Multiple response optimization for Cu, Fe and Pb determination in

Spectrochimica Acta Part B 66 (2011) 338–344
Contents lists available at ScienceDirect
Spectrochimica Acta Part B
j o u r n a l h o m e p a g e : w w w. e l s ev i e r. c o m / l o c a t e / s a b
Multiple response optimization for Cu, Fe and Pb determination in naphtha by graphite
furnace atomic absorption spectrometry with sample injection as detergent emulsion
Daniel M. Brum b, Claudio F. Lima b, Nicolle F. Robaina a, Teresa Cristina O. Fonseca c, Ricardo J. Cassella a,⁎
a
b
c
Departamento de Química Analítica, Universidade Federal Fluminense, Outeiro de S.J. Batista s/n, Centro, Niterói/RJ, 24020-141, Brazil
Departamento de Química, Universidade Federal de Viçosa, A. Peter Henry Rolfs s/n, Viçosa/MG, 36570-000, Brazil
Petrobras, Cenpes/PDEDS/QM, Av. Horácio Macedo 950, Ilha do Fundão, Rio de Janeiro/RJ, 21941-915, Brazil
a r t i c l e
i n f o
Article history:
Received 5 November 2010
Accepted 2 February 2011
Available online 4 March 2011
Keywords:
Naphtha
Metals
GF AAS
Detergent emulsions
a b s t r a c t
The present paper reports the optimization for Cu, Fe and Pb determination in naphtha by graphite furnace
atomic absorption spectrometry (GF AAS) employing a strategy based on the injection of the samples as
detergent emulsions. The method was optimized in relation to the experimental conditions for the emulsion
formation and taking into account that the three analytes (Cu, Fe and Pb) should be measured in the same
emulsion. The optimization was performed in a multivariate way by employing a three-variable Doehlert
design and a multiple response strategy. For this purpose, the individual responses of the three analytes were
combined, yielding a global response that was employed as a dependent variable. The three factors related to
the optimization process were: the concentration of HNO3, the concentration of the emulsifier agent (Triton
X-100 or Triton X-114) in aqueous solution used to emulsify the sample and the volume of solution. At
optimum conditions, it was possible to obtain satisfactory results with an emulsion formed by mixing 4 mL of
the samples with 1 mL of a 4.7% w/v Triton X-100 solution prepared in 10% v/v HNO3 medium. The resulting
emulsion was stable for 250 min, at least, and provided enough sensitivity to determine the three analytes in
the five samples tested. A recovery test was performed to evaluate the accuracy of the optimized procedure
and recovery rates, in the range of 88–105%; 94–118% and 95–120%, were verified for Cu, Fe and Pb,
respectively.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Naphtha is a colorless liquid that comprises a mixture of paraffinic,
olefinic, naphthenic and aromatic hydrocarbons with 5 to 15 carbon
atoms. The petrochemical naphtha can be obtained from direct distillation
of petroleum or from catalytic cracking of heavier fractions. It is a very
important feedstock for the fine chemical industries, being employed in
gasoline formulation and also in the production of ethylene, propene and
some solvents like benzene, toluene and xylenes [1,2].
Nowadays, it is well known that the stability of organic liquids
obtained from petroleum (as petrochemical naphtha) is closely related to
the concentration of trace metals. The presence of metals in these liquids,
even in very low concentrations, can accelerate the oxidation reactions of
several organic substances, especially certain heteroatomic compounds
containing nitrogen and/or sulfur [3]. Another problem derived from the
presence of metals in naphtha is the catalyst poisoning that occurs during
the catalytic cracking of heavier petroleum fractions or in the processes
applied for reducing sulfur and olefins in the gasoline [2]. Therefore, the
development of analytical methodologies for the determination of trace
⁎ Corresponding author. Tel.: +55 21 2629 2222; fax: +55 21 2629 2143.
E-mail address: [email protected] (R.J. Cassella).
0584-8547/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.sab.2011.02.001
elements in petrochemical naphtha is required for the quality control of
this important chemical product.
One of the most important analytical techniques used for the
determination of trace elements in fuels and petroleum-derived products
is the graphite furnace atomic absorption spectrometry (GF AAS) because
of its intrinsic sensitivity and selectivity. Moreover, the GF AAS allows the
injection of the samples as emulsions [4–7] and microemulsions [8–10],
permitting the development of analytical procedures with minimum
treatment of the samples before analysis. Only few papers in the current
literature report the determination of trace metals in naphtha by GF AAS
employing the direct injection of the samples [11,12] or the injection of
the samples as detergent emulsions [4–7]. The emulsification of naphtha
with detergent has also been applied in the determination of metals by
ETV-ICP-MS (electrothermal vaporization coupled to inductively coupled
plasma mass spectrometry) [13]. In most of these works, multiple analytes
are determined in the same emulsion, although the variation of sensitivity
for each analyte due to emulsion conditions is not taken into account. As
result, the sensitivity for the determination of some analytes could be
sacrificed, degrading the analytical capacity of the method. This problem
could be easily solved by applying a multivariate optimization strategy,
considering the response of each element of interest and combining them
into a proper overall response. The behavior of the overall response can
indicate the optimum experimental region for the analytical procedure
D.M. Brum et al. / Spectrochimica Acta Part B 66 (2011) 338–344
considering a compromise among the responses for the analytes under
evaluation.
Nowadays, the multivariate optimization strategies are very popular in
the development of analytical methodologies. The main advantage of their
use is the low number of experiments required to achieve the optimum
conditions and the indication of possible influences of some variables on
others, which is not possible in the univariate optimization. The response
surface methodology (RSM) can be considered one of the most important
approaches for the multivariate optimization of several analytical
procedures [14–16]. The use of the Doehlert matrix as a model for the
multivariate optimization of analytical procedures has grown in the last
few years, basically due to its higher efficiency when compared to other
second-order designs like Box-Behnken and Central Composite designs
[17–20]. Nevertheless, the multivariate optimization of GF AAS methods is
not very used [12,21,22], especially when multiple analytes are
determined.
The main goal of this present work was to perform a multivariate
optimization of a procedure for Cu, Fe and Pb determination in
petrochemical naphtha by GF AAS, employing the injection of the
samples as detergent emulsions (prepared with Triton X-100 or Triton
X-114). The variables associated to the preparation of the emulsions
were selected for the optimization process, once they showed
influence on the sensitivity obtained for the elements under study.
In order to achieve a final condition that could represent a
compromise among the conditions for each analyte, a global response
(derived from the multiple individual responses) was employed
during the optimization as dependent variable.
2. Experimental
2.1. Apparatus
The GF AAS measurements of the metals were performed with a
Varian AA240Z (Mulgrave, Australia) electrothermal atomic absorption
spectrometer equipped with a Varian GTA 120 graphite furnace and a
Zeeman-effect background corrector. The injection of the samples was
performed by a Varian PSD 120 auto sampler and codified hollow cathode
lamps for copper, iron and lead were employed in all experiments.
Integrated absorbance measurements were performed by using partitioned graphite tubes (coated with pyrolytic graphite), also supplied by
Varian (part no. 63-100012-00). Argon (99.99% purity, supplied by Linde
Gases, Macaé, Brazil) was used as protective gas. Operational conditions
used for the measurements of all elements under study are shown in
Table 1.
2.2. Reagents and solutions
Deionized water obtained from a Direct-Q 3 System (Millipore,
Milford, MA, USA) was employed in the preparation of the aqueous
solution utilized for sample emulsification. Trace metal grade nitric
acid (Tedia, São Paulo, Brazil) and analytical grade Triton X-100
(Tedia, São Paulo, Brazil) and Triton X-114 (Acros organics, St. Louis,
USA) were used in the experiments.
Oil-based stock solutions of copper, iron and lead with 1,000 μg mL−1
concentration were supplied by Conostan (Houston, TX, USA). Standard
solutions of Cu, Fe and Pb were prepared by diluting the respective oiled
solutions in hexane of HPLC grade (Tedia, São Paulo, Brazil).
Table 1
Operational conditions employed in the measurement of copper, iron and lead in the
detergent emulsions by GF AAS.
Parameter
Copper
Iron
Lead
Wavelength
Spectral band pass
Lamp current
324.8 nm
0.5 nm
4 mA
248.3 nm
0.2 nm
5 mA
283.3 nm
0.5 nm
5 mA
339
Stock solutions of Triton X-100 and Triton X-114 with 25% v/v
concentration were prepared by dissolving 25 g of each surfactant,
separately, in approximately 80 mL of purified water. After the total
dissolution of the reagent, the obtained mixture for each surfactant was
transferred to individual 100 mL volumetric flask and the volume was
completed to the mark with purified water. The solutions used for the
emulsification of the samples (or standards solutions) were prepared by
diluting Triton X-100 or Triton X-114 stock solutions in HNO3 solutions
with known concentrations. The concentration of each component
(surfactant and HNO3) in the solutions was established according to the
design utilized for multivariate optimization.
Naphtha from distillation unit, free of metals, was supplied by
PETROBRAS and was employed during the multivariate optimization of
the methodology for the preparation of the standard emulsions. Also, it
was employed in the quantification experiments for the preparation of Cu,
Fe and Pb standard solutions used for the construction of the analytical
curves.
2.3. Emulsion preparation
The multivariate optimization was carried out with emulsions
prepared by mixing, in a plastic capped tube of 6 mL capacity, 4 mL of
standard naphtha (free of metals) spiked with known concentrations of
Cu, Fe and Pb with a defined volume of Triton X-100/Triton X-114
solutions prepared in HNO3 medium. The concentrations of the
surfactants and HNO3, as well as the volume of aqueous solution used
for the emulsification were in accordance with the Doehlert design drawn
for the multivariate optimization. The mixture was vigorously shaken
until the emulsion formation. After that, the emulsion was directly
transferred to the auto sampler cup and injected into the graphite tube. All
the elements under study (Cu, Fe and Pb) were measured at the same
emulsion.
The preparation of real samples for analysis was carried out by mixing
4 mL of the petrochemical naphtha sample with 1 mL of a 4.7% w/v Triton
X-100 solution prepared in 10% v/v HNO3 medium, which corresponds to
the experimental conditions optimized in this present work. For the
quantification of Cu, Fe and Pb in the samples, the calibration was
performed with naphtha standard emulsions that were prepared like the
real samples were prepared, but employing the straight run naphtha free
of metals and oil-based standard solutions of the analytes.
2.4. General procedure for the determination of Cu, Fe and Pb by GF AAS
For the measurement of the Cu and Fe signals, 20 μL of sample or
standard emulsion was injected into the partitioned graphite tube
(pyrolytic coated) and the temperature program was run. The use of
chemical modifiers was not necessary, since the observed background
was very low at the temperatures used for pyrolysis, without losing
sensitivity. On the other hand, for the measurement of Pb, 20 μL of sample
or standard emulsion was added into the graphite tube followed by
addition of 10 μL of 1000 μg L−1 Pd modifier solution. The addition of the
chemical modifier was necessary due to the high volatility of the Pb, which
did not allow the use of a convenient pyrolysis temperature. The
temperature programs used for each analyte are shown in Table 2.
2.5. Multivariate optimization strategy
The multivariate optimization of the methodology was carried out
considering only three variables, all related to the emulsion preparation:
surfactant and nitric acid concentrations in the solution used for the
emulsification; and the volume of acidic surfactant solution employed for
emulsification. In all experiments, the emulsions were prepared as related
in the Section 2.3.
Two models were constructed for the evaluation of the system. One
using Triton X-100 as emulsifier agent and the other one using Triton
X-114 for the same purpose. A global response (GR) was used in both
D.M. Brum et al. / Spectrochimica Acta Part B 66 (2011) 338–344
Table 2
Temperature programs employed in the measurements of Cu, Fe and Pb in the
detergent emulsions by GF AAS.
Step
T
(°C)
Ramp
(s)
Hold
(s)
Ar flow rate
(mL min−1)
Drying
50
250
1000 (Cu)
1000 (Fe)
800 (Pb)
2300 (Cu)
2400 (Fe)
2100 (Pb)
2400 (Cu)
2500 (Fe)
2200 (Pb)
5
50
5
0
10
3
300
300
300
1
2
0
2
1
300
Pyrolysis
Atomization
Cleaning
cases [23–25] in order to establish a condition that could satisfy an
optimized condition for all the analytes under study in terms of sensitivity.
The global response employed in this present work is shown below:
GR =
p
ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi
3
RCu :RFe :RPb
where GR is the global response (or combined response) and Ri is the
individual response for each analyte under evaluation.
The GR function combines the individual responses for Cu, Fe and Pb in
a final response, which was used in the optimization. The individual
response for each analyte was derived from the combination of the
individual absorbance, measured in the injection of the emulsions, with
the relative sensitivity and the actual concentration of each analyte in the
emulsion. The individual responses can be represented by the following
relation:
Ri =
Ai
Si :Ci
where Ri is the individual response for each analyte, Ai is the
individual absorbance measured in each experiment, Si is the relative
sensitivity for each analyte and Ci is the real concentration of each
element in the emulsions used in the experiments.
3. Results and discussion
The preparation of the naphtha emulsion is an important step in its
analysis by GF AAS. The literature reports that unstable signals are
obtained in the direct naphtha measurements by GF AAS due to the high
volatility of the sample [5,7], being necessary the stabilization of the
sample in the form of emulsion or microemulsion. However, even when
this procedure is used, some drawbacks can be noted. One of these
drawbacks is observed when multiple analytes are determined in the
sample. In this case, the utilization of only one condition (in terms of
emulsion preparation) can lead to a suitable determination of some
analytes and the inefficient determination of others. So, it is important to
set a condition that represents a compromised situation for the analytes
under study. In these cases, the optimization based on multiple responses
can be very efficient in the establishment of such condition [24].
3.1. Multivariate optimization based on multiple responses
denoted in the Section 2.5. The global desirability function was used in
order to achieve an experimental condition that could make possible the
determination of the three selected analytes (Cu, Fe and Pb) in the
naphtha with maximum sensitivity. All experiments for optimization
were carried out using the straight run naphtha spiked with known
concentrations of the analytes under study.
When three variables are considered in the optimization process
using the Doehlert design, a spatial figure called cuboctahedron is
generated as result of the distribution of the variables over the
experimental region. The cuboctahedron is a solid with eight vertices
symmetrically truncated producing eight equilateral triangles whose
edges are equal to those of the remaining squares [26]. Fig. 1 shows
the plane projection of the design employed in this work with the real
levels of the variables. As mentioned previously, the experimental
variables selected for the optimization were: the concentrations of
surfactant (Triton X-100 or Triton X-114, depending on the design)
and HNO3 in such solution; and the volume of aqueous solution
employed in the emulsification process. The levels of the variables
were set according to the geometry of the design and the possible
range of application of each variable, resulting in a design with three
levels for the volume of aqueous solution (0.5 to 1.5 mL), five levels
for the surfactant concentration (1 to 9% w/v) and seven levels for the
HNO3 concentration (1 to 19% v/v). Each model was constructed after
performing a total of 15 experiments, which is the sum of the 13 regular
experiments drawn in the Doehlert design with 2 more experiments in
the central point of the design, employed to estimate the experimental
variance. Tables 3 and 4 show the experiments performed when Triton
X-100 and Triton X-114 were employed for emulsification, respectively,
and the global responses obtained in each experiment. The experiments
were executed randomly to avoid any tendency in the measurements.
A preliminary analysis of the results showed that the absorbance
signals of Cu and Pb had a different behavior when compared to the
absorbance signals of Fe. While the maximum absorbance signal for Fe
was verified in the experiments 2 and 4, using Triton X-100 as
emulsifier agent, the maximum absorbance for Cu and Pb, in the same
set of experiments, was registered in the central region of the
experimental domain, indicating the need of optimizing the system
through the use of a global response. The same phenomenon was not
observed when Triton X-114 was employed in the emulsification. This
probably occurred due to the lower variation of the absorbance
signals, for the three analytes, in the set of experiments performed
when the Triton X-114 was employed.
After the preliminary analysis, the obtained data were modeled
using the Statistica for Windows (version 6) software operated in the
experimental design module. The variables were coded in order to
avoid that different magnitudes could affect the calculated effects on
1
2
19.0
HNO3 concentration (% v/v)
340
3
16.0
4
5
13.0
6
8
7
10.0
9
10
7.0
4.0
11
1.0
In this present work, three variables were selected to draw two
Doehlert designs, which were used for the evaluation and for the
optimization of the variables related to the emulsion preparation. The first
design was drawn using Triton X-100 as emulsifying agent and the other
one was drawn using Triton X-114. In both cases, the response was the
global desirability function (D), calculated for each experiment exactly as
13
12
1.0
3.0
5.0
7.0
9.0
TX-100 or TX-114 concentration (%w/v)
Fig. 1. Planar projection of the design used in this work for three variables optimization
(black circles, 0.5 mL; gray circles, 1.0 mL, and white circles, 1.5 mL).
D.M. Brum et al. / Spectrochimica Acta Part B 66 (2011) 338–344
Table 3
Results obtained in the experiments of the Doehlert design when Triton X-100 was
used as detergent for emulsification.
Exp TX-100*
(% w/v)
HNO3*
(% v/v)
1
2
3
4
5
6
7a
7b
7c
8
9
10
11
12
13
19 (0.866)
1.0 (0)
19 (0.866)
1.0 (0)
16 (0.577)
0.5 (−0.817)
13 (0.289)
1.5 (0.817)
13 (0.289)
1.5 (0.817)
10 (0)
1.0 (0)
10 (0)
1.0 (0)
10 (0)
1.0 (0)
10 (0)
1.0 (0)
10 (0)
1.0 (0)
7 (−0.289) 0.5 (−0.817)
7 (−0.289) 0.5 (−0.817)
4 (−0.577) 1.5 (0.817)
1 (−0.866) 1.0 (0)
1 (−0.866) 1.0 (0)
3.0 (− 0.5)
7.0 (0.5)
5.0 (0)
3.0 (−0.5)
7.0 (0.5)
1.0 (−1)
5.0 (0)
5.0 (0)
5.0 (0)
9.0 (1)
3.0 (− 0.5)
7.0 (0.5)
5.0 (0)
3.0 (0.289)
7.0 (0.5)
Vol.*
(mL)
Abs
Abs
Abs
Cu
Fe
Pb
0.2201
0.2034
0.2061
0.2493
0.1741
0.2140
0.3011
0.2772
0.2902
0.2429
0.2568
0.2198
0.1850
0.1943
0.2232
0.4189
0.4912
0.3183
0.4879
0.4479
0.4619
0.3789
0.3601
0.3773
0.3026
0.3139
0.3104
0.2342
0.2487
0.3233
0.0536
0.0514
0.0683
0.0673
0.1279
0.0913
0.1630
0.1495
0.1524
0.0605
0.0743
0.0713
0.1011
0.0814
0.0521
GR
0.8678
0.8789
0.8399
1.0267
1.0966
1.0607
1.3497
1.2543
1.3018
0.8378
0.9252
0.8632
0.8336
0.8042
0.7922
* The values between parentheses represent the coded value of the variables.
the response. The codification was done using the following
relationship [27]:
Ci =
!
ðXi Xi0 Þ
α
ΔXi
where Ci is the coded value of the variable, X0i is the real value of the
variable at the centre of the experimental domain, ΔXi is the change in
the real value and α is the coded value limit for each factor.
The experimental responses obtained in each experiment (Tables 3
and 4) were used in the computation of the models. The nature of the
surfaces was verified by application of the Lagrange's criteria [28],
which indicated that the surfaces obtained for each surfactant
presented a point of maximum. In the optimization process, the
coordinates of the point of maximum of the surface represent the
experimental condition where better sensitivity for the simultaneous
determination of Cu, Fe and Pb can be achieved.
Before determining the critical points of the surfaces, the obtained
models were tested in order to evaluate their statistical significance and
predictive capacity. The application of the ANOVA indicated that the
models did not suffer lack of fit, thus providing a good theoretical
description of the experimental data. This affirmative was corroborated by
the value of the determination coefficient (r2) derived when the
341
experimental and predicted data were compared. Linear fits with r2 of
0.961 and 0.973 were obtained for the experiments carried out when
Triton X-100 and Triton X-114 were employed as emulsifier agents,
respectively. The evaluation of the most significant variables was
performed by employing the Pareto chart of effects (Fig. 2). For the
model constructed with Triton X-100 as emulsifying agent (Fig. 2A), the
three variables under evaluation affected significantly the response of
the system, although no important interaction among them was verified.
The same behavior was verified when Triton X-114 was employed in the
emulsification (Fig. 2B).
3.2. Determination of the critical points
The coordinates of the critical point ([S]c, [HNO3]c and [V]c) were
calculated by solving the linear equation system, obtained after
deriving the response function in terms of each variable considered in
the model [28], as follows:
δGR = δ½S = 0; δGR = δ½HNO3 = 0 and δGR = δ½V = 0
where GR represents the global response (as defined in the Section 2.3),
and [S], [HNO3] and [V] are the concentration of surfactant (Triton X-114
or Triton X-100), the concentration of HNO3 and the volume of aqueous
solution employed for emulsification, respectively.
From the application of such derivates, two systems with three
variables and three linear equations were obtained. The resolution of the
systems was performed to find the critical conditions for each surfactant
by simple application of the least-squares methodology.
As mentioned previously, the values of the variables found for the
critical point are the optimum values for each variable, once they
represent the point of the function where the global response is
maximized. In fact, this point must represent the best experimental
condition for the detergent emulsion formation aiming the Cu, Fe and Pb
determination in the naphtha samples, taking into account the individual
characteristics of the three metals. In the experiments using Triton X-100,
Table 4
Results obtained in the experiments of the Doehlert design when Triton X-114 was
used as detergent for emulsification.
Exp
1
2
3
4
5
6
7a
7b
7c
8
9
10
11
12
13
TX-114*
(% w/v)
HNO3*
(% v/v)
Vol.*
(mL)
3.0 (−0.5)
7.0 (0.5)
5.0 (0)
3.0 (−0.5)
7.0 (0.5)
1.0 (−1)
5.0 (0)
5.0 (0)
5.0 (0)
9.0 (1)
3.0 (−0.5)
7.0 (0.5)
5.0 (0)
3.0 (0.289)
7.0 (0.5)
19 (0.866)
19 (0.866)
16 (0.577)
13 (0.289)
13 (0.289)
10 (0)
10 (0)
10 (0)
10 (0)
10 (0)
7 (−0.289)
7 (−0.289)
4 (−0.577)
1 (−0.866)
1 (−0.866)
1.0 (0)
1.0 (0)
0.5 (−0.817)
1.5 (0.817)
1.5 (0.817)
1.0 (0)
1.0 (0)
1.0 (0)
1.0 (0)
1.0 (0)
0.5 (−0.817)
0.5 (−0.817)
1.5 (0.817)
1.0 (0)
1.0 (0)
Abs
Abs
Abs
Cu
Fe
Pb
GR
0.3757
0.3632
0.3439
0.3692
0.3580
0.3706
0.3873
0.3820
0.3853
0.3636
0.3812
0.3642
0.3649
0.3649
0.3790
0.3554
0.3584
0.3530
0.3297
0.3536
0.3328
0.3657
0.3631
0.3637
0.3588
0.3443
0.3394
0.3463
0.3481
0.3580
0.1295 1.3173
0.1021 1.2066
0.1082 1.2020
0.1139 1.2239
0.1059 1.2101
0.1214 1.2557
0.1368 1.3683
0.1366 1.3582
0.1315 1.3456
0.1036 1.2135
0.1208 1.2797
0.1031 1.1901
0.1146 1.2417
0.1343 1.3113
0.1246 1.3073
* The values between parentheses represent the coded value of the variables.
Fig. 2. Pareto chart of effects for the models generated in the present work using
(A) Triton X-100 and (B) Triton X-114 as emulsifier agents.
342
D.M. Brum et al. / Spectrochimica Acta Part B 66 (2011) 338–344
the values of the variables in the critical point were equal to 4.7% w/v,
10% v/v and 1 mL for the concentration of surfactant, concentration of
HNO3 and volume of solution, respectively. For Triton X-114, the optimum
conditions were 4.6% w/v, 7.8% v/v and 1 mL for the concentrations of
surfactant, concentration of HNO3 and volume of aqueous phase,
respectively. As it can be seen from the derived data of the critical points,
no remarkable distance between the optimum regions was verified when
Triton X-100 or Triton X-114 was utilized as emulsifying agents. Also, the
magnitudes of the global responses obtained with each surfactant were
not so different. These observations could indicate that the surfactants did
not affect significantly the measurement process and that they only
participated in the dispersion of the water phase in the naphtha phase.
As no remarkable differences were observed when Triton X-100 or
Triton X-114 was employed for emulsification, Triton X-100 was chosen
as emulsifying agent for the determination of the selected metals by the
optimized procedure, due to its lower cost and higher availability in the
most chemical laboratories. Also, it was considered that the emulsions
were more easily formed when Triton X-100 was used as disperser agent.
3.3. Curves of pyrolysis and atomization in the emulsion
Before testing the emulsion stability, curves of pyrolysis and
atomization were established for the analytes under study (as emulsions)
in the optimized conditions. The experiments were performed with
emulsions prepared with naphtha free of metals spiked with 25 μg L−1 of
each analyte in a form of organic standards.
As it can be seen in Fig. 3, there was no variation in the analytical
signal by changing the pyrolysis temperature from 200 to 1000 °C for
Cu. However, a continuous decrease of the analytical signal was noted
when higher temperatures were set, indicating the volatilization of Cu
during pyrolysis. A similar behavior in the emulsion medium was
noted for Fe, but it was stable in pyrolysis temperatures lower than
1200 °C. Once again, a continuous decrease of the signal was observed
in temperatures higher than 1200 °C. For both metals, no modifier was
added and the background signals were always efficiently corrected by
the corrector device of the instrument. The pyrolysis temperature for both
Cu and Fe was set at 1000 °C.
For Pb, the behavior was quite different. When the modifier was not
added, the analytical signal decreased when pyrolysis temperatures
higher than 400 °C were used. Also, lower sensitivity was observed,
evidencing that even at low temperatures, as 200 °C, a little fraction of the
element was lost by volatilization. The addition of 10 μg of Pd, as chemical
modifier, solved this problem, making possible the increase of the
pyrolysis temperature to 800 °C, without losing sensitivity. This pyrolysis
temperature was then set for the temperature program established for Pb
measurements.
Atomization temperatures were tested in the range of 1500–2400 °C
and good analytical signals (in terms of magnitude and shape) could be
verified for Cu, Fe and Pb in atomization temperatures of 2300, 2400 and
2100 °C, respectively. So, these temperatures were set for the
measurements.
3.4. Evaluation of the emulsion stability
The main problem in direct measurement of trace elements in
naphtha is related to the high volatility of the samples, which yields
unstable absorbance signal for the standard solutions prepared with
naphtha free of metals (or even hexane) and commercial organometallic standards. Also, the high volatility of the sample is inconvenient
because the vials must be filled continuously during analysis,
requiring constant supervision of the operator. These problems
were already reported in the literature and can be considered the
most important drawbacks in the trace metals determination in this
kind of sample. In order to solve this problem, the injection of the
sample, as detergent emulsions, has been chosen as sample
preparation strategy, since the current literature reports that good
stability of volatile organic liquids can be attained with the emulsion
or microemulsion formation. So, an experiment was run to evaluate
the emulsion stability in the optimized conditions. The experiment
comprised a continuous measurement of Cu, Fe and Pb in the
emulsion in intervals of 10 min during 250 min. The experiments
were performed with an emulsion prepared with naphtha free of
metals spiked with 25 μg L−1 of each analyte in the form of organic
standard. The results obtained in this experiment are shown in Fig. 4
and no noticeable variation in the analytical signals for any of the
studied metals was verified, indicating that the detergent emulsion
optimization was a correct strategy for the establishment of the
experimental conditions for the naphtha stabilization aiming metals
determination by GF AAS.
3.5. Analytical results
In order to derive the equations of the analytical curves and the limits
of detection and quantification of the optimized methodology, analytical
curves were constructed, for each analyte, in a concentration range of
2.00
Cu without modifier
Fe without modifier
Pb with modifier
Pb without modifier
0.16
0.12
0.08
Cu
Fe
Pb
1.75
1.50
Relative signal
Integrated absorbance (s)
0.20
1.25
1.00
0.75
0.50
0.04
0.25
0.00
0.00
0
200
400
600
800
1000
1200
1400
1600
1800
50
100
150
200
250
300
Time (min)
Pyrolysis temperature (°C)
Fig. 3. Pyrolysis curves for Cu, Fe and Pb in the emulsion prepared according to the
optimized conditions. The concentration of the analytes in the emulsion was 25 μg L−1.
In the case of Pb, 10 μg of Pd was added as chemical modifier.
Fig. 4. Stability of the emulsion prepared after optimization of the experimental conditions for
the measurement of Cu, Fe and Pb. Emulsion preparation was performed by mixing 4 mL of
spiked naphtha with 1 mL of a 4.7% w/v Triton X-100 solution prepared in 10% v/v HNO3. Error
bars represent the standard deviation of three measurements.
D.M. Brum et al. / Spectrochimica Acta Part B 66 (2011) 338–344
343
Table 5
Analytical characteristics of the methodologies optimized for the determination of Cu, Fe and Pb in naphtha emulsified samples by GF AAS.
Cu
Analytical curves
a
2
R
Characteristic mass (pg)
LOD (μg L− 1)b
LOQ (μg L− 1)b
a
b
Fe
Pb
Oil-based standard
Aqueous standard
Oil-based standard
Aqueous standard
Oil-based standard
Aqueous standard
A = 0.0076 [Cu]
+0.0051
0.9992
12
0.10
0.34
A = 0.0075 [Cu]
+0.0074
0.9989
12
0.11
0.38
A = 0.0035 [Fe]
-0.0017
0.9952
25
1.0
3.3
A = 0.0034 [Fe]
+ 0.0045
0.9942
26
0.95
3.2
A = 0.0029 [Pb]
+ 0.0020
0.9975
30
0.88
2.4
A = 0.0029 [Pb]
+ 0.0033
0.9958
30
0.56
1.9
A is the integrated absorbance (s) and [Cu], [Fe] and [Pb] are concentrations of Cu, Fe and Pb in μg L−1, respectively.
Both LOD and LOQ were calculated taking into account the sample dilution due to emulsification.
10–40 μg L−1, employing standard emulsions prepared from naphtha free
of metals. Two distinct approaches were tested for the establishment of
analytical curves: (i) addition of metals as oil-based organic standards;
and (ii) addition of metals as inorganic standards dissolved in water. In
both cases, the metals are easily incorporated to the emulsions since they
are formed by mixing aqueous and organic phases. According to Table 5,
there were no differences between the sensitivities observed when
organic or inorganic standards were added to the emulsions, for the three
metals. This phenomenon was already observed in the determination of
Cu and Fe in jet fuel [29] and should be related to the extraction of the
metals from organometallic molecules and/or organic complexes by the
action of the HNO3 added to the emulsions.
The analytical characteristics for the methodologies employing the
two approaches are shown in Table 5. The limits of detection (3 s
criterion) and quantification (10 s criterion) were estimated from the
measurements of standard naphtha detergent emulsions without Cu,
Fe or Pb and the precision was estimated from ten measurements of
standard emulsions containing 10 μg L−1 of each analyte.
In order to evaluate the accuracy of the optimized methodology for
Cu, Fe and Pb determination in naphtha, five real samples spiked with
organic standard of each analyte (within 9.0 and 19.3 μg L−1) were
analyzed using the procedure developed in this work. It is important
to remark that certified reference materials are not available for the
determination of metals in naphtha. All determinations were carried
out in triplicate, and the obtained results are listed in Table 6. As it can
be seen, recovery percentages ranging from 88 to 120% were verified
in the determination of Cu, Fe and Pb in spiked naphtha samples,
attesting the accuracy of the developed procedure.
4. Conclusions
The optimization strategy employed in this present work showed
to be very suitable for the optimization of the experimental variables
associated to the determination of Cu, Fe and Pb in naphtha samples
by GF AAS, using an approach based on the injection of the samples as
detergent emulsions. The employment of a global response (obtained
by combining the individual responses) made possible the establishment of optimum conditions for the preparation of only one emulsion
that could yield adequate sensitivity for the determination of the
three analytes.
As only three variables (concentration of surfactant, concentration
of nitric acid and volume of the aqueous phase) were chosen for the
optimization, a Doehlert design was directly employed for this
purpose. Two designs were drawn, one using Triton X-100 as
emulsifying agent and other one using Triton X-114 as emulsifying
agent. All the three variables under study presented significant effect
on the global response and important interactions among them was
only verified when Triton X-114 was used. The performance of the
method was not significantly different by employing either one of the
surfactants, once the global responses for the two cases showed
similar magnitudes. The experimental regions where the critical
points were situated were close to each other, indicating that the use
of Triton X-100 or Triton X-114 made possible the formation of
suitable detergent emulsions for GF AAS determination of the
considered metals. Triton X-100 was chosen in this present work
because of the high availability of this reagent in the most analytical
laboratories and also due to the easier formation of the emulsions
when it is employed. The stability of the emulsion prepared in the
optimized conditions was also tested and no significant variation of
the analytical signal measured for the three analytes was observed in
an interval of 250 min.
The optimized method was successfully applied in the determination of Cu, Fe and Pb in five samples of naphtha. A recovery test was
performed to evaluate the accuracy of the procedure and recovery
rates in the range of 88–105%, 94–118% and 95–120% for Cu, Fe and Pb
were verified, respectively.
Table 6
Results obtained in the analysis of real naphtha samples and in the recovery test. All values are expressed as the mean ± standard deviation of three independent determinations.
Sample
Cu added
(μg L− 1)
Cu found
(μg L−1)
I
0
9.8
19.3
0
9.8
19.3
0
9.8
19.3
0
9.8
19.3
0
9.8
19.3
2.7 ± 0.1
12.2 ± 0.8
21.9 ± 0.7
2.0 ± 0.1
10.6 ± 1.2
21.4 ± 1.5
1.3 ± 0.1
10.9 ± 0.5
21.6 ± 0.3
2.5 ± 0.1
11.7 ± 0.2
21.0 ± 0.6
1.7 ± 0.1
10.4 ± 0.8
20.7 ± 1.4
II
III
IV
V
Cu recovery
(%)
97
99
88
101
98
105
93
98
88
98
Fe added
(μg L− 1)
Fe found
(μg L− 1)
0
9.3
18.3
0
9.3
18.3
0
9.3
18.3
0
9.3
18.3
0
9.3
18.3
6.8 ± 0.1
15.8 ± 1.5
25.4 ± 1.7
9.5 ± 0.2
19.2 ± 0.9
28.7 ± 2.2
8.2 ± 0.1
18.5 ± 1.7
26.7 ± 2.0
7.4 ± 0.1
16.9 ± 0.5
24.6 ± 0.5
6.5 ± 0.7
17.4 ± 1.2
24.8 ± 1.3
Fe recovery
(%)
97
102
104
105
111
101
103
94
118
100
Pb added
(μg L− 1)
Pb found
(μg L− 1)
0
9.0
17.8
0
9.0
17.8
0
9.0
17.8
0
9.0
17.8
0
9.0
17.8
1.5 ± 0.1
10.3 ± 0.9
19.4 ± 1.5
1.7 ± 0.1
10.9 ± 1.0
18.5 ± 0.9
1.3 ± 0.1
10.2 ± 1.0
21.9 ± 1.1
5.8 ± 0.1
15.1 ± 1.2
22.6 ± 0.9
5.5 ± 0.1
16.3 ± 0.8
23.8 ± 1.6
Pb recovery
(%)
98
101
102
95
99
116
104
95
120
103
344
D.M. Brum et al. / Spectrochimica Acta Part B 66 (2011) 338–344
Acknowledgements
The authors are grateful to CTPetro/FINEP/PETROBRAS, CNPq
(Conselho Nacional de Desenvolvimento Científico e Tecnológico)
and CAPES (Coordenação de Aperfeiçoamento de Pessoal de Nível
Superior) for providing grants and fellowships and for financial
support.
[14]
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