www.elsevier.com/locate/sab

Spectrochimica Acta Part B

Aluminum alloy analysis using microchip-laser induced

breakdown spectroscopyB

Andrew Freedman*, Frank J. Iannarilli Jr., Joda C. Wormhoudt

Center for Sensor Systems and Technologies, Aerodyne Research, Inc., 45 Manning Road Billerica, MA, 01821-3976, USA

Received 23 March 2005; accepted 28 March 2005

Available online 23 May 2005

Abstract

A laser induced breakdown spectroscopy-based apparatus for the analysis of aluminum alloys which employs a microchip laser and a

handheld spectrometer with an ungated, non-intensified CCD array has been built and tested. The microchip laser, which emits low energy

pulses (4–15 AJ) at high repetition rates (1–10 kHz) at 1064 nm, produces, when focused, an ablation crater with a radius on the order of

only 10 Am. The resulting emission is focused onto an optical fiber connected to 0.10 m focal length spectrometer with a spectral range of

275–413 nm. The apparatus was tested using 30 different aluminum alloy reference samples. Two techniques for constructing calibration

curves from the data, peak integration and partial least squares regression, were quantitatively evaluated. Results for Fe, Mg, Mn, Ni, Si, and

Zn indicated limits of detection (LOD) that ranged from 0.05 to 0.14 wt.% and overall measurement errors which varied from 0.06 to 0.18

wt.%. Higher limits of detection and overall error for Cu (>0.3 wt.%) were attributed to analysis problems associated with the presence of

optically thick lines and a spectral interference from Zn. Improvements in design and component sensitivity should increase overall

performance by at least a factor of 2, allowing for dependable aluminum alloy classification.

D 2005 Elsevier B.V. All rights reserved.

Keywords: Breakdown spectroscopy; Microchip laser; Aluminum alloy; Partial least squares; Miniature spectrometer; Alloy classification

1. Introduction

As part of an investigation into the use of a microchip

laser as part of a laser induced breakdown spectroscopy

(LIBS) sensor for the detection of trace elements in a variety

of matrices [1], we present results of a microchip-laser based

LIBS study of aluminum alloys. The microchip laser, which

possesses a number of properties which differentiate it from

the high power lasers typically used in such studies, offers

the potential for a far more compact and inexpensive LIBS-

based sensor than is currently available. This paper will

detail the benefits and disadvantages of using such a laser in

0584-8547/$ - see front matter D 2005 Elsevier B.V. All rights reserved.

doi:10.1016/j.sab.2005.03.020

i This paper was presented at the 3rd International Conference on Laser

Induced Plasma Spectroscopy and Applications (LIBS 2004), held in

Torremolinos (Malaga, Spain), 28 September – 1 October 2004, and is

published in the special issue of Spectrochimica Acta Part B, dedicated to

that conference.

* Corresponding author. Tel.: +1 978 663 9500.

E-mail address: af@aerodyne.com (A. Freedman).

conjunction with a miniature spectrometer which uses an

ungated, non-intensified CCD array detector.

The properties of microchip lasers have been described

in detail elsewhere [2–5]. In summary, these lasers deliver

low pulse energies (4–15 AJ) with very short pulse widths

(500 ps) at high repetition rates (1–10 kHz) compared to

the flashlamp-pumped Nd:YAG or excimer lasers typically

used for LIBS sensors. These latter two classes of lasers

produce 5–30 ns long pulses with pulse energies in the

tens to hundreds of millijoules range and repetition rates of

1–100 Hz. Microchip-LIBS thus differs in the details of

the ablation/plasma formation process which in turn affects

the spectral emission. The most important consequences of

the microchip laser-induced breakdown are that the

emission is virtually simultaneous with the excitation pulse

and that the broadband background signal is relatively

small compared to what is encountered with high power

lasers [6]. These emission properties allow one to use an

ungated detector without a severe degradation in the

60 (2005) 1076 – 1082

A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–1082 1077

quality of the signal with respect to signal-to-noise or

spectral discrimination.

We chose to study aluminum alloys in order to compare

our results with a number of previous studies which utilized

more conventional experimental arrangements [7–13]. The

most relevant, in terms of spirit, is the work by Cravetchi et

al. [12,13] in which laser pulse energies in the range of 0.4–

10 AJ were produced using a conventional Nd:YAG laser

(operated at its fourth harmonic at 266 nm) by attenuating

the beam intensity. After being focused by a microscope

objective, the 10 ns long pulse produced craters in an

aluminum alloy substrate with 5 to 10 Am diameters. Using

a gated, intensified array detector coupled to a 0.25 m focal

length spectrometer, this group measured trace element

concentrations of Mg, Cu, Fe and Mn in aluminum using

averages of 21 laser shots with relative standard deviations

(RSD) ranging from 5% to 18% for pure matrix regions.

They specifically note that inclusion of areas on the sample

with precipitates (which are marked by large increases in

minor element concentrations) in the data can increase the

apparent RSDs by a factor of 5 or 6. When higher power

laser pulses are employed (10–500 mJ), such as in Ref. [7],

RSD values can be reduced to ¨5% when averaged over 50

laser shots. Detection limits in Ref. [7] range from 0.5 ppm

(for Mg) to 14 ppm (for Si).

2. Experimental

The microchip laser used in these studies (Litton

Synoptics) produced 500 ps long, 14 AJ pulses at a

repetition rate of 7.8 kHz. As shown in the photograph in

Fig. 1, the laser package was bolted onto an aluminum

alignment fixture which also acted as a heat sink for the

laser. A compound lens system with a nominal 1.5 cm focal

length (CVI), which provides a 1 cm working distance

between lens and sample, was used to focus the laser onto

the sample. The calculated beam waist diameter is ¨20 Am,

yielding a laser fluence of 4.7 J cm�2 (9.4 GW cm�2). The

emission from the laser-induced plume (which is readily

Fig. 1. Photograph of LIBS apparatus.

visible to the naked eye in normal room light) is focused

onto a 600 Am diameter optical fiber (Ocean Optics) using a

single lens. The fiber is essentially focused at 1:1 ensuring

that the entire plume is imaged. The angle between the laser

and sample is kept slightly more off normal with respect to

the sample (30-) than the angle between the emission

focusing optics and surface normal (25-) to ensure that there

is no specular reflection of the laser into the spectrometer.

The spectrometer used in these studies was an f/4, 101

mm focal length instrument (HR-2000, Ocean Optics)

which utilizes a silicon CCD detector array (Sony) to record

the signal. An 1800 groove mm�1 grating in conjunction

with 25 Am wide slits provided spectral coverage over a

range of 275–413 nm with a nominal resolution of 0.2 nm.

The 2048-element CCD array is fitted with a cylindrical

collection lens to concentrate light from the 0.1 cm tall slit

onto the detector elements, and with a phosphor-coated

quartz window to enhance detection sensitivity in this

spectral range.

A total of 30 disks made from aluminum alloy reference

materials were used in these studies. Each disk was

resurfaced and carefully degreased before use, but not

polished. As a result of this treatment, the surface roughness

of these disks was noticeable and certainly large compared

to the size of the 20 Am diameter ablation crater created by

the laser. Furthermore, there is no independent measurement

of the variation in the surface elemental composition which

could have been affected by the resurfacing treatment. It is

quite possible that surface segregation effects caused by

sample heating during the resurfacing could lead to

substantial discrepancies between the surface and bulk

compositions. Reference samples (metal foils with impurity

levels below 0.01%) for the trace elements and pure

aluminum were obtained commercially (Alfa Aesar). The

sample to be measured was placed against the alignment

fixture while the laser was firing. No air breakdown could

be observed with the focused laser.

The data acquisition period for each sample totaled 2.5 s;

10 sets of spectra generated from observing the emission for

250 ms (¨2000 laser shots) were averaged to produce a

single spectrum. Each of the 30 reference disks was sampled

six times (yielding 180 spectra) using a random draw

approach. In order to maintain a LIBS spark during the data

acquisition period, each sample was manually kept in

motion while being pressed against the alignment fixture.

This phenomenon is generally observed for LIBS with

microchip lasers [6]. We have been able to establish that the

mechanism is not simply caused by ablation past the focal

plane. Even though the experimentally observed depth of

focus with respect to producing a spark for our system is on

the order of 1 mm, LIBS sparks on 0.015 mm thick

aluminum foil quickly die out without the appearance of

holes in the foil. It has been suggested that an alteration of

the heat transfer into the substrate, perhaps caused by the

formation of a liquid pool, lies behind the extinction of the

LIBS plasma above a stationary substrate [6].

400380360340320300280

Cu 4.24%Si 10.13

Cu

Si

Cu

400380360340320300280

Mg

Mg

Al

CuCu

Zn Zn

Mg

Cu 0.84%Mg 0.98Mn 0.48Zn 1.03

Mn

Alloy 7075

Wavelength (nm)

Alloy 4145

Wavelength (nm)

Fig. 3. LIBS spectra of two aluminum alloys.

A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–10821078

3. Results and discussion

In order to characterize the performance of the system

and establish a list of suitable spectral peaks for analysis,

LIBS spectra of seven trace elements (Fe, Cu, Mg, Mn, Ni,

Si, and Zn) and aluminum were obtained using pure metal

foils. Fig. 2 presents spectra of three of these trace

elements (Cu, Mg, Zn) as well as aluminum; the spectra

of the other three elements are comparatively more

congested and are not shown for clarity. Note that the

measured emission line widths are on the order of 0.5 nm

(FWHM). The aluminum spectrum alone exhibits a broad

spectral feature at approximately 358 nm which is not

observed in LIBS spectra of Al or Al alloys which have

been recorded using time-gated spectrometers. Given its

breadth and its suppression by time-gating, we tentatively

assign it to a set of unresolved emission lines from Al+

(3s3d-3s4f configurations) that involve both excited upper

and lower electronic states [14].

Raw spectra for two aluminum alloys, 4145 and 7075,

are shown in Fig. 3. The 4000 series aluminum alloys are

characterized by significant silicon concentrations; this

particular alloy contains some copper as well. The 7000

series alloys contain Cu, Mg and Zn. Note from the spectra

that Cu, Mg and Zn are relatively easy to distinguish at low

concentrations, whereas the Si peak at 288 nm is small

despite its high concentration in the 4145 alloy sample. This

is caused by a marked falloff in sensitivity at the lowest

wavelengths for this spectrometer, determined by compar-

ison to a second spectrometer with an overlapping spectral

range.

The analysis of the data is complicated by the use of an

ungated, non-intensified CCD detector array. First, the

signals, although well averaged, are frequently small in

magnitude, especially at low elemental concentrations.

Second, the recorded baseline values are a substantial

fraction of the intensities in the spectral peaks causing

Inte

nsi

ty (

arb

.)

400380360340320300280

Wavelength (nm)

Al Cu Zn Mg

328326324

0.45 nm (FWHM)

Fig. 2. LIBS emission spectra of pure Al, Cu, Zn and Mg samples. The inset

shows details of the Cu spectrum.

significant shot noise. And third, the spectral peaks are

somewhat broad, leading to possible analysis interference

effects from neighboring peaks. In order to assess the

detection sensitivity attainable with such spectra, two

techniques of data analysis were used. In the first, labeled

as peak integration (PI), a linear baseline was established for

each spectral feature used in the analysis and subtracted

from that feature. The resulting peak was then integrated

and divided by the integrated intensity from an aluminum

spectral feature, thereby forming a ratio which should be

less sensitive to the exact plasma conditions than the raw

intensity. Table 1 presents in tabular form all the elemental

emission lines used in this analysis process and their

relevant properties [15]. Those emission lines chosen were

the strongest ones from the neutral species of each element.

Note that the aluminum, iron, magnesium and manganese

features comprise unresolved spectral lines. The ratios are

then plotted against the known elemental composition of

each sample and fit with a linear (for Fe and Si) or

exponential function. This peak integration technique,

however, proved highly sensitive to both the presence of

baseline noise and interference between neighboring spec-

tral peaks; both effects tended to degrade the overall

performance (precision and accuracy) of the calibration

analysis.

400380360340320300280

400380360340320300280

Wavelength (nm)

Mn Reference Spectrum

PLS Vector

Ni Reference Spectrum

PLS Vector

Wavelength (nm)

Fig. 4. Comparison of the PLS regression vectors with measured reference

spectra for Mn (upper panel) and Ni (lower panel).

Table 1

Spectral properties of peaks used in PI analysis [15]

Element Wavelength

(nm)

A21

(108 s�1)

E1 (cm�1) g1 E2 (cm

�1) g2

Al 308.2153 0.6268 0.000 2 32,435.435 4

Al 309.2710 0.7552 112.061 4 32,436.778 6

Al 309.2839 0.1242 112.061 4 32,435.435 4

Cu 324.7537 1.370 0.000 2 30,783.686 4

Fe 373.3317 0.0620 888.129 3 27,666.346 3

Fe 373.4864 0.9014 6928.266 11 33,695.394 11

Fe 373.7131 0.141 415.932 7 27,166.819 9

Mg 382.9355 0.8902 21,850.405 1 47,957.058 3

Mg 383.2299 0.6666 21,870.464 3 47,957.058 3

Mg 383.2304 1.199 21,870.464 1 47,957.027 5

Mg 383.8292 1.594 21,911.178 5 47,957.045 7

Mg 383.8295 0.3996 21,911.178 5 47,957.027 5

Mn 403.0753 0.1738 0.000 6 24,802.250 8

Mn 403.3062 0.1646 0.000 6 24,788.050 6

Mn 403.4483 0.1582 0.000 6 24,779.320 4

Ni 352.4535 1.002 204.786 7 28,569.210 5

Si 288.1578 1.894 6298.850 5 40,991.884 3

Zn 334.5015 1.500 32,890.35 5 62,776.993 7

A21 is the Einstein A coefficient and E1 and E2 are the energies of the lower

and upper states of the emitting atom, where g1 and g2 are the multiplicities

associated with both states.

A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–1082 1079

In an attempt to remedy the aforementioned problems,

we also analyzed our data using partial least squares (PLS)

regression [16–18]. PLS regression is an analysis strategy

which optimally employs all the available spectral informa-

tion. For PLS regression, we first normalize spectra to unit

vector length in order to accommodate scale variation

caused by varying emission intensities. (The PI technique

employs spectral ratios using one particular aluminum

feature.) Performing univariate PLS regression against the

entire set of spectra to predict concentrations for a particular

alloy element yields a PLS regression vector, which when

multiplied, pixel-by-pixel, with a measured spectrum,

results (with proper accounting for mean removal) in a

predicted concentration of that element. The PLS regression

vector itself is formed from a linear combination of a

selectable number of latent vectors. Care must be used so as

to not Foverfit_ the data—that is, reproducing noise in the set

of training spectra. If the analysis is carefully done, the

regression vector for an element should be physically

reasonable—i.e., it should qualitatively reproduce that

element’s reference spectrum, with small variations in other

wavelength regions corresponding to minor coincidental

correlations or anti-correlations with element concentration.

If an element’s concentration happens to be correlated with

the concentration of some other element, its PLS regression

vector can include spectral features of the other element

(such as aluminum).

To gauge the optimal level of fitting (bias) for minimum

prediction error, we employed a 10-fold cross-validation

over the set of 180 spectra. The number of latent vectors

selected varied from 5 to 22 depending on the element

analyzed. The calculated regression vectors for Mn and Ni

are shown in Fig. 4. The agreement with the reference

spectra is remarkably good, especially in the case of Mn,

where no spectral features except for the major one at ¨404

nm could be discerned by visual inspection of the alloy

spectra.

Examples of the resulting analysis using both techniques

are shown in Figs. 5 and 6 for Cu, Mg and Zn. The two

figures differ slightly in format. The PI data in Fig. 5

represent the elemental ratios plotted versus known ele-

mental composition; the non-linearity in the plot is caused

by the presence of an optically dense plume at higher

concentrations of each of the elements shown. The PLS

analysis plot shown in Fig. 6, by definition, assumes a linear

fit between spectral intensities and concentration, and the

predicted concentration is plotted versus the known con-

centration. Note that there is a considerable reduction in

noise, especially near the origin, for the PLS analysis of the

data compared to the PI-analyzed data.

6543210

6543210

6543210

0

0

0

Cu

Mg

Zn

Weight %

Rat

io

Fig. 5. Measured ratio of integrated Cu, Mg and Zn emission peaks to that

of aluminum as a function of their concentrations in various alloy samples.

The fits to the data are exponential functions.

6

4

2

0

6420

6

4

2

0

6420

6

4

2

0

6420

Cu

Mg

Zn

Reference (Weight %)

Pre

dic

ted

(W

eig

ht

%)

Fig. 6. Predicted elemental compositions versus nominal sample concen-

trations for Cu, Mg and Zn using PLS analysis. The ideal fit is depicted as

the solid line with slope 1 and intercept 0.

A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–10821080

A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–1082 1081

Several quantitative measures of the PI and PLS

calibration curves are presented in Table 2. The RMS error

(in units of wt.%) is the root-mean-square average of the

difference between the data points representing all 180

samples and the curve fit through them, divided by the slope

of the curve at that point. The level of detection (LOD), also

in units of wt.%, is defined by 3r0 /S0, where r0and S0are

the standard deviation and slope at zero trace species

concentration. We estimate r0 /S0 by computing the stand-

ard deviation of the above-mentioned differences-divided-

by-slopes, using all concentrations below the LOD. (Only

one iteration, at most, is needed to obtain a self-consistent

value.) The relative standard deviation (RSD) is the RMS

average of ratios of the difference between the observed

points and fit values to the fit values calculated for the set of

points above the LOD. The RSD is a relative quantity. As

can be seen in Table 2, the lower variance near the origin of

the PLS calibration curves (as seen in Fig. 6) results in a

substantial reduction in LOD for all the elements except Cu

compared to the PI technique. As a consequence, the RMS

error in composition averaged over all the data points

(which is heavily weighted by the preponderance of data

near the origin) using the PLS analysis is also reduced

compared to PI, for all the elements except Cu and Fe. On

the other hand, the RSD, which is calculated using only the

points above the LOD, presents a different view of the

relative performance of PLS compared to PI analysis. Only

3 of the 7 elements show an improved RSD value using PLS

analysis; RSD values of three others are actually substan-

tially worse. This finding reflects the fact that the PLS

regression metric acts to reduce the RMS error and not RSD.

The range of RSD values measured in this study (12–

20% for PI analysis, 4–29% for PLS analysis) is similar to

the range of RSDs observed in other studies (2–10%)

[7,11,12]. But with 1 to 10 Hz laser repetition rates, the time

to acquire spectra in these other studies ranged from slightly

over 2 s, as in the present work, to more than 10 times

longer. On the other hand, all of these conventional LIBS

studies exhibit far lower limits of detection (¨1–20 ppm).

In order to understand the relatively poor fit to the Cu

data using PI analysis and the lack of improvement using the

Table 2

Results of data analysis (all values are in wt.%, except RSD values)

Element PI RMS

errora

(wt.%)

PLS RMS

errorb

(wt.%)

PI LODa

(wt.%)

PLS

LODb

(wt.%)

PI RSDa

(%)

PLS

RSDb

(%)

Cu 0.32 0.38 0.40 0.36 12 22

Fe 0.13 0.18 0.32 0.14 12 29

Mg 0.22 0.11 0.26 0.11 14 10

Mn 0.07 0.06 0.11 0.05 20 28

Ni 0.20 0.14 0.51 0.10 14 13

Si 0.66 0.16 1.87 0.14 12 4

Zn 0.52 0.14 1.36 0.10 14 7

a Calculated using the Peak Integration (PI) technique described in the

text.b Calculated using a Partial Least Squares (PLS) analysis.

PLS technique, we need to consider two issues mentioned

above: the presence of spectral overlap and optical thick-

ness. First, note that the internal measurement precision is

actually quite good over the entire measurement range for

Cu (T0.1 wt.%), showing that the problem is not one of

signal-to-noise limitations or experimental reproducibility.

In the case of the PI analysis, the source of the high RMS

error is the high variance at and near the origin resulting in a

high LOD. The fit at finite Cu concentrations is relatively

good, with RSD values in line with the values for the other

elements. Inspection of line listings [14,15] reveals a Zn line

(at 328.23 nm) immediately adjacent to one of the two main

Cu lines (at 327.40 nm) which, given the limited resolution

of the spectrometer (¨0.2 nm) and the comparatively broad

emission line widths encountered (¨0.5 nm), causes a

spectral overlap. Additional nearby Zn lines can also

contribute to the difficulty in baseline definition. If the Cu

samples are placed into two subgroups, one containing Zn

(above 0.1 wt.%) and the other not, the PI-based LOD of the

samples with Zn are a factor of 2 higher than those with no

Zn. This result is consistent with the presence of over-

lapping spectral features causing an interference.

The inability of the PLS analysis to provide an improved

fit to the Cu data illustrates the limitations of PLS regression

analysis. PLS, by definition, assumes a linear dependence

between emission intensity and concentration. However, for

a number of elements, emission intensity at high elemental

concentration shows an exponential dependence—i.e., the

plasma becomes optically thick. This situation occurs for

both Zn and Mg, where PLS analysis provides substantially

improved calibration curves. One explanation for this result

is that there are a number of emission lines within the

spectral range of the instrument that exhibit different line

strengths, providing flexibility within the PLS analysis.

However, for Cu, there are only two strong lines which also

have virtually identical spectral parameters. Thus, their

emission intensities are highly correlated. This lack of

flexibility might prevent the PLS analysis from providing a

fit which improves any of the error metrics for copper.

4. Conclusions

A comparatively simple and inexpensive LIBS detector

for the analysis of aluminum alloys has been designed, built

and tested. It employs a microchip laser and a miniature

spectrometer that uses an ungated, non-intensified CCD

detector array. A handheld commercial device using this

architecture could be designed and built. With the exception

of Cu, the important trace elements (Fe, Mg, Mn, Ni, Si, and

Zn) could be detected with limits of detection (3r) rangingfrom 0.05 to 0.14 wt.%; RMS error over the entire

concentration range varied from 0.06 to 0.18 wt.%. We note

that this level of performance is probably a factor of 2 or 3

poorer than what is required for proper alloy classification.

Improvements in both overall design and component

A. Freedman et al. / Spectrochimica Acta Part B 60 (2005) 1076–10821082

performance, such as wider spectral range and longer

integration time, should enable one to reach the desired

sensitivity and precision. It should be noted the limits of

detection observed in this study are two orders of magnitude

greater than those observed using conventional LIBS with

high power lasers and gated detector arrays.

It is still an open question as to whether the trace element

concentration measured using low fluence laser pulses is

truly representative of the bulk composition. For instance,

the use of a comparatively weak fluence laser leads to very

shallow sampling (¨10 Am depth) where the presence of a

native oxide could influence the results. The most recent

work of Cravetchi et al. [13] suggests that, under the right

circumstances, the laser plasma-induced shock wave can be

used to clean the surrounding area of its native oxide,

allowing for accurate surface composition determination.

However, their work also focuses on the presence of regions

of precipitates whose domain dimensions are on the order of

the focused laser beam. There is also the issue of sample

history which might affect the surface composition. Vir-

tually all LIBS-based measurements face issues similar to

the ones discussed here, but a LIBS sensor using a

microchip laser, with its small spark footprint and high

repetition rate, has the unique potential to provide spatially

resolved or averaged information at a high acquisition rate.

Acknowledgments

The authors acknowledge financial support from the

National Science Foundation from a Small Business Inno-

vation Research award (Grant No. 0216309). They also thank

Dr. Ben Smith of the Chemistry Department and Dr. David

Hahn of the Mechanical and Aerospace Engineering Depart-

ment at the University of Florida for many helpful dis-

cussions. The authors also thank Niton, Inc. of Billerica, MA

for providing the aluminum reference samples.

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