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The Optimal Hotspots of Dynamic Surfaced-Enhanced Raman Spectroscopy (D-SERS) for Drugs Quantitative Detection Xiunan Yan, Pan Li, Binbin Zhou, Xianghu Tang, Xiaoyun Li, Shizhuang Weng, LiangBao Yang, and Jinhuai Liu Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.6b04688 • Publication Date (Web): 30 Mar 2017 Downloaded from http://pubs.acs.org on March 30, 2017
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Analytical Chemistry
The Optimal Hotspots of Dynamic Surfaced-Enhanced Raman Spectroscopy (D-SERS) for Drugs Quantitative Detection Xiunan Yan,†,‡ Pan Li,† Binbin Zhou,†,‡ Xianghu Tang,†,‡ Xiaoyun Li,§ Shizhuang Weng,† Liangbao Yang,*,† and Jinhuai Liu† †
Institute of Intelligent Machines, Hefei Institutes of Physical Science, Chinese Academy of Sciences, Hefei 230031, China Department of Chemistry, University of Science and Technology of China, Hefei 230026, China § Shanghai Synchrotron Radiation Facility, Shanghai Institute of Applied Physics, Chinese Academy of Sciences, Shanghai 201204, China *L.B.Y. E-mail:
[email protected]; Fax: (+86)551-65592420; Tel: (+86)551-65592385 ‡
ABSTRACT: Surface-enhanced Raman Spectroscopy (SERS) as a powerful qualitative analysis method has been widely applied in many fields. However, SERS for quantitative analysis still suffers from several challenges partially because of the absences of stable and credible analytical strategy. Here, we demonstrate that the optimal hotspots created from Dynamic Surfaced-enhanced Raman Spectroscopy (D-SERS) can be used for quantitative SERS measurements. In situ Small angle X-ray scattering (SAXS) was carried out to in situ real-time monitor the formation of the optimal hotspots, where the optimal hotspots with the most efficient hotspots were generated during the monodisperse Au-sol evaporating process. Importantly, the natural evaporation of Au-sol avoids the nanoparticles instability of salt-induced and formation of ordered three-dimensional (3D) hotspots allows to SERS detection with excellent reproducibility. Considering SERS signal variability in D-SERS process, 4-mercaptopyridine (4-mpy) was acted as internal standard (IS) to validly correct and improve stability as well as reduce fluctuation of signals. The strongest SERS spectra at the optimal hotspots of D-SERS have been extracted to statistics analysis. By using the SERS signal of 4-mpy as a stable internal calibration standard, the relative SERS intensity of target molecules demonstrated a linear response versus the negative logarithm of concentrations at the point of strongest SERS signals, which illustrates the great potential for quantitative analysis. The public drugs 3, 4-methylenedioxymethamphetamine (MDMA) and α-Methyltryptamine hydrochloride (α-MT) obtained precise analysis with internal standard D-SERS strategy. As a consequence, one has reason to believe our approach is promising to challenge quantitative problem in conventional SERS analysis.
Since Surface-enhanced Raman Spectroscopy (SERS) was
nanostructure is increasing.5 Second, the SERS enhancement
discovered in the 1970s,1 with the integration of unique spec-
also strongly depends on the distance between the analyte and
troscopic fingerprint, nondestructive and ultrasensitive charac-
SERS substrates,6 so that the different distance can lead to a
teristics, SERS technique has become a powerful spectroscop-
huge difference in the enhancement factor (EF) and then cause
2,3
ic tool for chemical, biological, and environmental analyses.
significant alterations in the SERS performance and repeata-
Nevertheless, applications of SERS are often more associated
bility. Third, addition of aggregation agent induces the unpre-
with qualitative and semi-quantitative analysis, leaving the
dictable and complex micro-environment in the nanoscale of
important problem that quantitative SERS measurement is full
the SERS substrates, further decreasing the stability and re-
of challenging. What causes this? First, SERS intensity typi-
producibility of the measurements. In general, the application
cally depends on the nanomorphology of the substrate and
of SERS in quantitative detection was mainly hindered by
nanomaterials can be synthesized in various different shapes,
above circumstances. In recent years, many groups have ex-
such as spheres, nanowires, nanostars, rod-shaped, cubes, tri-
plored several complex systems to resolve quantitative detec-
angles, shells, and composites. It is worth noting that aniso-
tion problem.4,7-11 The common strategy is decorating a SERS
tropic nanostructures, such as cubes, nanostars and triangles,
active core with internal standard (IS) to reduce SERS signal
exits wide size distribution in each batch, which will severely
variability. One example is presenting IS functionalized self-
4
influence plasmonic properties of nanostructure. Therefore,
assembled monolayer(SAM) for quantitative calibration in
the demand for synthesizing nearly spherical shapes
SERS, which efficiently improves the reproducibility by pre-
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venting chemisorption of the analyte onto the substrates sur4
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the D-SERS process can be precisely controlled by regulating
face. With a similar goal, Zhang and coworkers developed a
experimental conditions. The excellent reproducibility of the
novel method for quantitative, which is based on isotope edit-
optimal hotspots also ensures quantitative detection with ex-
ed internal standard (IEIS) strategy. These methods in some
cellent reproducibility. Finally, the optimal hotspots is easily
cases can solve the quantitative analysis of target molecules,
created by evaporating 1µL monodisperse Au-sol on a fluoros-
however, there are still some limitations. For example high
ilylated silicon wafer, which largely avoids the fabrication of
necessary for isotope-edited counterpart of interesting targets
SERS substrate with uniformity hotspots by complex method.
is difficult for wide applications. Recently, Gwo and cowork-
Simple operation makes D-SERS more beneficial to be widely
ers8 demonstrated that self-assembled AgNPs superlattices
applied in quantitative detection. In a word, D-SERS is an
regulated by the alkanethiolate surface ligands as a stable in-
advanced and distinctive method for quantitative detection.
ternal calibration standard had efficiently resolved above issue
In this work, the optimal hotspots created from D-SERS
and further expanded the scopes of application. Additionally,
combined with an IS has been developed for quantitative de-
Tan and coworkers9 also designed a unique graphitic IS
tection. First of all, monodisperse spherical gold nanoparticles
nanostructure to quantify the analyte with better accuracy by
have been synthesized to avoid the irreproducibility and in-
multilayered core–shell AGNs. Considering the competing
credibility of SERS measurements. In addition, during evapo-
adsorption between analyte and internal molecules, Vo-Dinh10
rating process the monodisperse Au-sol without addition of
and Ren,11 developed novel quantitative analysis method
aggregation agent allow for SERS detection with excellent
based on core-molecule-shell nanoparticles, which will protect
reproducibility. Second, the optimal hotspots with uniform
IS signal from external environment influences.
hotspots and largely EF has been demonstrated by using in
In spite of the great efforts in the past few years, more simple
situ synchrotron-radiation small angle X-ray scattering (SR-
and practical quantitative SERS detection method is still nec-
SAXS). It is important to emphasize that the optimal hotspots
essary to continually explore for further wide applications. In
is a hot-area, in which the strongest spectrum will appear in
12
the work of Long, a simple and novel method for detection
time-dependent SERS mapping. The strongest signal intensity
of most of substituted aromatic pollutants using TLC-SERS
points with highly sensitivity and reproducibility are quite
technique was explored to realize qualitative and quantitative
stable and credible, which are suitable for quantitative detec-
detection. Zhao
13
and coworkers developed a simple SERS
tion. Most importantly, the optimal hotspots can be regulated
quantitative detection method of label-free proteins for the
by precision controlling the size of nanoparticles, ionic
first time in an aqueous solutions. In recent years, our group
strength, surface potentials, laser power integration time, and
developed the dynamic surfaced-enhanced Raman Spectros-
temperature. The strongest signal intensity points combined
copy (D-SERS),
14-23
which is based on nanoparticles transform
with 4-mpy ligands acted as a stable internal calibration stand-
20
ard can be used for quantitative SERS measurements. The
During the state transition process, the optimal hotspots was
quantitative analysis of model molecules has been demonstrat-
generated, which is a 3D long-range-ordered structure with a
ed with a linear relationship between the relative SERS inten-
lot of effective hotspots maintaining dozens of second in the
sity and the negative logarithm of the concentration. Finally,
process of D-SERS. The optimal hotspots with excellent re-
the
producibility and stability as well as advantage of high sensi-
(MDMA) and α-methyltryptamine hydrochloride (α-MT) ob-
tivity characteristic provide the possibility for the quantitative
tained precise quantitative analysis by our methods.
from the wet state to the dry state for SERS measurements.
detection by D-SERS method. Firstly, D-SERS method avoids the inaccurate problem of the quantitative detection which possibly caused by laser damage, decreases unreliable signals and improves the accuracy of the results. Secondly, the optimal hotspots created from D-SERS is a hot area, which makes the electromagnetic field greatly enhance and provides high sensitivity for quantitative detection. Most importantly, in comparison with the conventional SERS method of using nanoparticles random aggregation with uncontrollable nanogaps,
public
drugs
3,4-methylenedioxymethamphetamine
EXPERIMENTAL SECTION Reagents. HAuCl4· 4H2O, sodium citrate and ascorbic acid were purchased from Shanghai Chemical Reagent Co. Ltd.Triethoxy-1H,1H,2H,2H-tridecafluoro-n-octylsilane were purchased from Tokyo Chemical Industry. Crystal violet (CV) and 4-mercaptopyridine (4-Mpy) (95%) were brought from sigma-Aldrich. α-Methyltryptamine hydrochloride (α-MT) was obtained from institute of forensic science
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Analytical Chemistry
of Anhui public security department. 3,4-methylenedioxy-
point of the droplet. All of the D-SERS measurements were
methamphetamine was obtained from the Beijing Institute of
performed under the same laser wavelength (632.8 nm), laser
Pharmacology and Toxicology. All solutions were prepared
power (1 mw), mapping time (900 s), integration time(1.2 s),
by ultrapure water (=18.25 MΩ·cm).
reaction time of the instrument (0.3 s), objectives( LMD 50 ×),
Apparatus. The absorption spectra were obtained using UV-2550 spectrophotometer. The morphology of nanoparti-
temperature(25 °C) and relative humidity(40%) to reduce the fluctuation of signals.
cles was obtained from Focused Ion beam (FIB). The Zeta potential of Au NPs were estimated using Malvern Zetasizer
RESULTS AND DISCUSSION
Nano ZS90. The spectra were collected by Lab RAM HR800
What is the optimal hotspots of D-SERS? The monodis-
confocal microscope Raman system (Horiba Jobin Yvon).
perse Au NPs were synthetized by a seeded growth method as
The small angle X-ray scattering (SAXS) were carried out on
described in the literature.24 The absorption spectra and SEM
beamline BL16B1 at the Shanghai Synchrotron Radiation
image of Au NPs were shown in Section S1 of supporting
Facility (SSRF). The beamline works at wavelength λ=0.124
information. Typically, D-SERS measurement was carried out
nm (10 keV) and the detector is Mar165 CCD. The distance
on a hydrophobic silicon slide with horizontal X-Y-Z sample
between sample and detector was 5060 mm and the scatter-
platform, and 1µL mixture (analyte and colloid) was dropped
-1
ing vector (q) range was 0.03-0.814 nm .The background
onto the hydrophobic silicon slide. Then, X-Y sample stage
scattering signal was subtracted from water.
was adjusted to focus on the center point of the droplet. Final-
Synthesis of monodisperse Au NPs. The synthes is of 58
ly, Raman spectra were collected continuously until the drop-
nm monodisperse Au nanospheres (Au NPs) was carried out
let was complete evaporated. Such a continuous time acquisi-
according to the literature.24 Finally, the concentration of Au-
tion process provides the significant advantage of real-time
sols was 62 PM. Detail characterization of the Au NPs was
capturing and monitoring the alterations of SERS signals in-
shown in supporting information.
tensity in the process of D-SERS, so that one can in directly
Hydrophobic treatment of the silicon slice. The treatment of the silicon slice was carried out according to our previous reported method.22 Briefly, the silicon wafer was cleaned and hydroxylated via immersion in a boiling piranha solution. Then, the silicon wafer were washed repeatedly with ul-
obtain the real-time change information of the hot spots. In this continuous acquisition process, the intensity of spectra was gradually increased, reaching the strongest area and then began to decline immediately. The detail D-SERS process was shown in Section 2 of supporting information.
trapure water, ethanol and dried with nitrogen. The silicon wafer were treated directly by triethoxy-1H,1H,2H,2Htridecafluoro-n-octylsilane solution (40 mM) for at least 12 h. Then, the slices wafer were washed with ethanol, ultrapure water and dried with nitrogen. The internal standard decorated. A 2.5 mL volume of 1×10-7 M 4-mpy solution was added into 10 mL gold colloids and put in a water bath with a temperature of 30 °C for 12h. There are on average 315 molecules on per colloid particles. The Au nanoparticles functionalized with 4-mpy was centrifugated at 7800 rpm for 13 min for further use. Sample Preparation and SERS measurement. 1 µL nanoparticles concentrated from 1 mL decorated 4-mpy Au nanoparticles were treated with 1µL CV molecules. A hydrophobic silicon slide was positioned on the X-Y-Z sample platform. The laser spot was focus on the silicon wafer surface and 1µL mixture was dropped onto the hydrophobic silicon wafer. Then, X-Y sample stage was adjusted to focus on the center
Figure 1. (A) Schematic diagram of the optimal hotspots created from D-SERS, sketches representing a drop of Au-sol, 3D hotspots and aggregate of Au NPs, respectively. (B) The SERS spectra of 510-8 M CV obtained from the conventional SERS method (red line) and the optimal hotspots of D-SERS strategy
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(black line), respectively. (C) Variation of SERS intensity at the -1
-8
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reliable SERS detection. Therefore, it is very reliable to use
specific Raman modes of 1173 cm for 510 M CV obtained
the optimal hotspots for SERS detection. The characteristic
from independent 10 times D-SERS measurements, average in-
peaks of the full width at half-maximum (FWHM) of the tar-
tensity of 3D hotspots (grey) and intensity of the optimal
get molecule at the optimal hotspots are also relatively stable,
hotspots(red). (D) Variation of the full width at half-maximum
as shown in Figure 1D. The optimal hotspots with excellent
-1
reproducibility and stability as well as advantage of high sen-
(FWHM) at the specific Raman modes of 1173 cm .
In the process of forming hotspots, the distance between the nanoparticles is constantly decreasing. The inter-nanoparticles
sitivity characteristic provide the possibility for the quantitative detection by D-SERS method.
distance is a vital parameter, which determines the intensity of
Whether the optimal hotspots is true existence? In order to
local electromagnetic field.25 In previous study it was shown
further prove that the optimal hotspots is true existence. Small
that the gap between adjacent metallic nanostructures required
angle X-ray scattering was used to trace the monodisperse Au-
to obtain large SERS enhancements are in the sub-10 nm
sol evaporating process. SAXS is a small-angle scattering
range, known as “hotspots”. When interparticle gap dimen-
(SAS) technique which is based on the fluctuations of electron
sion approaching about 1-2 nm, a giant electric-field en-
density in system. SAXS contains abundant information about
hancement was obtained, but when the distance is less than 0.5
the shape and size of sample, imperfection structure, pore siz-
26
the
es and the periodic order structure. Nowadays, SAXS is wide-
charge density of the hot spots is weaking and resulting in
ly used as an indispensable tool to study colloidal system and
SERS enhancement factor decreases. Thus, there must be
their self-assemble process.29 Here, in situ SAXS was applied
strong signal points appear in the process of D-SERS, which
to real-time monitor the monodisperse Au-sol evaporating
are referred to as the optimal hotspots. In our previous D-
process. Taking into account the diameter of the beam spot, as
SERS study shown that two kinds of interactive forces can
well as the time resolution of the SAXS instrument, 5µL Au-
produce a “trapping well” to trap nanoparticles in 3D space,
sol were used to measure evaporation process, so that it can
nm nanoparticles to produce serious tunneling effect,
27,28
Thus, the “optimal hotspots” is a kind
capture the variations of hotspots as much as possible in the
of special state of the “3D hotspots”. In Figure 1A, the optimal
process of evaporation. SR-SAXS curves are plotted as the
hotspots were shown up as blue circles. At Au-sol stage, the
intensity of X-ray scattering I (q) versus scattering vector q.
gaps between nanoparticles are far larger than 10 nm. During
The scattering vector is defined by following equation:30,31
call “3D hotspots”.
14-23
the producing stage of hotspots, the gaps are changing from 10
q = 4π(sinθ)⁄λ
nm to 1nm. Range of the hotspots time is about 60 seconds,
(1)
while range of the optimal hotspots time is about 10 seconds
where θ is half the scattering angle and λ is a wavelength of
(Figure 1D and Figure S3). The time-dependent SERS map-
X-ray. According to Bragg formula:
ping shows the strongest signal point appeared in the process
2Lsinθ = λ
of D-SERS (Figure S3B), which indicates the optimal hotspots truly exist. In Figure 1B, with giant electric-field enhancement
(2)
The long period can be calculated by the following equation:
of D-SERS provides high sensitivity for SERS detection comL = 2π⁄q
pared with the conventional SERS method. D-SERS method
(3)
with strong signal strength, high signal-to-noise ratio allows detection CV down to 1×10-9 M, which improve the detection
The gap value can be calculated by the following equation:
sensitivity of CV by two orders of magnitude ranged from
gap = L − d
5×10-7 M to 1×10-9 M (See Supporting Information Figure S4).
(4)
Figure 1C shows the SERS intensity of the optimal hotspots
The long period is defined as the statistically average distance
and average intensity of 3D hotspots from independent 10
between the particles. For a long-ordered structure, with in-
times D-SERS measurements. The average intensity of 3D
creasing nanoparticle size and distance, the scattering curves
hotspots varies greatly and standard deviation is 53.4%. How-
will appear a scattering maximum.22,32 In this case, the formula
ever, the signal reproducibility of the optimal hotspots is pret-
(3) can be used to calculate the long period. Figure 2 shows the
ty good and standard deviation is only 8.1%, which brings
variation trend of SAXS curves during the monodisperse Au col-
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Analytical Chemistry
loid evaporation process. There is no obvious peak in the scat-
is negative in the last stage, which may be caused by the error
tering curves at 0 min, which indicates nanoparticles in the
range. The SAXS curves show that the gap distance is gradu-
stage of Brown movement. A simple calculation shows that
ally decreasing and then becoming negative, which indicates
the average center-to-center distance between Au NPs is 390
that the optimal hotspots was formed during the evaporating
nm, which is larger than the diameter of the Au NPs. With the
process.
evaporation of the solvent, the distance between the nanoparti-
Having shown the optimal hotspots truly exist, we asked
cles is gradually decreased, such that scattering curves appear
whether we can systematically control the formation of the
a shoulder peak at 57.9 min (Figure 2C) and become increas-
optimal hotspots. To answer this, classical Derjaguin-Landau-
ingly sharp with decrease of interparticle distance. It is clear
Verwey-Overbeek (DLVO) was used to describe the evaporat-
from Figure 2D, an obvious peak at q= 0.09152 nm-1 appears
ing process of monodisperse Au-sol. The theory is based on
indicating that the nanoparticles form a long-range ordered
two kinds of interaction forces, including van der Waals attrac-
structure. The distance of center-to-center of nanoparticles is
tion (Vvdw) and electrostatic repulsive forces (Velec). In the
68.6 nm and the corresponding gap is 10.6 nm. The 3D long-
initial stage, the nanoparticles are in the stage of Brown
range ordered structure corresponds to the 3D hotspots of D-
movement. With the evaporation of the water, Vander Waals
SERS, which shows that a periodic ordered structure is formed
attractive forces, a long range forces make nanoparticles grad-
by Au nanoparticle self-assembly during the evaporating pro-
ually closing. The distance between the nanoparticles is grad-
cess.
ually reducing, resulting in the double layer partially overlap, and then generated electrostatic repulsion force hinds nanoparticles closing each other. The balance of van der Waals attraction and electrostatic repulsion can create the optimal hotspots. The total interaction potential (VT) can be calculated by the following equation:33,34 V = V V V = −
!"#
(!"$%)# &'"#
!"#
(5)
(!"$%)# ln
(!"$%)# &'"# (!"$%)#
) (6)
Figure 2. SR-SAXS curves plotted as normalized intensity of X-
where AH , r and g are Hamaker constant, the radius of Au NPs
ray scattering I(q) versus scattering vector q at different times.(A)
, the interparticle gap, respectively.
0 min. (B) 57 min. (C) 57.9 min. (D) 60.7 min. (E) 70 min. (F) 80
V = 32πε, ε" r .
min.
/0 ! 1
2 ψ, ! exp (−kg)
(7)
Table 1. The distances variation of center-to-center with time calculated from SAXS data. Time(min) 58.3 58.9 60.7 63.6 66.5 70
-1
q(nm ) 0.08759 0.08837 0.09152 0.0923 0.10016 0.11351
Long period(nm) 71.69 71.1 68.6 68 62.9 55.32
k= Gap(nm) 13.69 13.1 10.6 10 4.69 -2.68
8,,,# 9: (!;) 8? ) !
