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AC08CH06-Centrone ARI 10 June 2015 13:21
Infrared Imaging and
Spectroscopy Beyond the
Diffraction Limit∗
Andrea Centrone
Center for Nanoscale Science and Technology, National Institute of Standards and Technology,
Gaithersburg, Maryland 20899; email: andrea.centrone@nist.gov
Annu. Rev. Anal. Chem. 2015. 8:101–26
First published online as a Review in Advance on
May 18, 2015
The Annual Review of Analytical Chemistry is online
at anchem.annualreviews.org
This article’s doi:
10.1146/annurev-anchem-071114-040435
∗This is a work of the U.S. Government and is not
subject to copyright protection in the United
States.
Keywords
s-SNOM, PTIR, resonance-enhanced AFM-IR, nanoscale infrared
spectroscopy, nanomaterials, chemical composition
Abstract
Progress in nanotechnology is enabled by and dependent on the availabil-
ity of measurement methods with spatial resolution commensurate with
nanomaterials’ length scales. Chemical imaging techniques, such as scatter-
ing scanning near-field optical microscopy (s-SNOM) and photothermal-
induced resonance (PTIR), have provided scientists with means of extract-
ing rich chemical and structural information with nanoscale resolution. This
review presents some basics of infrared spectroscopy and microscopy, fol-
lowed by detailed descriptions of s-SNOM and PTIR working principles.
Nanoscale spectra are compared with far-field macroscale spectra, which are
widely used for chemical identification. Selected examples illustrate either
technical aspects of the measurements or applications in materials science.
Central to this review is the ability to record nanoscale infrared spectra be-
cause, although chemical maps enable immediate visualization, the spectra
provide information to interpret the images and characterize the sample.
The growing breadth of nanomaterials and biological applications suggest
rapid growth for this field.
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Further
ANNUAL
REVIEWS
AC08CH06-Centrone ARI 10 June 2015 13:21
AFM: atomic force
microscopy
s-SNOM: scattering
scanning near-field
optical microscopy
PTIR:
photothermal-induced
resonance
RE-AFM-IR:
resonance-enhanced
AFM-IR
1. INTRODUCTION
Over the past two decades advances in top-down fabrication methods (1, 2) and in bottom-up
nanomaterial synthesis (3–5) have enabled the preparation of a plethora of nanomaterials. Be-
cause nanomaterials typically provide novel properties or improved performance with respect to
their macroscopic counterparts (6–10), they are of great interest in fields such as electronics (11),
photovoltaics (12), biology (13), therapeutics (8, 14), and many more. However, harnessing the
properties of nanomaterials in functional devices is not straightforward because their development
and integration often require characterizing the material/device properties (electrical, chemical,
thermal, etc.) at relevant length scales. This is particularly important for materials presenting
phase separation, heterogeneity, interfaces, or degradation at the nanoscale.
Infrared (IR) absorption spectroscopy directly probes vibrational energy levels and phonons
of materials, providing rich chemical and structural information (13, 15, 16), without any a priori
knowledge of the sample. However, the diffraction of the long IR wavelengths (2.5 μmto20μm)
limits the lateral resolution of IR microscopy to several micrometers (17, 18). In contrast, atomic
force microscopy (AFM) (19) provides nanoscale-resolution images of the sample topography by
scanning a sharp tip over the sample. Advanced AFM techniques can assess local mechanical (20–
22), electrical (23), and thermal (24, 25) properties by exploiting various tip-sample interactions.
However, they typically require complex modeling, a priori knowledge of the sample, or extensive
(and tip-specific) calibration to extract information quantitatively. Ideally, nanoscale information
should be immediately informative without necessitating complex calculations or prior knowledge
of the sample.
Two techniques that combine the high spatial resolution of AFM with the high chemical speci-
ficity of IR spectroscopy are scattering scanning near-field optical microscopy (s-SNOM) (26, 27)
and photothermal-induced resonance (PTIR) (28–30), also known as AFM-IR. IR s-SNOM (26,
27) measures the amplitude and the phase of light scattered from a tip in proximity to the sam-
ple. These quantities are functions of the sample index of refraction and absorption coefficient as
well as some other parameters. Although s-SNOM technology benefits from 30 years of steady
development, in the author’s opinion, it became of general utility to the analytical chemist only
recently, thanks to the ability of recording nanoscale IR spectra. Such an advance has been enabled
primarily by the availability of continuous narrow-band lasers with broader wavelength tunability
(31–33) and by broadband sources with wider bandwidth (34–39). By comparison, PTIR (28–30)
couples a pulsed, wavelength-tunable laser and an AFM cantilever to measure light absorption
in the sample by transducing the sample thermal expansion into mechanical cantilever motion.
Although of more recent development (≈10 years), PTIR received the immediate attention of an-
alytical chemists because of its ability to record nanoscale spectra that are immediately comparable
to far-field IR spectral libraries.
The intent of this review is to inform the reader on the fundamentals of near-field IR chemical
imaging and spectroscopy, focusing on the current state of the art, rather than to provide a
comprehensive history of technical development and a complete list of references. A few basic
concepts pertaining to IR spectroscopy and microscopy are presented first, followed by the working
principles of the s-SNOM and PTIR techniques. A few selected examples from the literature,
illustrating either technical aspects of the measurements or applications, are discussed. Finally, a
novel implementation of the PTIR technique, referred to as photoexpansion nanospectroscopy
(40, 41) or resonance-enhanced AFM-IR (RE-AFM-IR), is described. Because IR analysis should
be carried out by looking at a whole spectral pattern rather than a single peak or frequency, this
review emphasizes the ability of collecting nanoscale IR spectra and their comparison with far-field
spectra, commonly used for materials identification.
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2. INFRARED SPECTROSCOPY
IR spectroscopy measures vibrational transitions of molecules and materials via electrical dipolar
interactions with light, typically requiring photons with energies between 500 cm−1and 4,000 cm−1
(20 μm to 2.5 μm). The IR optical properties of a sample can be described by molecular dynamics
(42) at the molecular level, or by the complex refractive index (N) at the macroscale:
N(λ)=n(λ)+i·κ(λ),1.
where λis the wavelength of light and nand κare the real and imaginary parts of the index that
are related to light scattering and absorption, respectively. For nonmagnetic materials N,nand
κare related to the dielectric permittivity (ε) of the sample by
N(λ)=ε(λ),2.
Re[ε]=n2−κ2,3.
Im[ε]=2·n·κ. 4.
Although nand κcould be considered as the fundamental parameters of absorption peaks, ex-
perimentally they cannot be determined easily and typically they are not used for chemical
identification.
At the molecular level, a sample is classically represented as a collection of point masses (nu-
clei) held together by weightless springs (bonds). Because intermolecular interactions are much
weaker than intramolecular forces, in first approximation, the vibrational dynamic problem for a
molecule can be solved by considering the molecule to be isolated (and treating the intermolecular
interactions as a small perturbation). A molecule with nonlinear geometry composed of Natoms
has 3N−6 vibrational degrees of freedom (modes). To a hypothetical observer, the superposition
of all these vibrations would appear as a complex convolution. However, by approximating the
intramolecular potential with a harmonic function, it is possible to find a set of coordinates de-
scribing the motion of the nuclei for which all atoms reach the position of maximum displacement
and pass through their equilibrium positions at the same time (42). Such coordinates (normal
coordinates) are typically used in the theoretical calculations of IR spectra, and the vibrations they
describe are known as normal modes, which are independent (normal) from each other. IR light
can be absorbed only if the light electric vector oscillates at the same frequency as the molecular
dipole moment modulated by a particular vibrational mode (and if these two vectorial quantities
are not orthogonal to each other):
∂¯
μ
∂Q0
= 0,5.
where ¯
μis the molecular dipole moment and Qis the normal coordinate describing a normal
mode. The absorption intensity of a normal mode is proportional to the square of the molecular
dipole moment derivative with respect to the normal coordinate.
Internal coordinates describe vibrational modes independently of the molecule position and
orientation in space, giving scientists a chemically intuitive description of molecular vibrations
through changes of bond lengths and angles. Some vibrations are localized on a particular chemical
group and always occur in a narrow frequency range, no matter how complex the rest of the
molecule. The presence of functional groups in the sample can be easily determined by comparison
with empirical correlation tables (29, 30). Other vibrations involve many atoms (skeletal vibrations)
and are characteristic of the whole molecule.
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FPA: focal planar
array
The most important aspect of IR absorption frequencies is that they are the equivalent of fin-
gerprints for chemical identification and, typically, allow the identification of unknown samples
by comparison with IR spectral databases. Additionally, the perturbation of the molecule’s in-
tramolecular potential by intermolecular interactions (43), electronegativity of neighboring atoms
(44), intramolecular conformations (45, 46), or mechanical coupling with other vibrational modes
(44) can cause frequency shifts and intensity changes to the absorption peaks. Advanced use of
IR spectroscopy takes advantage of these perturbations to extract important chemical and struc-
tural information. Because of these complexities, IR characterization should not be carried out by
looking only at a specific peak, but rather by analyzing the entire spectral pattern.
An implicit consequence of considering isolated molecules for calculating IR spectra is that the
spectra are determined purely by absorption (not scattering), which is an approximation for solid
samples. In general, IR spectra are nontrivially related to nand κ(i.e., absorption and scattering),
with relative contributions that depend on the sample preparation and experimental conditions
(transmission, reflection, etc.). However, provided that the samples are prepared correctly and
consistently (i.e., diluted sample measured in transmission), the resulting spectra are determined
mostly by absorption and are, in practice, very useful for chemical identification.
In transmission, neglecting the reflection losses, the transmitted light (T) is a function of the
absorption coefficient (αabs) and of the path length of light in the sample (l):
T=I
I0
=e−αabs ·l,6.
where I0and Iare the light intensity of the incident and transmitted beams, respectively. The
absorbance is defined as
A=γ·l·c=−log T,7.
where αabs =γ·c,γis the molar absorptivity and cis the molar concentration of the absorbing
species. Equations 6 and 7 are good approximations if (a) the sample is homogeneous, (b) the sample
is weakly absorbing, (c) scattering is negligible, and (d)nand κof the sample are independent.
Under these approximations, the absorption coefficient is related to nand κby
αabs =4·π·n·κ
λ.8.
However, in the vicinity of sharp IR absorption peaks, ncommonly shows a strong dispersion (i.e.,
depends on κ) (47), and Equation 8 should be regarded only as an approximation.
3. INFRARED MICROSCOPY
Fourier transform infrared (FTIR) spectrometers coupled with IR microscopes enable scientists
to routinely obtain spectral information from small sample areas and chemical images. Given
the broad application of IR spectroscopy, the use of IR microscopy is also widespread and has
been the subject of recent reviews (48–51). Commonly, IR microimaging setups consist of a
broadband thermal light source, an FTIR spectrometer, an optical microscope with reflective
optics, and an IR focal planar array (FPA) detector. The availability of FPA detectors (with up to
256 ×256 elements) has been a truly enabling technology for IR chemical imaging, allowing for
the simultaneous recording of more than 65,000 location-specific IR spectra. The lateral resolution
of IR microscopes, limited by light diffraction, can be estimated by the Rayleigh criterion:
ξ=0.61 ·λ
NA ,9.
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SNR: signal-to-noise
ratio
where ξis the lateral resolution and NA is the numerical aperture of the microscope objective.
From Equation 9, two significant drawbacks are immediately clear. First, the lateral resolution
is comparable to λ, which in the mid-IR is too large to capture nanoscale details. Second, the
wavelength-dependent lateral resolution can limit the utility of IR analysis when mapping the
chemical distribution of components with characteristic peaks in different spectral regions or
when calculating intensity map ratios. The lateral resolution can be improved by a factor of four
by using germanium (n=4) attenuated total reflection objectives (49), but it is insufficient to
reach the nanoscale range. Recently, FPAs with pixel sizes smaller than the optical diffraction limit
have significantly improved image quality at the expense of lower signal-to-noise ratio (SNR) (52).
High-brilliance light sources such as synchrotrons (18) and quantum cascade lasers can compensate
for the lower SNR of those detectors (53, 54). Recently, a synchrotron source that combines
multiple beams (55) was used in combination with tomographic reconstruction algorithms to
extend IR microscopy to 3D.
Although the utility of IR chemical imaging is undeniable, it is also commonly recognized that
differences (peak shifts, baselines distortions, etc.) exist between IR spectra from the bulk or from
a microscopic portion of the sample. These differences arise mainly from the scattering and trans-
mission of light at interfaces with characteristic length scales comparable to the IR wavelengths
(56). In other words, spectra recorded with IR microscopes contain information related to both
nand κthat should be decoupled to obtain pure local chemical information independently of
morphology (i.e., scattering). Thanks to FPAs, far-field IR microscopy provides full spectral in-
formation within every pixel over relatively large areas, thus enabling high throughput. However,
the poor lateral resolution hampers its utility in nanoscale science and technology.
4. SCATTERING SCANNING NEAR-FIELD OPTICAL MICROSCOPY
In the quest for subwavelength imaging, earlier works (57–59) employed scanning probes with
small (≈50 nm) apertures to illuminate the sample locally. Because the power transmitted through
the aperture scales as ∼λ−4(60), this approach proved to be challenging in the mid-IR. In contrast,
IR s-SNOM (26, 27) measures the light scattering from a scanning probe tip in proximity to
the sample and has been applied to characterize several materials including plasmonic antennas
(61–67), nanowires and nanotubes (32, 68–70), polymers (26, 34, 35, 71), small-molecule thin
films (72), minerals (36, 37, 74), graphene (75–77), hexagonal boron nitride (78), and biological
samples (79–81). In-depth instrumental and theoretical details of s-SNOM are available in
former reviews (82–85). Although different s-SNOM setups have been reported, they commonly
require a light source (spectrally narrow or broad), far-field optics to focus and collect light, a
sharp scanning probe tip operating in tapping mode, and an interferometer with an IR-sensitive
detector for measuring the amplitude and phase of the scattered light (Figure 1a). Additionally,
a measurement scheme to amplify the weak near-field signal and to discriminate it from the
broad, nonlocal background is necessary. A spectrally flat reference material (such as Si or Au) is
typically used to compensate for the instrument spectral response.
The tip is crucial in s-SNOM because, in addition to the sample topography, it provides the
electric field confinement and enhancement necessary for achieving subwavelength resolution
(≈20 nm) (82). In s-SNOM, the tip-mediated light-sample interaction changes the amplitude and
phase of the scattered light as a function of the local complex index of refraction. Because the ampli-
tude and phase of the scattered light are not simple functions of κ(33), the resulting s-SNOM spec-
tral line shapes may not correlate with far-field IR spectra. In general, this makes the identification
of unknown materials and the interpretation of small spectral shifts or intensity changes challeng-
ing tasks requiring tip-specific modeling (27, 86) to accurately describe the tip-sample-substrate
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DFG
Fiber
Laser
a
2r
d
2r
2L
d
z
bc
Detector
RM
RM
s-SNOM
AFM cantilever
εsεSiO2
εSi
εtip
Figure 1
(a) Example of a s-SNOM setup: A fiber laser emits a pulse train (1.55 μm) (blue) together with a broadened redshifted beam ( green)
that are superimposed in a DFG unit resulting in a mid-IR continuum beam (red ) that illuminates the s-SNOM tip. The backscattered
light is measured with an asymmetric Michelson interferometer comprising a beam splitter and a reference mirror (RM). Panel a
adapted with permission from Reference 34. Copyright (2012) American Chemical Society. (b) Schematic for the point-dipole model:
The s-SNOM tip is replaced by the point dipole generated by a sphere with radius (r). According to this model, the scattered light
depends on the tip-sample distance (d) and on the dielectric functions of the tip (εtip) and of the sample (εS), but the sample thickness
(z) probed is limited to ≈r.(c) Modeling the tip as an ellipsoid of length 2Land apex rcan better reproduce the dependence of the
s-SNOM signal as a function of z. Abbreviations: AFM, atomic force microscopy; DFG, difference frequency generation; s-SNOM,
scattering scanning near-field optical microscopy.
interactions in the near field. Although rigorous, tip-specific modeling of light scattering is neces-
sary to interpret fine details in s-SNOM experiments, calculations typically approximate the tip as
a sphere of radius (r) comparable to the tip apex (Figure 1b). This simple model allows interpre-
tation of a large fraction of s-SNOM data with reasonable accuracy. According to this model, for
weakly dispersive (i.e., weakly absorbing) modes, the s-SNOM phase provides spectra that resem-
ble far-field IR spectra (34, 37, 61, 87). The sphere field scattered (Es) is modeled as a point dipole:
Es≈αeff ·Einc,10.
where Einc is the incident electric field and αeff is the effective polarizability of the tip, which
is a function of the dielectric properties of the tip (εtip) and of the sample (εs) (26). Under
the assumptions that (a) the sphere is polarized only in the sample direction, (b) the sample is
polarized only by the sphere dipolar field, (c) there are no retardation effects, and (d)rλ,
αeff =α·(1+β)
1−α·β
16π·(r+d)3,11.
where dis the distance between the point dipole and the sample and αand βare the polarizability
of the sphere and of the point dipole induced by the tip in the sample, respectively:
α=4·π·r3·εtip −1
εtip +2,12.
β=εs−1
εs+1.13.
Because εs,εtip,α,αeff ,andβare complex-valued quantities, characterized by an amplitude and
a phase shift with respect to the incident light, s-SNOM measurements rely on interferometric
techniques. For IR absorption (linear process), the s-SNOM signal increases approximately with
the fourth power of the near-field enhancement (83). Tips providing spectrally flat enhancement
(via the lightning-rod effect) such as dielectric tips or nonresonant metal-coated (Pt, Au) tips
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are preferred for measuring near-field spectra. For imaging at a fixed wavelength, noble metal
tips exhibiting λ-matching plasmonic or antenna resonances can provide higher sensitivity (88).
Metallic substrates under a thin sample can further increase the local field enhancement (89).
Despite the field enhancement, the s-SNOM signal typically is much weaker than the nonlo-
cal scattered background. To discriminate the two contributions, the near-field interactions are
periodically modulated by driving tip oscillations (of tens of nanometers) close to the cantilever
bending mode frequency (). Although the background is essentially unaffected by the modula-
tion, the nonlinear dependence of the near-field signal on the tip-sample distance (see Equation 11)
introduces higher harmonics in the scattered signal. The near-field signal is retrieved with a phase-
sensitive lock-in detection of the harmonics of the tip oscillation frequency. As a rule of thumb, for
the second and higher harmonics, the near-field signal typically exceeds that of the background.
The selective excitation of the tip apex via adiabatic surface plasmon polariton nanofocusing is an
interesting novel approach to reduce the unwanted background scattering that exploits the spatial
separation between the illumination location and the tip apex (90–92).
Homodyne (32, 93) or pseudoheterodyne (94) interferometric techniques are typically used to
amplify the near-field signal and determine its phase with respect to a known reference field. In
the case of homodyne amplification, the harmonics of the s-SNOM signal amplitude are mea-
sured for two reference phases shifted by 90◦, thereby enabling the determination of the near-field
magnitude and phase of the scattered light. In the case of pseudoheterodyne amplification, a con-
tinuous (sinusoidal) phase modulation is applied to the reference field, which results in improved
background suppression and allows the determination of the real and imaginary components of
the near-field signal.
Graphene attracts interest in several applications because of its unique combination of prop-
erties. However, electrical transport and plasmon propagation in graphene are negatively affected
by grain boundaries and other defects (75) and are typically difficult to characterize with con-
ventional techniques. s-SNOM tips were used to launch surface plasmons in graphene and to
visualize grain boundaries, invisible in AFM topography, by revealing the interference between
the plasmon waves launched by the tip and the wave reflected by the grain boundaries or other
defects (75, 76). s-SNOM was also used to image plasmon propagation in graphene flakes, reveal-
ing plasmonic wavelengths (tunable by electrical gating) that are up to 40 times shorter than in
free space (77). Similarly, surface phonon polaritons were measured in hexagonal boron-nitride
flakes (78), revealing long (10 μm) propagation lengths with wavelengths (tunable as a function of
the material thickness) that are up to 25 times shorter than in free space. Even higher wavelength
confinement (up to ≈70 times) was observed in boron-nitride nanotubes (70).
One common application of s-SNOM is mapping the amplitude (|Az|) and phase (Φz)of
the electric field component perpendicular to the plane of plasmonic structures (61–67, 95). The
modes of two plasmonic structures with similar resonant frequencies in proximity hybridize (96),
forming symmetric (bright) and antisymmetric (dark) collective modes (97). Whereas symmetric
bright modes have a large electric dipole and interact strongly with light, dark modes have a
small net electric dipole because the electric polarization vectors are in opposite phase in the two
structures. However, dark modes typically lead to stronger near-field enhancement because of their
reduced coupling with free space. s-SNOM provided the first direct experimental visualization of
the interference between bright and dark plasmonic modes (Figure 2) (62). These experiments
used a continuous-wave CO2laser that has a high brilliance but limited wavelength tunability,
thus preventing the acquisition of near-field spectra. However, by measuring the phase of the
scattered field, s-SNOM images can distinguish between bright-mode (in phase) and dark-mode
(out of phase) excitations, as exemplified in Figure 2cwhere the rods extremities are 180◦out of
phase, indicating a dark-mode excitation.
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A
B
QC
D
ac
0max-ππ
z
Si tip
Au
Polarizer
CaF2
Ein
Ep
y
x
IAzI
IAzI
Φz
Φz
b
1 µm
0.5
0.0 9
81011 12
1.0
1.5
Wavelength (μm)
Normalized reectivity
Einc
Einc
Figure 2
(a) Scattering scanning near-field optical microscopy (s-SNOM) measurement scheme. The structure is side illuminated with
s-polarized light, and light scattered by the tip is measured with an interferometer yielding both amplitude and phase near-field images
simultaneously with topography. (b) Numerically calculated reflection spectra for horizontal (blue) and vertical (red ) polarization as
indicated in panel a. The letter Q marks the wavelength used to acquire the near-field images with horizontal polarization in panel c;
the red letters mark the wavelengths used to acquire s-SNOM images for vertical polarization (data not shown). The inset shows the
topography image. (c) Experimental (top row) and calculated (bottom row) amplitude (|Az|) and phase (Φz) images for horizontal
polarization at 10.2 μm. Figure adapted with permission from Reference 62. Copyright (2011) American Chemical Society.
Quantum cascade lasers have wider wavelength tunability (a few hundred wavenumbers) than
CO2lasers, enabling the acquisition of near-field IR maps and spectra (31–33, 63). Even broader
spectral coverage in s-SNOM was achieved using a thermal source, similar to those used in FTIR
(98), but the low brilliance of this source limits its applicability. The availability of brilliant coher-
ent sources based on difference frequency generation of femtosecond pump pulses (34–36) (see
Figure 1a) has enabled the acquisition of broadband IR s-SNOM spectra covering up to
≈800 cm−1in the 700–2,100 cm−1spectral range (Figure 3) (34). For weak IR absorption peaks,
the s-SNOM phase spectra resemble, in good approximation, far-field IR absorption spectra,
thus enabling material identification (Figure 3c) (34). Furthermore, using the same setup and
more sophisticated modeling to analytically invert the near-field scattering problem, both n(λ)
and κ(λ) can be determined for weak absorbers, in good agreement with FTIR and ellipsometry
measurements (35).
Very recently, broadband synchrotron sources (37, 39) enabled the acquisition of near-field
amplitude images (showing contrast in the sample refractive index) and amplitude (|A|) and phase
(Φ) spectra across the full mid-IR spectral range (from 700 cm−1to 5,000 cm−1) (37). These setups
were used to measure SiO2,CaCO
3polymorphs, dried proteins (37), and semiconductors (39).
For an SiO2sample (Figure 4), the spectra reveal spectral shifts at the SiO2step edge, which are
attributed to changes in the sample thickness and/or to changes in the tip-sample interaction for
the sharp interface (37).
According to the point-dipole approximation, the field enhancement under the tip is confined
to a depth comparable to the tip radius, thus limiting the utility of the s-SNOM analysis to
only the top (≈25 nm) portion of the sample. This is a good approximation for nontransparent
samples. However, at IR frequencies, s-SNOM can image high index materials under a low index
transparent layer up to 80-nm thick (89, 99). Optimization of the imaging conditions of buried
objects is still the subject of investigations (100), but the point-dipole approximation is insufficient
to interpret those results (101). For an SiO2film on Si, calculations that approximate the tip as
a spheroid of length 2Land radius r(r2Lλ) show that the saturation of the s-SNOM
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200 nm200 nm
Si PMMA
P4
P3
P3
P4Si-CH3
Si-O-Si
CH2
C-C-O
C-O-C
a
c
b
0.0
0.1
0.2
1,400 1,300 1,200 1,100 1,000 900
Frequency (cm–1)
Aabs = |A|sinΦ
0.0
0.1
0.2
0°
69°
Min
Max
Figure 3
(a) Topography image of a scratched PMMA film on an Si substrate. (b) Tapping-mode atomic force
microscopy phase image, where the strong contrast designates a particle consisting of neither PMMA nor Si.
(c) s-SNOM absorption (Aabs =|A|sinΦ) spectra of PMMA (from position P3) and of the particle (P4),
identified by its IR spectrum as PDMS. The acquisition time was 7 min, and the spectral resolution was
13 cm−1. Figure adapted with permission from Reference 34. Copyright (2012) American Chemical Society.
Abbreviations: PDMS, polydimethylsiloxane; PMMA, poly(methyl methacrylate); s-SNOM, scattering
scanning near-field optical microscopy.
signal occurs only for sample thicknesses (z) comparable to L(Figure 5), in better agreement with
experiments (101).
In far-field transmission experiments, the positions of IR bands are used to identify the sample
chemical composition, and their intensities (proportional to the molar concentration, Equation
6) are often used to quantify the relative amounts of chemical species in a mixture via multivariate
analysis. Although experimental validation on heterogeneous samples is needed, significant
progress in relating s-SNOM spectra with far-field FTIR spectra was recently accomplished
through analyses of the amplitude (A), phase (φ), and absorption (Aabs =|A|sinΦ) signals as a
function of thickness in PMMA films on highly reflective substrates (Si) (102). Peak intensities in
the phase spectra increased with thickness, and their positions (blueshifted by ≈3and9cm
−1with
respect to far-field grazing angle and transmission spectra, respectively) were mostly insensitive
to the thickness. If such small spectral shifts are not crucial for material identification, the phase
signal may be used to identify the chemical composition of samples with varying thicknesses.
In contrast, although the peak intensities of absorption spectra are a nonmonotonic function of
thickness, their peak positions match well with the bulk transmission spectra for thick (174 nm)
samples or with the grazing angle spectra for thin (10 nm) samples, allowing for direct comparison
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Topography
Si
500 nm
0 100
0
z (nm)
Wavenumber (cm–1)
Distance (nm)
D (nm)
D (nm)
200
SiO2
Si
SiO2
a
Broadband amplitude
b
|A(v)|
c
Ф(v)
d
e
f
1,000
2,000
3,000
4,000
0 200 400 600 800
0
1
a.u.
1
2
3
4
1,000 1,500 2,000
Wavenumber (cm–1)
|A(v)| (a.u.)
0
1
2
3
1,000 1,500 2,000
Wavenumber (cm–1)
Ф(v)
1
1
2
2
3
Wavenumber (cm–1)
Distance (nm)
1,000
2,000
3,000
4,000
0 200 400 600 800
0
1
rad
123
3
500 nm
40
nm
20 40
0 200
μV
Figure 4
(a) Atomic force microscopy topography and (b) broadband scattering scanning near-field optical microscopy
(s-SNOM) amplitude image of an SiO2disk on Si. The insets show the profiles of topography and s-SNOM
intensity across the SiO2edge corresponding to the white lines. Dindicates the distance along the white
lines. (c) Amplitude and (d) phase line scans along the white line in panel a, consisting of 30 spectra (each
16-cm−1spectral resolution, 1-min acquisition time). (e) Amplitude and ( f) phase spectra obtained at the
three marked locations in panels cand d. The spectra have an offset for clarity. Figure reproduced with
permission from Reference 37.
MOF: metal-organic
framework
with far-field measurements. Although for intermediate or unknown sample thicknesses, model-
ing is necessary for comparing the s-SNOM absorption spectra with FTIR absorbance spectra,
these recent findings bring the application of multivariate analysis at the nanoscale a step closer.
5. PHOTOTHERMAL-INDUCED RESONANCE
In PTIR (28–30), the AFM cantilever serves the function of near-field detector to measure the
absorption of IR light in the sample. PTIR characterization has been applied to investigate a wide
array of samples including bacteria (28, 29, 103–106), cells (107–110), lipids (111), proteins (112),
polymers (30, 113–121), drugs (122, 123), quantum dots (124), plasmonic nanostructures (97,
125–127), metal-organic frameworks (MOFs) (128), and organo-trihalide perovskites (129). PTIR
setups require a pulsed, spectrally narrow, wavelength-tunable laser source; an AFM tip operating
in contact mode; and far-field optics to focus light under the tip. PTIR was implemented initially
with a free electron laser offering wavelength tunability over the whole mid- and far-IR (28, 103).
Today, PTIR more commonly relies on tabletop sources based on optical parametric oscillators
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Si
Si
SiO2
SiO2
2 µm
300 nm
105 nm
22 nm
18 nm
2 nm
50 nm
a
c
b
1,000900 1,100 1,200 1,300
1
2
00
3
4
5
6
1
2
3
4
5
6
Frequency (cm–1)
1,000900 1,100 1,200 1,300
Frequency (cm–1)
A3
A3
300 nm
105 nm
22 nm
18 nm
2 nm
1
1,000900 1,100 1,200 1,300
2
3
0
4
5
6
Frequency (cm–1)
A3
max A3
300 nm
105 nm
22 nm
18 nm
2 nm
50 nm
Experiment
r = 30 nm
r = 50 nm
r = 30 nm, L = 15a
6
1
2
3
4
5
1000 200 300
SiO2 thickness (nm)
d
Figure 5
(a) Experimental scattering scanning near-field optical microscopy (s-SNOM) spectra for different SiO2film
thicknesses obtained from the absolute value of the third harmonic scattering amplitude (A3) normalized by
the signal from the bare Si wafer. Inset: topographic atomic force microscopy image of 18-nm-thick SiO2
sample (top) and corresponding near-field image acquired at 1,130 cm−1.(b) Theoretical results for the
spheroid model with r=30 nm and L=15r(see Figure 1c). (c) Theoretical results for the point-dipole
model with r=30 nm and b=0.75r,wherebis the distance between the position of the point dipole and
the bottom of the tip. (d) Calculations of the thickness dependence of the A3intensity for different tip
models: point dipole with r=30 nm (blue), point dipole with r=50 nm ( green), and spheroid model with
r=30 nm and L=15r(red ). Black circles indicate data derived from the experiments shown in panel a
after some smoothing over of fluctuations. Figure adapted with permission from Reference 101. Copyright
by the American Physical Society.
(30, 114, 130) or difference frequency generation (118, 128), covering a range as large as 625–
20,000 cm−1(0.5–16.0 μm) (130) and reaching the visible range. Most commonly, total internal
reflection (30, 115, 118) is used to illuminate a sample placed on top of an optically transparent
prism (i.e., zinc selenide) (Figure 6a). In total internal reflection, the evanescent field excites the
sample and minimizes the direct light-tip interaction, resulting in better SNR. Setups using top
illumination (40, 41, 113) or transmission (109) through a transparent substrate have also been
reported.
Although the laser spot is large (≈30 μm), the sharp AFM tip serves as a near-field detector
by locally transducing the small thermal expansion resulting from light absorption in the sample
into large cantilever oscillations, thus conferring high lateral resolution on the measurement.
Such oscillations are monitored by reflecting the AFM laser beam from the cantilever onto a
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AFM laser
4-Q detector
Tunable pulsed laser
1 µm
Absorbance (a.u.)
Wavenumber (cm–1)
PMMA
1,142 cm–1 825 cm–1 694 cm–1
0
100
200
nm
+
+
+
+
+
+
50 1,100 900 700150 250
Time (μs)
a.u.
100 300 500 700
Frequency (kHz)
a.u.
f
e
gh
Epoxy PS
bd
a
c
Figure 6
(a) PTIR schematic: When the sample absorbs an IR laser pulse, it rapidly expands, deflecting the AFM cantilever, which is monitored
by a 4-Q detector. (b) The maximum peak to peak deflection during the cantilever ring down is proportional to the absorbed energy.
(c) Cantilever’s contact resonance modes excited by the sample expansion and obtained by Fourier transformation of the ring-down
signal. (d) AFM topography image of a sample made of PMMA and PS particles in epoxy matrix. (e) PTIR spectra recorded at the
locations marked with +symbols in panel d.(f) PTIR image recorded at 1,142 cm−1corresponding to PMMA. (g) PTIR image
recorded at 825 cm−1corresponding to epoxy. (h) PTIR image recorded at 694 cm−1corresponding to PS. All scale bars are 1 μm.
Figure adapted with permission from Reference (118). Copyright 2013 American Chemical Society. Abbreviations: 4-Q, four-quadrant;
AFM, atomic force microscopy; PMMA, poly(methyl methacrylate); PS, polystyrene; PTIR, photothermal-induced resonance.
four-quadrant photodetector (Figure 6). The theory of PTIR signal generation was developed
in detail by Dazzi and coworkers (131, 132) and was recently corroborated with experimental
observations (30, 118). Briefly, the PTIR signal is obtained by transducing optical energy into
heat, heat into thermal expansion, thermal expansion into cantilever oscillation, and cantilever
motion into photodetector signal (118, 131). The sample thermal expansion is much faster than
the AFM feedback (impulsive loading), fast enough to induce a ring down in the photodetector
signal (Figure 6b) and to excite several mechanical modes in the cantilever (Figure 6c). The
laser repetition rate (typically 1 kHz) allows sufficient time for completing the ring down before
the arrival of a new pulse and enables synchronous signal averaging. Spectroscopic information
is extracted either by determining the deflection maximum in the time domain (Figure 6b)or
by determining the amplitude of one of the cantilever normal modes in the frequency domain
obtained by Fourier transforming the ring-down signal (Figure 6c). Location-specific IR spectra
are obtained by plotting the signal, normalized by the laser power, as a function of λ(Figure 6e).
Chemical maps (see, for example, Figure 6f–h) are obtained by illuminating the sample at constant
wavelength and plotting the signal as a function of location. Typically, several pulses are averaged
at each point to increase the SNR.
Methods for achieving a higher SNR are of great interest because, by reducing the acquisition
time, they can increase the typically low throughput of scanning probe experiments. Because the
PTIR signal varies in both time and frequency (Figure 6), noise can be reduced by applying a
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1
0
3,3003,400 3,200 3,100 3,000 2,900 2,800
2
3
4
5
6
7
8
9
Wavenumber (cm–1)
Amplitude (a.u.)
Figure 7
Photothermal-induced resonance spectra of an 800-nm-thick ethylene acrylic acid polymer film obtained
with commercial rectangular Si cantilever (bottom,black), paddle probe (middle,blue), and modified paddle
probe (top,red ). The spectra obtained with the paddle and the modified paddle probes have amplitudes three
and six times larger than the rectangular probe, respectively. Figure adapted with permission from
Reference 114. Copyright Institute of Physics. All rights reserved.
PMMA: poly(methyl
methacrylate)
time-frequency signal transformation (such as the Morlet wavelet transform) and by filtering the
signal for times longer than the cantilever ring down and for frequencies not corresponding to the
cantilever bending modes (119, 120). By virtue of reduced noise, this method yields a 32-fold
reduction in the need for pulse averaging (120), resulting in a corresponding throughput increase.
The SNR can also be increased by using cantilevers with higher sensitivity, which provide a
stronger signal. For example, modified Si cantilevers with an internal resonator paddle (Figure 7)
resulted in a 6-fold SNR increase with respect to unmodified cantilevers (114). Such modification
reduces the cantilever stiffness, providing higher sensitivity, and reduces the cantilever dumping,
prolonging the ring down, thus reducing the relative noise. Because the SNR is a square-root
function of the number of averages, a 6-fold-better SNR leads to a 36-fold-higher throughput
(114).
According to theory (131, 132), the PTIR signal (S) is proportional to the absorbed energy
per unit area (Uabs), to the cube of the sample thickness (z), and to the sample thermal expansion
coefficient (αexp). It is also inversely proportional to the sample thermal conductivity (η):
S∼αexp
η·Uabs ·z3.14.
This expression was confirmed experimentally in a total internal reflection configuration by analyz-
ing the spectra of a poly(methyl methacrylate) (PMMA) wedge patterned with e-beam lithography
(30) (Figure 8). The PTIR signal increases linearly with thickness up to ≈1μm; it then reaches
a(λ-dependent) maximum and decreases for larger z(Figure 8a), because of the exponentially
decaying field amplitude in the sample. In a total internal reflection configuration, Uabs is not
proportional to the sample thickness and does not have a simple analytical expression. Instead,
it depends on how steeply the evanescent field intensity decays inside the sample (30). In total
internal reflection, the field amplitude in the sample (E) is usually expressed as a function of the
penetration depth (dp) (133, 134):
E=E0·e−z/dp,15.
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1,7001,800 1,600 1,500 1,400 1,300 1,200
Wavenumber (cm–1)
Absorbance (a.u.)
1,0000 2,000 3,000
Thickness (nm)
1,720 cm–1 Intensity (a.u.)
5000 1,000
Thickness (nm)
1,720 cm–1 Intensity (a.u.)
1,0000
0.00
0.02
0.04
2,000 3,000
Thickness (nm)
Uabs
a
d
bc
90 nm
145 nm
200 nm
245 nm
335 nm
380 nm
445 nm
485 nm
555 nm
705 nm
z
Figure 8
(a) Photothermal-induced resonance (PTIR) intensity of the 1,720 cm−1poly(methyl methacrylate) (PMMA) peak as a function of
thickness; experimental data are interpolated by the function a·z3·e−2·z
dpwith correlation coefficient (R2)of0.963anddp=
1,070 nm. (b) PTIR intensity of 1,720 cm−1PMMA peak as a function of thickness (up to 1,200 nm). In this region, the data can be
interpolated linearly with an R2=0.993. (c) Calculated absorbed energy per unit area as a function of thickness according to
Equation 17 (λ=1,720 cm−1,θ=45◦,nZnSe =2.438, nPMMA =1.54, and κPMMA =0.2). (d) PTIR spectra as a function of PMMA
thickness. Copyright Wiley-VCH Verlag GmbH & Co., KGaA. Figure adapted with permission from Reference 30.
where E0is the electric field amplitude at the prism-sample interface and dpis the distance where
Eis reduced by a factor of eand is given by (135):
dp=λ
2·π·⎡
⎣(n2
1·sin2θ−n2
2+κ2
2)2+(2 ·n2·κ2)2+(n2
1·sin2θ−n2
2+κ2
2)
2⎤
⎦
−1/2
,16.
where θis the light incident angle and N1(λ)=n1(λ)and N2(λ)=n2(λ)+iκ2(λ)are the refractive
index of the nonabsorbing prism and of the sample, respectively (30). Uabs is a nonmonotonic
function of z(Figure 8c) and can be calculated (30) as the difference between the evanescent fields
intensities for a nonabsorbing medium (κ=0) and for a sample with extinction coefficient κ2:
Uabs ∼E2
κ=0−E2
κ=κ2.17.
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SEIRA:
surface-enhanced IR
absorption
By using λ-dependent optical constants (47), the data in Figure 8 (30) fit well with the theory
prediction (131, 132) (Equation 14), demonstrating the proportionality between the PTIR signal
and the absorbed energy. Additionally, for thin (z<1μm) samples, the PTIR signal increases
approximately linearly with thickness (Figure 8b), a necessary condition for enabling quantitative
IR analysis. For a mixture of two components (A,B), the thermal conductivity can be expressed
as the concentration-weighted average of the component’s thermal conductivities (ηA,ηB):
ηmi x =cA·ηA+(1−cA)·ηB.18.
Because the PTIR signal is proportional to the absorbed energy and, in first approximation,
the absorbance is proportional to the molar concentration (Equation 7), the concentration of
A(cA) affects both the absorbed energy (Equation 14) and the thermomechanical properties of
the mixture (Equation 18). Consequently, the PTIR signal is strictly linearly proportional to the
concentration only for components with the same thermomechanical properties and will deviate
from linearity if α/ηis substantially different for the two components. However, for mixtures with
similar thermomechanical properties such as some biological samples (136), polymer blends, and
diluted mixtures, the deviation is small (30).
The application of PTIR in biology and polymer science was reviewed recently (85, 137) and
is not discussed here extensively. A short list of those applications includes the localization of
viruses inside bacteria and the identification of viral infection stages (103), the localization and
quantification of biopolymer (105) and biofuel (106) production in bacteria cultures, the imaging
of phase-separated drug-polymer blends (122), and the characterization of drug nanocrystals in
solid matrixes (123). A few examples of PTIR characterization in materials science are reported
below.
Plasmonic nanostructures attract interest in energy (138), sensing (139), and therapeutic (14,
140) applications because of their ability to enhance light-matter interactions at the nanoscale.
Surface-enhanced infrared absorption (SEIRA) spectroscopy (66, 97, 141, 142) exploits this effect
to increase the sensitivity of IR spectroscopy. Until recently, SEIRA hot-spot engineering relied
only on theoretical calculations (143) because of the low resolution of IR microscopy. Furthermore,
high SEIRA sensitivity is typically accompanied by Fano spectral distortions (144), which can
complicate chemical identification. PTIR broke those paradigms by enabling direct observation
and quantification of SEIRA near-field hot spots with nanoscale resolution (97). For example,
PTIR maps and spectra on gold asymmetric split ring resonators coated with PMMA revealed
local absorption enhancement factors up to ≈30 at 100-nm lateral resolution (Figure 9) (97).
Because the PTIR signal is a function of κand independent of n(scattering) (30), PTIR spectra
are proportional to the local field intensity times the sample absorption coefficient and are free
of Fano distortions [which derive from n(λ)] (97). For the same reason, PTIR maps of PMMA
absorption in Figure 9 are a direct image of the near-field absorption enhancement.
PTIR has also been used to obtain near-field absorption spectra of plasmonic resonators and
to image their plasmonic modes (Figure 10) (126). Again, the PTIR spectra of the resonators are
not complicated by Fano resonances, typically observed in s-SNOM and far-field spectroscopies.
Although probed locally, the PTIR spectra are characteristic of the resonator’s collective plasmonic
modes, not of the constituent isolated arcs. The PTIR images in Figure 10 display the energy
dissipated in the arcs due to the plasmonic excitation (126, 145), and provide evidence of the
interference between the bright and dark modes in these structures. One limitation of PTIR
images is that they do not provide phase information, making theoretical calculations necessary
to assign bright- or dark-mode excitations (97).
Another challenge that PTIR has addressed is the determination of the linkers’ distribution
within MOF single crystals (128). MOFs are nanoporous functional materials consisting of
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0
2.5
5
10
7.5
(nm)
1,455 cm–1 intensity (a.u.)
1,263 cm–1 intensity (a.u.)
Absorbance (a.u.)
PMMA on hot spot
PMMA away from ASRR
1,3001,4001,5001,600
1,7001,800 1,200 1,100
0
10
20
30
1,100 1,300 1,500 1,700
Enhancement
a
de
bc
Wavenumber (cm–1) Wavenumber (cm–1)
Figure 9
(a) AFM height image of gold resonators with 1,700-nm diameter coated with a 200-nm PMMA film. The
white arrow indicates the direction of the electric field polarization in the PTIR experiments. (b) PTIR
image of the PMMA CH3antisymmetric deformation mode at 1,455 cm−1.(c) PTIR image of PMMA C–O
stretching vibrational mode at 1,263 cm−1. The intensity scale of the PTIR images was normalized by the
incident laser power; the scale bars are 500 nm. (d) PTIR spectra (in common scale) for 200-nm-thick
PMMA, either away from the array (red ) or on the hot spot of a resonator with 2,000-nm diameter (black).
(e) Surface-enhanced IR absorption enhancement factor is defined as the ratio of the PTIR spectral intensity
of PMMA in the hot spot to the spectral intensity of PMMA away from the resonator. Figure adapted with
permission from Reference 97. Copyright 2013 American Chemical Society. Abbreviations: AFM, atomic
force microscopy; ASRR, asymmetric split ring resonators; PMMA, poly(methyl methacrylate); PTIR,
photothermal-induced resonance.
inorganic clusters interconnected by organic linkers whose structure and properties can be
tailored by chemical design (146–148). MOFs find applications in catalysis (149), separation
(150), sensing (151), etc. Synthesis of MOFs with multiple linkers (mix-MOFs) raised the
question pertaining to the linkers’ spatial distribution within MOF crystals (148), which is hardly
determinable with conventional techniques because of the low spatial resolution. PTIR data
on individual mix-MOF crystals isoreticular to In-MIL-68 (152) determined that those crystals
are homogeneous at the ≈100-nm scale (128). Additionally, such characterization enabled the
development of a new synthesis method for engineering anisotropic domains in MOF single
crystals with linker composition gradients occurring over ≈600 nm (128).
In contrast with FTIR, the lateral resolution of PTIR does not depend on λ(118, 130, 132),
but instead depends on the tip size and on the sample thermomechanical properties (132). For
thin samples with ηsample significantly smaller than ηsubstrate, the resolution is determined by the tip
size rather than by the sample properties (105, 130). For thicker samples, the lateral resolution
may also depend on the sample properties, but it is not easily predictable because the thermal
properties at the nanoscale often differ from bulk values (153) and because the thermal diffusion at
the materials’ interfaces is not well understood. In fact, mismatches in phonon energy and density
of states across different materials can cause phonon scattering instead of transmission, resulting
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Wavenumber (cm–1)
ab
cd
1,432 cm–1
Intensity (a.u.)
Intensity (a.u.)
1,268 cm–1
Intensity (a.u.)
1,384 cm–1
Intensity (a.u.)
1,150
1,350
1,550
++
Figure 10
(a) Photothermal-induced resonance (PTIR) spectra of a gold asymmetric split ring resonator collected at
locations identified by a color-coded plus symbol in the inset height image. (b–d) PTIR maps (each displayed
with its own unique full scale) emphasize the contribution of each arc to the collective resonator’s modes at
wavelengths corresponding to the dotted color-coded vertical lines in panel a. All scale bars are 500 nm.
White arrow in the inset in panel aindicates the direction of the electric field polarization. Copyright
Wiley-VCH Verlag GmbH & Co., KGaA. Figure reproduced with permission from Reference 126.
in interfacial thermal resistance (154, 155). Even if not completely understood, these effects favor
high lateral resolution in PTIR, which is typically 100 nm or better (30, 104, 107, 114, 116).
Recently, the PTIR spectra range was extended to the visible range, enabling the acquisition of
correlated vibrational (chemical) and electronic property maps and spectra with a wavelength-
independent resolution as high as ≈20 nm (130). As in the case of s-SNOM, the lateral resolution
is reduced for buried objects (97, 103).
A major challenge of PTIR in the total internal reflection configuration is the sample prepa-
ration on the prism. However, the linear dependence of the PTIR signal with sample thickness
(for z<1μm), its proportionality to the absorption coefficient, and its independence of scat-
tering (30) allow materials to be identified at the nanoscale using FTIR spectral libraries. Newly
commercially available setups employing top sample illumination remove the challenge of sample
preparation, but they require gold-coated cantilevers to avoid direct light absorption in the tip.
The dependence of the signal intensity on the sample thickness has not yet been determined for
the top sample illumination configuration.
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Lens
1,340 1,350
Wavenumber (cm–1)
Cantilever deection signal (a.u.)
1
0
2
0 120 240
Position (nm)
Height (nm)
11
10
9
8
7
6
5
4
3
2
1
11
10
9
8
7
6
5
4
3
2
1
b
ad
e
c
Mid-IR
laser
Lock-in Amplifier
Ref.
Sig. Output
Trigger
4-Q detector
Cantilever
deflection
Wavelength
F
F
Gold
Self-assembled
monolayer
200 nm 200 nm
0
4
Height (nm)
0.6
2.0
Cantilever deflection (a.u.)
Figure 11
(a) Schematic of resonance-enhanced AFM-IR (RE-AFM-IR) setup: Light pulses from a quantum cascade
laser (p-polarized) are focused on a sample. The signal of the cantilever deflection is sent to a lock-in
amplifier referenced to the laser repetition rate. The amplifier output is a measure of the cantilever oscillation
amplitude at the lock-in reference frequency. (b) Topography of poly(ethylene glycol)-methyl-ether-
thiol monolayer islands; (c) corresponding RE-AFM-IR absorption image at 1,342 cm−1(CH2wagging
mode). Bright regions are the sample, and dark regions are gold. (d) Topographic line scan along the blue
arrow in panel b: Square symbols mark positions where the RE-AFM-IR spectra were recorded. (e)
RE-AFM-IR spectra (with offset) corresponding to the color-coded positions in panel d. Figure adapted with
permission from Macmillan Publishers Ltd., Nature Photonics, Reference 41, copyright 2014.
6. RESONANCE-ENHANCED AFM-IR
RE-AFM-IR is a novel technique combining some aspects of PTIR and s-SNOM measurements
and has been used to characterize polymers (40) and self-assembled monolayers (41). RE-AFM-
IR setups require a pulsed, spectrally narrow laser source tunable both in wavelength and in
repetition rate, an AFM tip operating in contact mode, far-field optics to focus light under the tip,
and a lock-in amplifier (Figure 11). A gold reference spectrum is typically used to account for the
instrument spectral response (41). The sample is illuminated either through a transparent substrate
(40) or from the top (41). Quantum cascade lasers have characteristics suitable for RE-AFM-IR.
RE-AFM-IR exploits both the Q-factor of the cantilever and the sample thermal expansion to
measure IR absorption spectra and maps, by matching the laser repetition rate to the frequency of
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one of the cantilever oscillations. This way, subsequent laser pulses induce sample expansion and
transfer energy to the cantilever synchronously with the cantilever oscillation (i.e., on resonance),
resulting in Q-fold amplification of the signal (Q ≈100 for commercially available cantilevers).
Lu et al. (41) showed that by exploiting the large electromagnetic field enhancement (≈105)
found in the nanogap between a sharp gold-coated tip and a gold substrate, RE-AFM-IR can
achieve monolayer sensitivity (see Figure 11). In this case, the field confinement at the tip pro-
vided lateral resolution comparable to the tip size (≈25 nm). RE-AFM-IR induces very small
temperature changes in the sample, which is important for studying biological samples. While
scanning, the cantilever resonances typically vary in frequency, intensity, and Q-factor because
of, for example, changes in the sample mechanical properties. Consequently, for general applica-
bility, measurement schemes that spatially maintain or track the cantilever resonance should be
employed to ensure measurement stability.
7. CONCLUDING REMARKS AND PERSPECTIVE
Although driven by a common goal, by measuring absorbed and scattered light, respectively,
PTIR and s-SNOM are largely complementary techniques. For example, bright and dark plas-
monic modes behave differently: The bright-mode excitation results mainly in scattered light and
little absorption, whereas the dark mode exhibits strong absorption and weak scattering (126).
Consequently, PTIR is typically more sensitive to dark modes, whereas s-SNOM is more sensi-
tive to bright modes. Additionally, s-SNOM is mostly sensitive to a thin portion of the sample
in proximity to the tip, whereas, in the total internal reflection configuration, PTIR probes sam-
ple thicknesses comparable to the field penetration depth (30). Finally, materials with large αexp
and small ηare generally easy to measure with PTIR, whereas materials with very small αexp are
challenging (118). However, the latter do not present additional challenges for s-SNOM. Con-
sequently, the nature of the material and its thickness play important roles when choosing an
appropriate measurement method. Furthermore, the two methods are not entirely equivalent:
s-SNOM can provide phase information and potentially provides information about the sam-
ple complex refractive index; PTIR provides spectra that depend on absorption and are directly
comparable with IR spectral databases.
The three nanoscale techniques described here produce chemical maps at selected wavelengths
or spectra at selected locations, but the acquisition of full IR spectra at each pixel, and within a
reasonably fast timeframe, has not yet been achieved. It is expected that research will focus on
increasing the SNR by increasing the sensitivity and by reducing noise, thus increasing throughput.
To this end, improvements to laser sources will certainly be beneficial. Another important area to
be addressed for both s-SNOM and PTIR is the interpretation of the signal intensity for samples
presenting material heterogeneity through the sample thickness. The availability of commercial
s-SNOM, PTIR, and RE-AFM-IR setups, combined with the general utility of IR analysis and the
growing breadth of nanomaterials and biological applications, suggests that the field of nanoscale
IR imaging and spectroscopy is poised for rapid growth in both academic and industrial settings.
DISCLOSURE STATEMENT
The authors are not aware of any affiliations, memberships, funding, or financial holdings that
might be perceived as affecting the objectivity of this review.
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Annual Review of
Analytical Chemistry
Volume 8, 2015
Contents
Structure Determination of Natural Products by Mass Spectrometry
Klaus Biemann ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp1
Ionization Mechanism of Matrix-Assisted Laser Desorption/Ionization
I-Chung Lu, Chuping Lee, Yuan-Tseh Lee, and Chi-Kung Ni pppppppppppppppppppppppppppp21
A Thermal Mechanism of Ion Formation in MALDI
Yong Jin Bae and Myung Soo Kim ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp41
Evolution of Orbitrap Mass Spectrometry Instrumentation
Shannon Eliuk and Alexander A. Makarov ppppppppppppppppppppppppppppppppppppppppppppppppp61
Functionalizing Microporous Membranes for Protein Purification
and Protein Digestion
Jinlan Dong and Merlin L. Bruening pppppppppppppppppppppppppppppppppppppppppppppppppppppppp81
Infrared Imaging and Spectroscopy Beyond the Diffraction Limit
Andrea Centrone pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp101
Analytical Aspects of Hydrogen Exchange Mass Spectrometry
John R. Engen and Thomas E. Wales pppppppppppppppppppppppppppppppppppppppppppppppppppppp127
Electronic Biosensors Based on III-Nitride Semiconductors
Ronny Kirste, Nathaniel Rohrbaugh, Isaac Bryan, Zachary Bryan,
Ramon Collazo, and Albena Ivanisevic pppppppppppppppppppppppppppppppppppppppppppppppppp149
Real-Time Monitoring of Critical Care Analytes in the Bloodstream
with Chemical Sensors: Progress and Challenges
Megan C. Frost and Mark E. Meyerhoff pppppppppppppppppppppppppppppppppppppppppppppppppp171
Single-Molecule Investigations of Morphology and Mass Transport
Dynamics in Nanostructured Materials
Daniel A. Higgins, Seok Chan Park, Khanh-Hoa Tran-Ba, and Takashi Ito pppppppppp193
MicroRNA Detection: Current Technology and Research Strategies
Eric A. Hunt, David Broyles, Trajen Head, and Sapna K. Deo pppppppppppppppppppppppppp217
Electrochemical Analysis of Neurotransmitters
Elizabeth S. Bucher and R. Mark Wightman ppppppppppppppppppppppppppppppppppppppppppppp239
v
Annual Review of Analytical Chemistry 2015.8:101-126. Downloaded from www.annualreviews.org
by Dr. Andrea Centrone on 07/30/15. For personal use only.
AC08-FrontMatter ARI 25 June 2015 14:47
Carbon Substrates: A Stable Foundation for Biomolecular Arrays
Matthew R. Lockett and Lloyd M. Smith pppppppppppppppppppppppppppppppppppppppppppppppppp263
Sensor Array Design for Complex Sensing Tasks
Kevin J. Johnson and Susan L. Rose-Pehrsson ppppppppppppppppppppppppppppppppppppppppppppp287
Synthetic Nano- and Micromachines in Analytical Chemistry: Sensing,
Migration, Capture, Delivery, and Separation
Wentao Duan, Wei Wang, Sambeeta Das, Vinita Yadav, Thomas E. Mallouk,
and Ayusman Sen pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp311
Cell-Based Microarrays for In Vitro Toxicology
Joachim Wegener pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp335
Multimodal Imaging Spectroscopy of Tissue
Nadine Vogler, Sandro Heuke, Thomas W. Bocklitz, Michael Schmitt,
and J¨urgen Popp pppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp359
Single-Molecule Electronics: Chemical and Analytical Perspectives
Richard J. Nichols and Simon J. Higgins pppppppppppppppppppppppppppppppppppppppppppppppppp389
Forensic Mass Spectrometry
William D. Hoffmann and Glen P. Jackson ppppppppppppppppppppppppppppppppppppppppppppppp419
Iontronics
Honggu Chun and Taek Dong Chung ppppppppppppppppppppppppppppppppppppppppppppppppppppp441
Carbohydrates on Proteins: Site-Specific Glycosylation Analysis
by Mass Spectrometry
Zhikai Zhu and Heather Desaire ppppppppppppppppppppppppppppppppppppppppppppppppppppppppppp463
Advances in Mass Spectrometric Tools for Probing Neuropeptides
Amanda Buchberger, Qing Yu, and Lingjun Li ppppppppppppppppppppppppppppppppppppppppppp485
Indexes
Cumulative Index of Contributing Authors, Volumes 1–8 pppppppppppppppppppppppppppppp511
Cumulative Index of Article Titles, Volumes 1–8 pppppppppppppppppppppppppppppppppppppppp516
Errata
An online log of corrections to Annual Review of Analytical Chemistry articles may be
found at http://www.annualreviews.org/errata/anchem
vi Contents
Annual Review of Analytical Chemistry 2015.8:101-126. Downloaded from www.annualreviews.org
by Dr. Andrea Centrone on 07/30/15. For personal use only.
