have been continuous efforts to understand the ablation mechanisms (e.g., Caruso & Gratton, 1968; Austin, Michaud, Guenther, & Putman, 1973; Dreyfus, McDonald, & Von Gutfeld, 1987; Wood, Leboeuf, Chen, Geohegan, & Puretzky, 1998; Yilbas, Shuja, Arif, & Gondal, 2003; Zhigilei, 2003, and references therein), but there remain many unknowns. Simultaneously, there have been a number of reports on laser-ablation applications in industries such as medical surgery, materials processing (Houriet, Vacassy, & Hofmann, 1999; Korte et al., 1999), micromachining (Schäfer, Ihlemann, Marowsky, & Herman, 2001; Strgar & Možina, 2002), and decontamination (Schmidt, Li, & Spencer, 2001; Li, 2002; Minami, Lawrence, Li, Edwards, & Gale, 2002). Decontamination and decommissioning (D&D) of a large number of nuclear facilities is a major effort at US Department of Energy (DOE) complexes across the United States. The use of laser plasma for surface decontamination and cleaning is a new and effective technique. A large quantity of very small particles can be produced during the laser-decontamination process. Effective production of particles is critical in determining the surface cleaning efficiency of the process. However, the particles could contain contaminants like toxic heavy metals (e.g., Cr, Hg, Pb, and Ni), radionuclides (e.g., Th, Cs, and U), and hazardous organic solvents, all of which might cause health concerns. Therefore, it is imperative to understand particle generation from laser decontamination. In this paper, we describe experimental results of nanoparticle formation during laser decontamination processes using laboratory-prepared target surfaces made from Portland cement (cement), stainless steel 316, and pure alumina. The first two materials are commonly found in DOE installations. Our experiments were conducted to determine the threshold energy needed to remove particles from the surface of materials, an important factor in understanding the cleaning efficiency of laser techniques and in understanding laser-material interactions at the material surface. Another objective of this study was to characterize the generated particle size distribution with respect to the characteristics of laser energy during the decontamination processes.
2. Materials and method Unlike cement and stainless steel 316, alumina (Al2 O3 ) is a pure substance and has very favorable surface properties for studies of surface morphology, surface electronic structure, and laser ablation processes (Chase, 1994). Shown in Table 1 are the analytically identified compounds and their relative weight proportions for cement and stainless steel. Cement consists of several oxides and some trace elements; CaO is the major oxide (45%), with SiO2 second (16.7%). For our study we prepared Portland cement specimens of ∼ 5-cm-diameter circular blocks without aggregate, cured them under moist conditions for a minimum of 30 days, and then air-dried them for a minimum of 1 week before use (ASTM Standard Methods 1995, 1996). Stainless steel 316 consists of Fe, Cr, and Ni with weight percentages of 70, 19, and 11, respectively. The experimental setup is shown in Fig. 1. An airtight chamber with a volume of ∼ 14.5 l was used to house a sample 5-cm-diameter disc on a rotating mount. The target disc was rotated at about a fixed speed of 14.1 rotations per minute (rpm). The disc was subjected to laser treatment by which particles were
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Table 1 Chemical composition of targets used in this study Cement
Weight %
Stainless steel
Weight %
CaO SiO2 Al2 O3 Fe2 O3 MgO K2 O TiO2 Na2 O MnO2 SrO Loss-on-ignition (1400 ◦ C) Sum of oxides
44.79 16.65 3.35 2.66 2.01 0.49 0.21 0.14 0.06 0.04 28.53 98.95
Fe Cr Ni
70 19 11
Fig. 1. Experimental setup for surface cleaning and particle generation.
generated and characterized. Before a prepared disc sample/target was placed on the mount, the target surface was cleaned by wiping the surface gently using Kimwipes䉸 with ethanol to remove possible impurities such as residual grease and debris which may have remained on the surface from target disc manufacturing, cutting, handling, and so on. The target was mounted after ensuring there were no wet spots on the surface of the target. The target was then placed in the chamber filled with particle-free airflow for at least 10 min that also helped dry the surface somewhat. An Nd:YAG laser was used as the energy source for ablation and particle generation; the laser emits a 1064-, 532-, or 266-nm wavelength depending on the needs of a specific experiment. The maximum
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energy for each wavelength was as follows: 1064-nm wavelength 532-nm wavelength 266-nm wavelength
∼195(±2%) mJ per pulse, ∼95(±3.5%) mJ per pulse, ∼25(±7%) mJ per pulse.
The pulse width is 3–5 ns as in (the full-width half-maximum). The laser was fired at a fixed 10-Hz repetition rate. The circular beam diameter at the material surfaces was about 5 mm. The laser energy was monitored by splitting a small amount of the beam to an energy meter (Molectron EM 500 Single-Channel Joulemeter) located at a 90-degree-angle location (see Fig. 1). Depending on the number of particles generated and the purpose of a specific experiment, we employed a suite of instruments consisting of a scanning mobility particle sizer (SMPS; TSI SMPS 3936) and an ultrafine condensation particle counter (UCPC; TSI UCPC 3025A). A diluter was used in some instances, particularly when the UCPC was used alone because the maximum number concentration that the UCPC could measure is 105 particles cm−3 . The UCPC does not provide size distribution data, but it yields reliable counts of particles 3-nm in diameter and larger (Stolzenburg & McMurry, 1991; Stolzenburg, McDermott, & Schwartz, 1988). The SMPS provides the size distribution of particles, but does not provide reliable results if the particle number concentration is low—around 103 particles cm−3 (or less), for example—because it divides the number into 64 bin sizes, resulting in a small count for each bin and leading to a large Poisson counting uncertainty. The Poisson counting uncertainty is proportional to the square root of the total counts; i.e., for a count of 100, there is a 10% counting uncertainty. The higher the count, the lower the uncertainty will be. In order to examine whether any large particles were produced during the ablation processes, we used an aerodynamic particle sizer (APS; TSI model 3320), which is capable of detection of particles larger than 0.3 m. Throughout the whole set of experiments, almost no particles were detected by the APS. The highest concentration of particles larger than 0.3 m detected by the APS for all of the experimental conditions was about 2 particles cm−3 . The APS data confirm that almost no materials were removed from the surfaces in the form of particles larger than 0.3 m in diameter. The sampling rate of the APS was 5.0 l min−1 while that of the UCPC was 1.5 l min−1 . The total sampling rate was 6.5 l min−1 .
3. Results Data from a number of sets of experiments are reported in this paper. An experiment could run for as long as 8 h or for as short as 5 min, depending on the objective of the experiment. With a 5-s measurement rate for the UCPC, a very large volume of data was generated for each experiment. Under normal circumstances, 120 data points could be generated for analysis in 10 min. The maximum number of data points for a given experimental condition, collected in about 1 h, was 822. The size of the data points was more than sufficient to calculate the statistics. 3.1. Ablation by the Vis (532-nm) laser The experimental results obtained with the 532-nm laser are shown in Fig. 2. Note that both axes are on the log scale. The three regression lines are virtually parallel to each other. After the data were processed and quality-checked, the mean value and standard deviation were computed. Each data point on the plot
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1e+5
concentration (#/cm3)
cement stainless steel alumina regression lines
1e+4
1e+3
1e+2 0.01
0.1
1
10
2
fluence (J/cm )
Fig. 2. Observed log–log linear relationship between the generated particle concentration (# cm−3 ) and the 532-nm wavelength laser fluence (mJ cm−2 ).
is a result of at least 60 data points. The range of data variation on particle concentration, shown by the error bar associated with each data point, is small (coefficients of variation are in 10.9–14.6% range). Throughout the experiments, laser energy variation was between 1.3% and 4.5%. A higher irradiated energy leads to smaller variation. The goodness-of-fit indicated by the R 2 value was 0.96 for the alumina curve, 0.98 for cement, and 0.99 for stainless steel. These values indicate a strong linear relationship between particle concentration and laser fluence on a log–log scale. Based on the data, the particle concentrations produced, on a log–log scale, are linear functions of laser fluence energy at the 532-nm wavelength.
3.2. Ablation by the UV (266-nm) laser The results obtained with the 266-nm laser are shown in Fig. 3, which is also a log–log plot of total particle concentration vs the fluence of the 266-nm laser. The linear relationships for all three materials are strong, with R 2 values of 0.99 for cement and alumina and 0.98 for stainless steel. Again, the experimental data variations were small (coefficients of variation range from 10.6% to 15.6%). Each point and the error bar were obtained with at least 105 data points. The variation in laser energy was between 4.5% and 7.5%. The cement and stainless-steel regression lines are almost parallel to each other; however, the alumina line is steeper than the other two, with a slope that is almost 1.5 times that of the other two. One possible reason is the lack of data at fluences below 1.2 J cm−2 . No particles were reliably measured the alumina target when the irradiated laser energy was below a fluence of 1.2 J cm−2 . With the fluence ranging from 1.2 to 1.35 J cm−2 , as shown in Fig. 3, alumina particle number concentrations are still small (within 100–1000 particles cm−3 ), while cement and stainless steel have 103 –106 particles cm−3 . The results of 266-nm laser ablation also indicate that the total number concentration of particles generated is log–log linearly proportional to the applied UV laser fluence during surface treatments.
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concentration (#/cm3)
cement stainless steel alumina regression lines
1e+5
1e+4
1e+3
1e+2 0.1
1
10
fluence (J/cm2)
Fig. 3. Observed log–log linear relationship between the generated particle concentration (# cm−3 ) and the 266-nm wavelength laser fluence (mJ cm−2 ).
1e+6
concentration (#/cm3)
cement stainless steel alumina regression lines
1e+5
1e+4
1e+3
1e+2 1
10 fluence (J/cm2)
Fig. 4. Observed log–log linear relationship between the generated particle concentration (# cm−3 ) and the 1064-nm wavelength laser fluence (mJ cm−2 ).
3.3. Ablation by the IR (1064-nm) laser The results obtained with the 1064-nm laser are shown in Fig. 4, again on a log–log scale for both axes. Similar to the cases of 532- and 266-nm, data from the 1064-nm laser show strong linear relationships between the generated particle concentration and the applied fluence for all three materials. The R 2 values were 0.99 for stainless steel and alumina and 0.98 for cement. Again, variations in the experimental data were small (coefficients of variation are 10.9–17.2%). Each point and the error bar were obtained with 78 data points or more. The variation in laser energy was less than 1.0%. The regression line for cement has the steepest slope—about 1.7 times steeper than that of the alumina, which has the least slope. The
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Fig. 5. Particle size distributions for (a) 266-nm laser ablation of stainless steel at a fluence of 1240 mJ cm−2 , (b) 1064-nm laser ablation of stainless steel at a fluence of 5080 mJ cm−2 , (c) 266-nm laser ablation of cement at a fluence of 1220 mJ cm−2 .
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Table 2 Conditions and data presented in Fig. 5 Conditions
Fig. 5(a)
Fig. 5(b)
Fig. 5(c)
Laser wavelength and material Fluence, mJ cm−2 Total no. concentration, cm−3 Geometric mean, nm Geometric std. deviation
266-nm on stainless steel 1240 7.2 × 105 28 1.8
1064-nm on stainless steel 5080 1.3 × 105 34 2.0
266-nm on cement 1220 6.6 × 104 30 2.1
slope of the stainless-steel line is 1.3 times that of the alumina. For alumina, lack of data (less than one order of magnitude range of particle number concentrations) at fluences below 6.3 J cm−2 may play a role in the slope difference, as with the data for 266-nm laser: no particles were reliably measured from the alumina target when laser irradiation was below a fluence of 6.3 J cm−2 . Even though in the case of the 266-nm laser, the regression lines for the three materials seem not to be parallel, as they were with the other two wavelengths, the observed results still suggest that the total concentration of particle generated is log–log linearly proportional to laser fluence of the IR wavelength. The R 2 was 0.99 for stainless steel and alumina and 0.98 for cement. 3.4. Size distributions of the generated particles For all experiments, generated particles rarely exceeded 200-nm based on the SMPS data, which agrees with the results from APS measurements mentioned earlier that almost no particles larger than 0.3 m were detected. Examples of SMPS measurements are shown in Fig. 5. The conditions and data represented in Fig. 5 are summarized in Table 2. The ranges of variation in the particle concentration data are shown by the error bars associated with the data; the coefficient of variation for the number concentrations across the entire size distributions are from 18% to about 200%. Since linear scales are used for the concentration axes, the error bars around the modes are noticeably larger than those at both ends of the size distributions. From all SMPS data, the mode diameters were about 20-nm with the geometric mean diameters of around 30-nm for all three wavelengths and all materials tested. Regardless of the materials and laser wavelengths used, the size distributions of the generated particles did not change significantly for the given fluence range, as shown in Fig. 5, either.
4. Discussion 4.1. Generated particles The numerical values of the regression lines shown in Figs. 2–4 are summarized in Table 3. The particle concentrations (Y; particles cm−3 ) are observed to relate to the laser fluence (X; mJ cm−2 ) linearly on the log–log scale shown in these three figures. Although Liu, Mao, Yoo, and Russo (1999) used log–log linear regression for correlating the electron number density (not particle number concentration) with
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Table 3 Log–log linear regression of total particle concentration (Y; particles cm−3 ) vs fluence (X; mJ cm−2 )[log(Y )=log(A)+B log(X)] Bb
R2
532-nm Wavelength
Aa
Cement Stainless steel Alumina
4.70E−7 1.68E−4 3.57E−7
3.33 2.94 3.05
0.98 0.99 0.96
266-nm Wavelength Cement Stainless steel Alumina
2.11E−17 8.17E−18 2.55E−32
6.97 7.43 11.0
0.99 0.98 0.99
1064-nm Wavelength Cement Stainless steel Alumina
6.14E−21 7.86E−12 9.00E−12
6.03 4.43 3.50
0.98 0.99 0.99
a A = background particle concentration, particles cm −3 . b B = particle generation capacity (), particles cm −3 per mJ cm −2 .
laser irradiance, we believe our results are the first to correlate the produced particle concentration with laser fluence (distinguished by laser wavelength) by the following equation: Y = AX B .
(1)
The log–log function is given as follows: log(Y ) = log(A) + B log(X).
(2)
The slope B in the regression equation (2) describes the particle generation capacity () in particles cm−3 per mJ cm−2 , while the parameter A describes the background particle concentration in particles cm−3 . Calculated values for particle generation capability () were as follows: Wavelength (nm)
Cement
Stainless steel
Alumina
266 532 1064
6.97 3.33 6.03
7.43 2.94 4.43
11.0 3.05 3.50
For a given material, the 266-nm wavelength gives the greatest , 1064-nm gives the next, and 532-nm gives the least, as shown above and also in Table 3. The results indicate that the 266-nm laser wavelength has a higher particle generation capacity at a given laser energy than the 1064- and the 532-nm. Similar results were reported by Dupont, Caminat, Bournot, and Gauchon (1995). Laser absorptivity is likely to play a major role. The absorptivity of light on a material’s surface depends both on laser wavelength and laser energy. The absorptivity increases as the wavelength of the incident laser decreases and the fluence increases (Dreyfus et al., 1987). As shown in Figs. 2 and 3, the materials absorb the shorter 266-nm laser
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Table 4 Estimated threshold fluence (mJ cm−2 ) of particle production for the three materials (estimated using a threshold value of 100 particles cm−3 )
532-nm 266-nm 1064-nm
Alumina
Cement
Stainless steel
588 1132 5338
317 478 4826
92 371 908
energy more effectively than the 532-nm. Consequently, more particles are generated with the 266-nm laser than with the 532-nm. Since the 532-nm wavelength is shorter than the 1064-nm, a material surface should absorb the 532-nm laser more than the 1064-nm under the same conditions. The applied fluence range of the 1064-nm laser is approximately one order of magnitude higher than that of the 532-nm. Because of its higher energy, the 1064-nm laser would raise the surface temperature higher than would the 532-nm laser. Schmidt, Li, Spencer, and Key (1999) showed that absorptivity also increases as the surface temperature increases. We believe that the absorptivity increase due to a higher fluence, leading to higher surface temperature, enables the material to absorb more energy from the 1064-nm laser than from the 532-nm laser. Therefore, more particles were generated with the 1064-nm than with the 532-nm laser at the given fluences. The background particle concentrations as shown in Table 3 (in particles cm−3 , expressed as A in Eq. (2)) for all the materials tested were found to be virtually zero. The values of A are neither dependent on laser wavelength nor the material chosen for the target because, theoretically, A is only a function of the chamber condition prior to an experiment that is supposed to be reasonably clean, particle-free. The regression values for A reflect the clean conditions. 4.2. Threshold fluence To perform a reliable assessment of the threshold energy, one has to consider the uncertainty in counting “one particle” with the condensation particle counter. As an acceptable counting error, we chose 10%, which is the counting error for a particle concentration of 100 particles cm−3 according to the Poisson counting statistics. The threshold energy is conveniently defined as the minimal energy needed to remove material in the form of ablated particles (with the amount to be reliably detected by the UCPC) from a surface during a laser cleaning process. In the following analysis, we used 100 cm−3 as the lower quantifiable limit of a reliable particle number concentration. Corresponding to this limit, the threshold energy () is obtained by substituting 100 for the particle concentration (Y) into Eq. (2). The results (shown in Table 4) show that is dependent on both the laser wavelength and the material properties. At a given wavelength, alumina has the highest threshold energy; cement has the next, and stainless steel the lowest. The absorption coefficient of stainless steel is higher than the coefficients of cement and alumina. For example, the absorption coefficients of stainless steel, cement, and alumina are 6 × 105 , 3 × 104 , and 3 × 104 cm−1 , respectively, with respect to the 1064-nm wavelength (Yilbas et al., 2003; Schmidt & Li, 2002; Atanasov, Eugenieva, & Nedialkov, 2001). From a given amount of laser energy, stainless steel absorbs more energy than cement and alumina. That is, in order to absorb the same amount
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of energy, cement and alumina need higher incident laser energy than stainless steel. Therefore, the higher absorption coefficient is probably responsible for stainless steel having the lowest threshold energy. Although cement and alumina have the same absorption coefficients, cement has a lower threshold than alumina. Some of the components of cement can produce particles at temperatures as low as 400 ◦ C. Minami et al. (2002) found that decomposition and expansion of the cement materials commenced at about 400 ◦ C. This indicates that particles can be generated when the applied laser energy “heats” the cement surface above this temperature. If the energy is enough to raise the temperature above 1050 ◦ C, particle generation by further decomposition and fusion of other compounds in cement, such as silica, takes place (Minami et al., 2002). Alumina, on the other hand, is pure Al2 O3 having a melting point of 2054 ◦ C and needs to be heated to higher temperature to produce particles. Therefore, alumina has a higher threshold than cement has. 4.3. Particle generation rate Based on the experimental results presented in Sections 3.1–3.3 we calculated the particle generation rates, R, in units of # of particles per unit area per laser pulse from the following equation: AX B Q 1000 cm3 1 min R= , × rrep airradiated 1l 60 s
(3)
where A, X and B are the same as in Eq. (1), Q is the sampling flow rates (6.5 lpm), rrep is the repetition rate (10 pulses s−1 ), and the conversion factor for Q is shown in the bracket. The irradiated surface area, airradiated , is calculated as ( × [0.5 cm/2]2 ), where “0.5 cm” denotes the circular beam diameter. For a given material and a wavelength, R can be calculated at a fluence using A and B values in Table 3 for the given condition of material and wavelength. The calculated particle generation rates along with the measured concentration for all the conditions are shown in Table 5. Results show that stainless steel generated the most particles by laser ablation under the given laser fluence conditions used in this study; cement was next best; and alumina produced the least.
5. Conclusions The threshold energies required for producing particles from alumina, cement, and stainless-steel surface were estimated based on experimental data. The threshold energy is dependent upon the laser wavelength, fluence, and the material of the surface. We found that the threshold energy was greatest for alumina, then for cement, and least for stainless steel at a given laser wavelength. For a given material, the 532-nm laser required the least amount of energy. The energy requirement for the 1064-nm laser was higher than for the 266-nm laser. For the given fluence range (∼ 0.1–10 J cm−2 ), the 266-nm laser has the greatest particle generation capacity, with the 1064-nm next, and the 532-nm least, for all three materials (cement, stainless steel, and alumina). Throughout all sets of experiments, stainless steel produced the greatest amount of particles by laser ablation at a given laser fluence, cement was next, and alumina was last. A log–log linear model was found to provide a good correlation between particle production and laser fluence.
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Table 5 Calculated particle generation rates (R; particles cm−2 per pulse) with respect to laser fluence (mJ cm−2 ) for all of materials and laser wavelengths Wavelength (nm)
Material
Fluence
R
Concentration (particles/cm3 )
532
Stainless steel
1.06E+2 1.59E+2 2.38E+2 4.39E+2 7.99E+2
5.91E+3 3.17E+4 1.20E+5 5.80E+5 2.50E+6
1.07E+2 5.74E+2 2.18E+3 1.05E+4 4.54E+4
Cement
3.64E+2 4.89E+2 1.15E+3 1.98E+3
6.46E+3 3.54E+4 3.22E+5 2.60E+6
1.17E+2 6.42E+2 5.83E+3 4.71E+4
Alumina
7.74E+2 1.16E+3 2.05E+3 3.11E+3 4.14E+3 4.89E+3
7.01E+3 6.31E+4 4.28E+5 1.12E+6 1.86E+6 2.25E+6
1.27E+2 1.14E+3 7.76E+3 2.04E+4 3.37E+4 4.08E+4
Stainless steel
4.85E+2 5.03E+2 7.41E+2 1.01E+3 1.21E+3 1.28E+3
2.61E+4 5.05E+4 2.12E+6 8.52E+6 3.40E+7 4.00E+7
4.74E+2 9.16E+2 3.85E+4 1.55E+5 6.17E+5 7.24E+5
Cement
7.08E+2 8.22E+2 8.25E+2 9.45E+2 1.06E+3 1.14E+3 1.21E+3 1.22E+3 1.22E+3
6.91E+4 2.57E+5 2.97E+5 7.34E+5 1.50E+6 2.53E+6 3.33E+6 3.65E+6 3.50E+6
1.25E+3 4.65E+3 5.39E+3 1.33E+4 2.72E+4 4.59E+4 6.03E+4 6.61E+4 6.34E+4
Alumina
1.21E+3 1.25E+3 1.34E+3 1.35E+3
9.40E+3 1.53E+4 2.84E+4 3.32E+4
1.70E+2 2.76E+2 5.15E+2 6.01E+2
Stainless steel
1.21E+3 1.59E+3 1.85E+3 3.24E+3 4.74E+3
1.70E+4 6.89E+4 1.70E+5 1.23E+6 8.92E+6
3.07E+2 1.25E+3 3.08E+3 2.23E+4 1.62E+5
266
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1525
Table 5 (continued) Wavelength (nm)
Material
Fluence
R
Concentration (particles/cm3 )
Cement
4.65E+3 6.25E+3 6.88E+3 8.20E+3 9.21E+3 9.81E+3
5.68E+3 2.60E+4 3.88E+4 1.02E+5 2.87E+5 5.59E+5
1.03E+2 4.71E+2 7.03E+2 1.85E+3 5.20E+3 1.01E+4
Alumina
6.33E+3 7.86E+3 8.97E+3 9.76E+3 1.00E+4
1.07E+4 1.90E+4 3.28E+4 4.75E+4 5.10E+4
1.93E+2 3.45E+2 5.95E+2 8.62E+2 9.25E+2
Acknowledgements This work was performed by researchers at Oak Ridge National Laboratory (ORNL) for a project funded by the US Department of Energy, Office of Science, Biological and Environmental Research Program, Environmental Science Management Program (EMSP Project # 82,807). ORNL is managed by UT-Battelle, LLC, for the US Department of Energy under Contract DE-AC05-00OR22725. D.-W. Lee was supported in part by an appointment to the ORNL Postdoctoral Research Associates Program administered jointly by ORNL and the Oak Ridge Institute for Science and Education. Baohua Gu of the Environmental Sciences Division at ORNL is acknowledged for providing and preparing the samples used in this study. We express our appreciation to the anonymous reviewers for their thorough and constructive comments.
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