Translational spectral densities show a universal low-frequency behavior, namely linearity in the square root of frequency, . This is the result of an intrinsic feature of translational diffusion and found for every model as long as P
r0,r,t
(cf. Eq. 18) obeys the diffusion equation, P tD122P, for large distances, rr0, and long times, t, [47]. In the correlation function this feature manifests itself in a power-law behavior Ctranst32 at long times, t, a fact well known for long [52].Figure 13a shows the correlation function of the models discussed for the intermolecular relaxation (cf. Sec 3.1.6) obtained by cosine transform of their spectral densities (cf. Eq. 16).
For comparison an exponential correlation loss is also plotted (red line) which corresponds to a Debye spectral density (Eq. 4). It is the only curve which does not show the translational behavior t32 at long times which is indicated by the dashed line. Even Torrey’s model with a large jump distance (cyan line) finally obeys the power-law after following the rotational correlation function quite long. The curve for the eccentricity model (purple line) shows a correlation loss due to rotation at short times, but eventually also obeys the translational power-law at long times.
10-3 10-2 10-1 100 101 102 103 104 10-7
10-6 10-5 10-4 10-3 10-2 10-1 100
Abragam FFHS Torrey ‹r2›/d2=1
Torrey ‹r2›/d2=10
eccentricity e=0.75, r=9
Debye
Cinter
ttrans
t-3/2
(a)
10-1100 101 102 103 104 105 106 107 108 10-7
10-6 10-5 10-4 10-3 10-2 10-1 100
M=
860 5940 41400 propylene glycol
C DD(t)
t/s
(c)
PDMSFigure 13a: Intermolecular correlation functions versus reduced time, Cinter
t trans
, for the models discussed in Sec. 3.1.6 derived from the spectral densities, Jinter
trans
, via cosine transform (cf. Eq.16), for comparison an exponential correlation loss (rotational diffusion), Crot
t rot
, is also plotted (red line). Figure 13b: Intra- and intermolecular correlation function derived by MD simulation on di(propylene oxide). Taken from Ref. [64]. Figure 13c: Dipolar correlation functions, CDD
t rot
, for PG and PDMS with different molecular masses derived via Eq. 16; dashed lines: power laws t32. Dotted line: Kohlrausch function. Taken from Pub. 6.Due to the growth of computing capacity this behavior C
t t32 at long times could also be identified in recent molecular-dynamics (MD) simulations of liquids and polymers. Figure 13b shows a MD simulation on di(propylene oxide) done by Henritzi et al. [64]. The dashed black line is the intramolecular correlation function, the solid red line the intermolecular correlation function. The former was interpolated by a stretched exponential (Kohlrausch function, cf. Sec. 3.1.1) (green solid line) while the latter was fitted to the correlation function which corresponds to the FFHS model (Eq. 21) (blue dashed-dotted line). The interpolation with the stretched exponential works almost perfectly while the intermolecular part shows slight deviations to the FFHS correlation function. This may be due to the eccentricity effect which is omitted by the FFHS model. However, the most important fact in this context is the crossover of the intermolecular correlation function (red solid line) to the behavior
t t32C at longest times which reflects translational diffusion in the hydrodynamic limit.
In Pub. 6 this power-law is firstly demonstrated at experimental data. Via Eq. 16 the susceptibility master curves of propylene glycol (PG) and of a polymer, poly(dimethyl siloxane) (PDMS) with different molecular masses, M , were transformed into the corresponding dipolar correlation functions, CDD
t rot
. Figure 13c shows the results. At short correlation times the loss is given by a stretched exponential (dotted line) while at sufficient long times the power-law C
t t32 is established for every system. While for the liquid systems (PG, PDMS with M 860g mol) this happens at shorter times, for higher M the crossover gets more and more protracted due to additional polymer dynamics. In Pub. 5, Pub. 6 and Sec. 3.1.9 the polymer dynamics are addressed in more detail.Concerning the spectral densities this feature of the correlation function at long times comes into effect at low frequencies where it is reflected as a first order linear behavior in . For example, the series expansion for the FFHS spectral density (Eq. 21) is as follows [50]:
trans trans trans 32
trans
2 12
1 8
2 1 3 9
4
J (25)
While the first order of the expansion in frequency of any translational spectral density thus is
, a rotational one is 2 as seen exemplarily at the series expansion of a Debye spectral density (Eq. 4):
rot rot 2
rot 1
J (26)
This crucial difference between Jtrans and Jrot is well known for a long time, e.g., it was given by Harmon in 1970 [65]. Due to experimental restrictions it has not been systematically and extensively applied so far. The present work will fill this gap as Pub. 3 – Pub. 6 apply the consequences of this feature to the rate dispersion data of a variety of systems.
By dipole-dipole coupling of a considered nucleus in a bulk liquid to identical nuclei, Jtrans contributes to the translational part of the relaxation rate, R1trans,inter
, via (BPP expression, cf.Eq. 11):
Hereby, NA is the spin density of nuclei A; d denotes the distance of closest approach. When coupled to different nuclei the contribution to R1trans,inter
is as follows (SBM expression, cf. Eq.12):
where NB represents the spin density of nuclei B. As the rotational contribution to the total rate dispersion yet goes with the square in frequency, 2 (cf. Eq. 26), R1
is dominated by the translational contribution at sufficiently low frequencies. Hence, from Eqs. 25, 27 and 28 follows (assuming the additivity of R1trans,inter,AA
and R1trans,inter,AB
):The summation over X accounts for all species of different nuclei (i.e. B, C, …) which undergo dipole-dipole coupling with the nucleus A. We note that Eq. 29 implies that there is no ‘extreme narrowing’ in 1H NMR, i.e. R1
is always frequency dependent even at lowest frequencies. The introduction of the coupling constant cancels out the model specific parameters (d and trans) of Jtrans and the prefactor of the term becomes model independent providing direct access to D. As said, it is also independent of the form of the radial distribution function, g
r , [47]. Thus the first-order behavior of R1
is universal. to the overall rate dispersion at low frequencies (cf. Eqs. 23, 25 - 28):
intra inter
rot inter transAs a result of Eq. 29 one can directly calculate the self-diffusion coefficient from the slope,
d dR1
m , at low frequencies, when simply plotting the relaxation rate against the square root of frequency:
B m
23D (32)
Except physical constants, B only contains the spin densities, NX, which are easily accessible in most cases (cf. Eq. 6 in Pub. 6).
Unlike the treatment given in from Pub. 3 - Pub. 6 where only proton-proton coupling has to be considered (the H-D coupling e.g. in glycerol-h3 (i.e. CD5(OH)3) is negligible as will be shown in Chap. 3.4) the examination given above is improved as it also includes heteronuclear coupling. As further shown exemplarily in Chap. 3.4 the analysis of rate dispersions obtained by 19F NMR for the liquid 3-fluoroaniline requires the full expression in Eq. 29.
As said, the effect of translational diffusion on the relaxation dispersion is well known for a