generally expressed in terms of a two time-point time-dependent frequencyfrequency correlation function (FFCF, C 2 (t), Eq. 3), which in most cases is taken to
follow the form of a sum of exponential functions, weighted with amplitudes D i .
However, it is important to note that this assumption is not rigorously valid for all
samples and much more complicated dynamics can exist.
C 2 t 2
ð Þ ¼ hdx 01 t 2
ð Þdx 01 0
ð Þi /
X
i
D i exp Àt 2 =s c;i
À
Á
ð3Þ
Different parameters of 2D IR signals can be used to obtain a measure that is
directly proportional to the FFCF, e.g. the nodal slope between the excited state
absorption and ground state bleach signals, the ellipticity of the signals, the centerline slope (CLS) [10, 69–71]. Which method is the best to characterize the dynamics
should be evaluated on a case-by-case basis to be as accurate as possible [69–71].
Spectral diffusion can take place on time scales of a few tens of femtoseconds
up to hundreds of picoseconds and is thus directly addressable with 2D IR
spectroscopy. Next to the frequently observed exponential behavior of the FFCF,
sometimes quasi-static contributions are determined experimentally, that is,
contributions which have correlation times much longer than the experimentally
accessible temporal range. From a molecular point of view, different mechanisms
are responsible for the observation of spectral diffusion in 2D IR signals. Thermal
motion of molecules in their environment causes collisions of the IR active
functional groups with solvents. The persistent changes in the environmental
conditions (re-orientation, solvation, hydrogen bonding) influence the frequency
of the functional groups and cause dynamic transitions between the frequencies.
Other mechanisms can be structural fluctuations of the sample molecule itself (i.e.
conformational changes, bond rotation/bending) [37]. Finally, also intermolecular
interactions between different functional groups can cause spectral diffusion such
as energy transfer between the same type of oscillators [72]. Different mechanisms
are often active at the same time and contribute to the ultrafast response.
Therefore, a combined approach of experiments and theory, mostly based on
molecular dynamics simulations and density-functional theory geometry optimization, is very helpful in identifying which of the contributions is most
dominant and what the observed time scales tell about molecular dynamics and
properties. In this chapter, different examples for the use of spectral diffusion for
interpreting molecular dynamics are discussed, which focus on molecules in
different environment, i.e. molecules in bulk solution environment, as well as
under different types of dimensional confinement. These examples are discussed
in Sects. 3 and 4
2.1.2 Off-Diagonal Peaks
The real strength of 2D IR spectroscopy is reflected by the possibility to resolve
interactions between IR-active functional groups, and in particular by the possibility
to follow such interactions on the sub-picosecond timescale. Such interactions show
up as cross peaks between different diagonal peaks in a 2D IR spectrum, just as in
Top Curr Chem (Z) (2017) 375:86
123
124
Reprinted from the journal
follow the form of a sum of exponential functions, weighted with amplitudes D i .
However, it is important to note that this assumption is not rigorously valid for all
samples and much more complicated dynamics can exist.
C 2 t 2
ð Þ ¼ hdx 01 t 2
ð Þdx 01 0
ð Þi /
X
i
D i exp Àt 2 =s c;i
À
Á
ð3Þ
Different parameters of 2D IR signals can be used to obtain a measure that is
directly proportional to the FFCF, e.g. the nodal slope between the excited state
absorption and ground state bleach signals, the ellipticity of the signals, the centerline slope (CLS) [10, 69–71]. Which method is the best to characterize the dynamics
should be evaluated on a case-by-case basis to be as accurate as possible [69–71].
Spectral diffusion can take place on time scales of a few tens of femtoseconds
up to hundreds of picoseconds and is thus directly addressable with 2D IR
spectroscopy. Next to the frequently observed exponential behavior of the FFCF,
sometimes quasi-static contributions are determined experimentally, that is,
contributions which have correlation times much longer than the experimentally
accessible temporal range. From a molecular point of view, different mechanisms
are responsible for the observation of spectral diffusion in 2D IR signals. Thermal
motion of molecules in their environment causes collisions of the IR active
functional groups with solvents. The persistent changes in the environmental
conditions (re-orientation, solvation, hydrogen bonding) influence the frequency
of the functional groups and cause dynamic transitions between the frequencies.
Other mechanisms can be structural fluctuations of the sample molecule itself (i.e.
conformational changes, bond rotation/bending) [37]. Finally, also intermolecular
interactions between different functional groups can cause spectral diffusion such
as energy transfer between the same type of oscillators [72]. Different mechanisms
are often active at the same time and contribute to the ultrafast response.
Therefore, a combined approach of experiments and theory, mostly based on
molecular dynamics simulations and density-functional theory geometry optimization, is very helpful in identifying which of the contributions is most
dominant and what the observed time scales tell about molecular dynamics and
properties. In this chapter, different examples for the use of spectral diffusion for
interpreting molecular dynamics are discussed, which focus on molecules in
different environment, i.e. molecules in bulk solution environment, as well as
under different types of dimensional confinement. These examples are discussed
in Sects. 3 and 4
2.1.2 Off-Diagonal Peaks
The real strength of 2D IR spectroscopy is reflected by the possibility to resolve
interactions between IR-active functional groups, and in particular by the possibility
to follow such interactions on the sub-picosecond timescale. Such interactions show
up as cross peaks between different diagonal peaks in a 2D IR spectrum, just as in
Top Curr Chem (Z) (2017) 375:86
123
124
Reprinted from the journal