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Field-cycling NMR relaxometry is a methodology typifying the Wiener/Khinchine theorem of statistical physics of molecular fluctuations in the pure form. The relevant information carrier is the Fourier transform pair ‘autocorrelation function versus spectral density’. It is shown how these characteristics of molecular dynamics can be derived from field-cycling NMR relaxometry experiments or, vice versa, how the features of relaxation dispersion can be predicted based on model considerations. The angular spin transition frequencies adjusted in the experiment via the magnetic flux density are conjugate to the time intervals after which the autocorrelation function is probed. Fundamental principles of the technique are outlined, and the ranges of validity of relaxation formalisms are specified. This includes both technical and physical limits of applications. Examples of molecular fluctuations are discussed in terms of motional restrictions and of limits of exchange between different phases. Systems of particular interest referred to are bulk liquids, polymers, liquid crystals and adsorbate molecules in porous or colloidal media.

The objective of this book is to introduce the reader to the theory of NMR relaxometry, to the field-cycling technique in all its variants, to instrumental aspects, to model concepts of molecular dynamics and to the large variety of applications including their future perspectives. This first chapter is intended to outline essential principles and to draw attention to key issues that sometimes lead to misunderstandings or even misinterpretations. For more comprehensive discussions of certain application aspects of the technique, we will frequently refer to subsequent, more specific chapters.

The primary purpose of field-cycling NMR relaxometry is to study molecular dynamics in condensed materials and systems. Nuclear magnetic relaxation is examined as a function of the angular Larmor frequency ω=2πν=|γ|B0, where γ is the gyromagnetic ratio of the experimentally resonant nuclei and B0 is the external magnetic flux density quantizing the spin states. The phenomenon of interest is normally the frequency dependence (or ‘dispersion’) of spin–lattice relaxation with the time constant T1=T1(ω) or the rate 1/T1R1=R1(ω).

From the theoretical point of view, the technique centres around one of the most fundamental principles of the statistical physics of stationary random processes, namely the Wiener/Khinchine theorem:

formula
Equation 1.1

applied to molecular dynamics. The spectral density (ωk) at the angular frequency ωk is given as the Fourier transform of the autocorrelation function (τ), where τ is the time interval conjugate to ωk. The Wiener/Khinchine theorem links the key information carriers of molecular dynamics, (τ) and (ωk), while the experimental access to these functions is provided by field-cycling NMR relaxometry in an unmatched way.

Later, we will identify ωk with angular transition frequencies in spin systems as far as they are relevant in the present context. Spin–lattice relaxation of coupled ‘like’ spins can be traced back to angular single- and double-quantum transition frequencies ωk=, where k=1, 2 and ω=|γ|B0 is the angular Larmor frequency of the spins. In the case of coupled ‘unlike’ spins with spin quantum numbers I, S, zero-quantum transitions also matter. In this case, the relevant angular transition frequencies are ω0=|ωSωI|, ω1=ω1 and ω2=ωS+ωI, where ωI=|γI|B0 is the angular Larmor frequency of the resonant spins I, and ωS=|γS|B0 that of the coupling partners that are off-resonant in the experiment. For an illustration, see Figure 1.1.

Figure 1.1

Zeeman energy levels of spin systems in a quantizing field B⃑0 (assumed to point upwards). The double arrows between the levels indicate the (allowed) zero-, single-, and double-quantum transitions relevant for spin relaxation. The spectral densities (ωk) of the fluctuations inducing these transitions are indicated. In the schemes, all gyromagnetic ratios are assumed to be positive. (a) Dipolar coupled ‘like’ spin pairs with quantum numbers I=½, S=½, magnetic quantum numbers mI, mS and a common gyromagnetic ratio γ. The spin eigenstates are symbolized by kets |↑↑〉, |↑↓〉, |↓↑〉 and |↓↓〉 for the diverse combinations of spin-up and spin-down states relative to the vector B⃑0. The Zeeman eigenenergies are , where ω=γB0 is the angular Larmor frequency. The angular transition frequencies are ωk= for zero- (k=0), single- (k=1) and double- (k=2) quantum transitions. Typical examples are protons in organic materials. (b) Pairs of dipolar coupled ‘unlike’ spins ½ having different gyromagnetic ratios γIγS and Larmor frequencies ωI=γIB0 and ωS=γSB0. The Zeeman eigenenergies are . The angular transition frequencies are ωk=|ΔEk|/ħ for zero- (ω0=|ωSωI|), single- (ω1=ωI) and double- (ω2=ωS+ωI) quantum transitions. Typical examples are protons with spins I coupled to unpaired electrons with spin S. (c) (Single) spins 1 subjected to quadrupole interaction in the high-field limit. Quadrupolar coupled spin-1 particles have three Zeeman eigenstates with the kets |m=1〉, |m=0〉 and |m=−1〉 and energies Em=−mħω. The angular Larmor frequency is ω=γB0 as before. The angular transition frequencies are ωk=|ΔEk|/ħ= for single- (k=1) and double- (k=2) quantum transitions. Typical examples are deuterons.

Figure 1.1

Zeeman energy levels of spin systems in a quantizing field B⃑0 (assumed to point upwards). The double arrows between the levels indicate the (allowed) zero-, single-, and double-quantum transitions relevant for spin relaxation. The spectral densities (ωk) of the fluctuations inducing these transitions are indicated. In the schemes, all gyromagnetic ratios are assumed to be positive. (a) Dipolar coupled ‘like’ spin pairs with quantum numbers I=½, S=½, magnetic quantum numbers mI, mS and a common gyromagnetic ratio γ. The spin eigenstates are symbolized by kets |↑↑〉, |↑↓〉, |↓↑〉 and |↓↓〉 for the diverse combinations of spin-up and spin-down states relative to the vector B⃑0. The Zeeman eigenenergies are , where ω=γB0 is the angular Larmor frequency. The angular transition frequencies are ωk= for zero- (k=0), single- (k=1) and double- (k=2) quantum transitions. Typical examples are protons in organic materials. (b) Pairs of dipolar coupled ‘unlike’ spins ½ having different gyromagnetic ratios γIγS and Larmor frequencies ωI=γIB0 and ωS=γSB0. The Zeeman eigenenergies are . The angular transition frequencies are ωk=|ΔEk|/ħ for zero- (ω0=|ωSωI|), single- (ω1=ωI) and double- (ω2=ωS+ωI) quantum transitions. Typical examples are protons with spins I coupled to unpaired electrons with spin S. (c) (Single) spins 1 subjected to quadrupole interaction in the high-field limit. Quadrupolar coupled spin-1 particles have three Zeeman eigenstates with the kets |m=1〉, |m=0〉 and |m=−1〉 and energies Em=−mħω. The angular Larmor frequency is ω=γB0 as before. The angular transition frequencies are ωk=|ΔEk|/ħ= for single- (k=1) and double- (k=2) quantum transitions. Typical examples are deuterons.

Close modal

The meaning of the functions (τ) and (ωk) in terms of parameters of molecular dynamics will be specified below in more detail. Generally, a function F(t) is defined characterizing molecular orientations and neighbour distances in terms of thermally fluctuating spherical coordinates r(t), φ(t), ϑ(t) [see Figure 1.2 and eqn (1.10) for fluctuations of dipolar couplings]. The autocorrelation function thereof is defined by

formula
Equation 1.2

in its ‘normalized’ or ‘reduced’ form with the initial value (τ=0)=1. The asterisk indicates that the conjugate complex since the function F(t) may be complex1. The angular brackets indicate averages for ensembles of molecules.

Figure 1.2

Schematic representation of the interrelations of molecular motions, field-cycling NMR relaxometry experiments and theoretical model concepts. Dipolar couplings between two dipoles with the vector operators I=γIħI⃑ and S=γSħS⃑ depend on the inter-dipole vector r⃑. If I and S are identical, one speaks of ‘like’ spins. The cones symbolize precession about the external flux density B⃑0. r⃑ can be expressed in spherical coordinates r(t), ϕ(t), ϑ(t) fluctuating as a consequence of molecular dynamics. For dipolar couplings, the autocorrelation function (τ) is formed on the basis of the functions Fd(k){r(t), φ(t), ϑ(t)} [see eqn (1.10)]. Note that the functions Fd(k) depend on the absolute time t whereas the autocorrelation function varies with the time interval τ. The spectral densities (ωk) are Fourier transforms of (τ) for the angular frequencies ωk. They determine the spin–lattice relaxation rate as a linear combination for all allowed angular transition frequencies ωk in the spin system under consideration. Predictions based on a theoretical model can be compared with experimental field-cycling NMR relaxometry data. The other way round, dispersion features of the spectral density deduced from experimental data can be taken as conditions to be fulfilled by dynamic models in question.

Figure 1.2

Schematic representation of the interrelations of molecular motions, field-cycling NMR relaxometry experiments and theoretical model concepts. Dipolar couplings between two dipoles with the vector operators I=γIħI⃑ and S=γSħS⃑ depend on the inter-dipole vector r⃑. If I and S are identical, one speaks of ‘like’ spins. The cones symbolize precession about the external flux density B⃑0. r⃑ can be expressed in spherical coordinates r(t), ϕ(t), ϑ(t) fluctuating as a consequence of molecular dynamics. For dipolar couplings, the autocorrelation function (τ) is formed on the basis of the functions Fd(k){r(t), φ(t), ϑ(t)} [see eqn (1.10)]. Note that the functions Fd(k) depend on the absolute time t whereas the autocorrelation function varies with the time interval τ. The spectral densities (ωk) are Fourier transforms of (τ) for the angular frequencies ωk. They determine the spin–lattice relaxation rate as a linear combination for all allowed angular transition frequencies ωk in the spin system under consideration. Predictions based on a theoretical model can be compared with experimental field-cycling NMR relaxometry data. The other way round, dispersion features of the spectral density deduced from experimental data can be taken as conditions to be fulfilled by dynamic models in question.

Close modal

The definition eqn (1.2) implies an important feature of molecular fluctuations at thermal equilibrium, namely stationarity. That is, the autocorrelation function depends on the interval τ, but does not depend on the absolute time t. We can therefore set t=0 without loss of generality. Eqn (1.2) can thus be rewritten in the usual form as2

formula
Equation 1.3

A further important property of stochastic processes is the invariance upon time reversal. The autocorrelation function is not a matter of the question of which of the two times under consideration is before or after. That is,

(τ)=(−τ)
Equation 1.4

The time scale of correlation functions is characterized by the correlation time:

formula
Equation 1.5

A simple (but not necessarily realistic) example of an autocorrelation functions is the monoexponential decay:

formula
Equation 1.6

which is Fourier conjugate to the Lorentzian spectral density:

formula
Equation 1.7

Figure 1.3 shows a graphical representation of eqn (1.7). Note that here and in general it is important not to forget the magnitude bars in the expression for the autocorrelation function in order to avoid conflicts with the Wiener/Khinchine theorem eqn (1.1). The magnitude bars so to speak warrant the time-reversal invariance condition eqn (1.4).3

Figure 1.3

Graphical representation of a Lorentzian spectral density, eqn (1.7), as the Fourier transform of monoexponential autocorrelation functions, eqn (1.6), for different values of the correlation time τc. The crossover from the plateau (ωkτc≪1)≈2τc at low angular frequencies to the limit (ωkτc≫1)≈2/(ωk2τc) at high angular frequencies occurs around the positions ωk=τc−1. Note that for ωk<τc−1, the spectral density (ωk) increases with increasing values of τc and decreases in the opposite case ωk>τc−1. This is exemplified by the vertical lines and the dots at two angular frequencies complying with the respective conditions ωa<τc−1 and ωb>τc−1 in the frame of consideration here. Qualitatively, this behaviour applies generally to all stochastic processes irrespective of the actual shape of the autocorrelation function. With respect to field-cycling NMR relaxometry, this means that spin–lattice relaxation rates 1/T1increase with longer τc values (i.e. slower fluctuations) for ωkτc<1 whereas they decrease for ωkτc>1.

Figure 1.3

Graphical representation of a Lorentzian spectral density, eqn (1.7), as the Fourier transform of monoexponential autocorrelation functions, eqn (1.6), for different values of the correlation time τc. The crossover from the plateau (ωkτc≪1)≈2τc at low angular frequencies to the limit (ωkτc≫1)≈2/(ωk2τc) at high angular frequencies occurs around the positions ωk=τc−1. Note that for ωk<τc−1, the spectral density (ωk) increases with increasing values of τc and decreases in the opposite case ωk>τc−1. This is exemplified by the vertical lines and the dots at two angular frequencies complying with the respective conditions ωa<τc−1 and ωb>τc−1 in the frame of consideration here. Qualitatively, this behaviour applies generally to all stochastic processes irrespective of the actual shape of the autocorrelation function. With respect to field-cycling NMR relaxometry, this means that spin–lattice relaxation rates 1/T1increase with longer τc values (i.e. slower fluctuations) for ωkτc<1 whereas they decrease for ωkτc>1.

Close modal

Figure 1.2 shows a scheme of how experimental field-cycling NMR relaxometry results are interrelated with models for molecular dynamics. From empirical data for the spin–lattice relaxation dispersion, conclusions can be drawn concerning spectral densities (ωk), whereas – the other way round – theoretical model treatments permit one to predict features of autocorrelation functions (τ) and, on this basis, what dispersion features are to be expected in experiments. Prior to any detailed data analysis, the time scale of molecular dynamics can directly be estimated from the dispersion range. For instance, if a finite dispersion slope is observed down to the lower end of the available frequency window, one knows that correlations exist longer than the inverse angular frequency ωκ of the spin transition leading in that magnetic-field regime. A more specific discussion of this point follows in Section 1.1.2.

Most applications of field-cycling NMR relaxometry refer to scenarios where the standard formalism for nuclear spin relaxation applies. It is referred to as BWR (Bloch–Wangsness–Redfield) theory. The principle is as follows: molecular dynamics, i.e. rotational and translational Brownian motions, cause fluctuations of spin interactions, which in turn induce spin transitions. In the present context, dipole–dipole couplings and/or quadrupole interactions with molecular electric field gradients are of particular importance depending on the particle species under consideration. Spin interactions are treated as perturbations of the (much larger) Zeeman interaction with the external magnetic flux density B0. Starting with a state of the spin ensemble initially at non-equilibrium, the fluctuating perturbations will induce spin transitions causing the evolution towards thermal equilibrium4.

The Hamiltonian of dipole–dipole couplings among a spin pair can be analysed in terms of linear combinations of products of spin operator expressions d(k) and spatial functions Fd(k)(r, φ, ϑ):

formula
Equation 1.8

where

formula
Equation 1.9

is a constant characteristic for the coupled spin pairs (μ0, magnetic field constant; ħ, Planck's constant divided by 2π; γI and γS, gyromagnetic ratios of the coupled dipoles with spin quantum numbers I and S, respectively). The functions Fd(k)(r, φ, ϑ) depend on the (fluctuating) spherical coordinates r, φ, ϑ of the distance vector (see Figure 1.2). They are special versions of the function F in eqn (1.2) for dipolar couplings. These functions characterize the position- and orientation-dependent strength of dipolar couplings and are related to second-degree spherical harmonics according to

formula
Equation 1.10

Based on fluctuations of the spherical coordinates r, φ, ϑ – and hence of the spatial functions Fd(k)(r, φ, ϑ) – the respective spin operator terms d(k) induce the allowed spin transitions indicated in Figure 1.1a or b.

The autocorrelation functions, eqn (1.3), for dipolar coupled spin pairs read

formula
Equation 1.11

for the zero- (k=0), single- (k=±1) and double- (k=±2) quantum spin transitions defined in Figure 1.1a and b. In the case of exclusively rotational fluctuations, that is, for fixed (intramolecular) inter-dipole distances r, these expressions can be reduced to autocorrelation functions of second-degree spherical harmonics:

k(τ)≈(−1)k4π〈Y2,k (0) Y2,−k (τ)〉
Equation 1.12

In disordered liquid systems where molecular reorientations are not restricted significantly by topological or steric constraints, the autocorrelation functions turn out to be independent of the order k, so that [compare eqn (1.75)]

0(τ)=1(τ)=2(τ)  (τ)
Equation 1.13

The corresponding spectral densities (0), (ω) and (2ω) follow from eqn (1.1).

For dipolar coupled pairs of ‘like’ spins defined by identical gyromagnetic ratios, γI=γSγ, the BWR theory predicts the spin–lattice relaxation rate:

formula
Equation 1.14

where ω=|γ|B0 is the angular Larmor frequency.

The result for dipolar coupled pairs of ‘unlike’ spins with quantum numbers I (resonant), S (off-resonant) and gyromagnetic ratios γI ≠ γS is5

formula
Equation 1.15

The angular Larmor frequencies for the two spin species are ωI=|γI|B0 and ωS=|γS|B0. Detailed descriptions, definitions and derivations of eqn (1.8)–(1.15) can be found in ref. 2–7, for instance.

Eqn (1.14) and (1.15) have been derived for a number of important premises that need to be commented upon:

  1. The time scale of molecular fluctuations relevant for spin–lattice relaxation is limited by that of T1. For slower motions, the BWR theory does not apply. This is expressed by the so-called Redfield limit T1τc in terms of correlation times. Since T1 is smallest at angular frequencies ωk ≤ τc−1, i.e. where (ωk) is largest (see Figure 1.3), the Redfield condition can also be expressed by T1ωk−1 with respect to the angular spin transition frequency ωk dominating in eqn (1.14) or (1.15) under the experimental conditions.

  2. In principle, these equations are valid for ensembles of isolated, i.e. independently fluctuating, two-spin systems. This assumption conflicts with the multi-spin composition of the materials of interest here. There are two reasons why the BWR theory nevertheless works: the dipolar Hamiltonian eqn (1.8) actually couples only two particles, so that the total dipolar Hamiltonian of a multi-spin system is composed of the sum of mutual two-spin interaction terms. A superposition of two-spin Hamiltonians complicates the treatment of the relaxation mechanism only if the fluctuations of two-spin couplings are correlated (as one would suspect for intramolecular multi-spin systems). Correlation effects are indeed perceptible in high-field, high-resolution NMR spectroscopy.8,9  However, under the low-field conditions typical for field-cycling NMR relaxometry, such phenomena will scarcely influence the relaxation behaviour, even for practically rigid atomic arrangements such as methyl groups10  or alkenes. Multi-spin systems can therefore be modelled as a set of independently fluctuating two-spin systems with an accuracy better than experimental errors.11  Spin–lattice relaxation of dipolar coupled, multi-spin systems is thus represented by a sum of independent two-spin relaxation rates 1/T1(i):
    formula
    Equation 1.16
    where the index i runs over all coupling partners with which a resonant spin interacts at a time. Some care should nevertheless be taken if field cycling is combined with high-field high-resolution NMR spectroscopy (see Chapters 15 and 21).
  3. Eqn (1.14) and (1.15) hold for fixed dipole–dipole distances r, i.e. for intramolecular interactions fluctuating as a consequence of rotational diffusion of the spin-bearing molecule. However, dipolar couplings between spins located on different molecules may also be significant. This intermolecular dipolar interaction can give rise to a further relaxation contribution 1/T1inter in addition to the intramolecular rate 1/T1intra:
    formula
    Equation 1.17
    The intermolecular relaxation rate is based on fluctuations of the intermolecular inter-dipole distance r⃑=r⃑(t) due to translational diffusion and – to a minor extent – possibly also by rotational diffusion. For further details, see Sections 1.1.1.3 and 1.1.1.4.
  4. The rotational and translational fluctuations referred to so far govern molecular dynamics in liquid-like systems. In solid-like materials such as immobilized macromolecules of synthetic or biological origin, the pervasive fluctuation process may rather be vibrational dynamics (see Chapter 9) and/or diffusion of microstructural defects.5,14–16  A special case of this sort is collective vibration phenomena in field- or surface-ordered liquid crystals called order director fluctuation17  (see Chapter 11 in this book and Chapter 6 in ref. 5).

  5. The numerical factors weighting the spectral densities (ωk) in eqn (1.14) and (1.15) at the allowed spin transition frequencies ωk have been calculated for unrestricted rotational diffusion of the molecules on the time scale of spin–lattice relaxation (see Section 1.3.1). The averages 〈|Fd(k)|2〉 refer to the whole variation range of the angles defined in Figure 1.2, i.e. 0≤φ≤2π and 0≤ϑ≤π, while r is assumed to be fixed. The results are related as follows:
    formula
    Equation 1.18
    The prerequisite of unrestricted molecular reorientations on the relaxation time scale will be violated in ordered systems such as liquid crystals or in materials implying strong reorientation constraints such as polymers.5,18  Strictly, eqn (1.18) will then no longer apply. Effects on this basis can be demonstrated by comparing field-cycling NMR relaxometry data 1/T1(ω) with data measured with the aid of spin–lattice relaxation in the rotating frame, 1/T1ρ(ω1). This rate refers to the angular frequency ω1=|γ|B1, where B1 is the amplitude of the rotating radiofrequency (rf) flux density (see Chapter 7 and ref. 7). On the other hand, strongly constrained reorientation processes are often accompanied by superimposed faster components, reducing the effective spatial restrictions substantially. Taken as a whole, reorientations will then be largely unrestricted, and eqn (1.18) will be a good approach.

The second type of spin interaction of major interest is the coupling of nuclear electric quadrupoles to electric field gradients produced by asymmetric charge distributions in molecules. Analogously to the dipolar Hamiltonian eqn (1.8), the Hamiltonian of a nucleus with a spin quantum number I≥1 and a quadrupole moment Q interacting with an effectively rotationally symmetric electric field gradient can be expressed by7 

formula
Equation 1.19

The constant fq=e2qQ/[8I(2I−1)] characterizes the nuclear species and the strength of the electric field gradient in the molecule (e, positive elementary charge; q33/e, largest field-gradient component divided by e). The operators d(k) represent spin operator terms responsible for allowed spin transitions, i.e. single- and double-quantum transitions (see Figure 1.1c). The spatial functions Fq(k)(ϑ) depend on the polar angle ϑ defined by the orientation of the quantizing field B⃑0 relative to the principal axis system of the field-gradient tensor. Note that this definition deviates from that for dipolar couplings, as illustrated in Figure 1.2. However, in both cases, the polar angle ϑ fluctuates as a consequence of rotational diffusion relative to the laboratory frame. Owing to the rotational symmetry of the electric field-gradient tensor anticipated here, the azimuth angle does not matter and can arbitrarily be set as φ=0:

formula
Equation 1.20

Of these expressions, the second and third are relevant for spin–lattice relaxation: The spin operators q(±1) and q(±2) produce the transitions illustrated in Figure 1.1c. Eqn (1.11)–(1.13) apply in an analogous way again. The spin–lattice relaxation rate of quadrupolar coupled spins 1 in rotationally symmetric electric field gradients is thus found to obey

formula
Equation 1.21

Detailed derivations can be found in ref. 2–7, for instance.

Two remarks referring mainly to deuterons (I=1) may be appropriate in this context:

  1. As in the dipolar coupling case, the Redfield limit requiring T1ω−1 applies for the applicability of eqn (1.21).

  2. Quadrupole couplings to electric field gradients in molecules are relatively strong, so that dipolar interactions from deuteron to deuteron or from resonant deuterons to protons or – at moderate concentrations – from resonant deuterons to electron paramagnetic centres are normally negligible. Deuteron spin–lattice relaxation therefore reflects single-spin – and hence intramolecular – phenomena.

In condensed matter consisting of multi-spin molecules, which is the case in practically all materials of interest here, we have a superposition of intramolecular and intermolecular spin–lattice relaxation rates as expressed by eqn (1.17). Intermolecular relaxation can refer to fluctuating couplings to both ‘like’ and ‘unlike’ dipoles located on different molecules. The latter interaction partners may also include electron paramagnetic ions or centres. An intra/inter distinction is important if, for instance, there are doubts about whether relaxation in aqueous systems is governed by intramolecular proton–proton couplings or by intermolecular interactions with electron paramagnetic ions. Cases of this or similar sorts raise the question of how to distinguish and quantify the two contributions in experiments. There are diverse scenarios that will be discussed one by one.

  1. The simplest situation arises if spin–lattice relaxation dispersion data for deuterons are available for comparison with proton data of the same chemical system. Eqn (1.16) for multi-spin interactions and eqn (1.17) for intermolecular contributions are insignificant in the deuteron case (provided that there is no excessive abundance of electron paramagnetic centres). Deuteron relaxation is therefore an intrinsically intramolecular and single-spin mechanism. Such a comparison has been exemplified in ref. 13, where it was shown that the low-frequency relaxation mechanism in water confined in silica porous glasses is of an exclusively intramolecular nature.

  2. For proton resonance of non-exchangeable hydrogen atoms, intermolecular proton–proton couplings can be reduced by isotopic dilution, that is, by mixing perdeuterated and undeuterated homologues. The gyromagnetic ratio of deuterons is 6.5 times smaller than that of protons. The spin–lattice relaxation rate of protons that are dipolar coupled to deuterons will therefore be reduced by a factor of 42 relative to homonuclear proton systems [see the quadratic prefactor of eqn (1.15)]. In this way, the term 1/T1intra in eqn (1.17) can be discriminated from 1/T1inter. This method has been exploited, e.g., for studies of translational diffusion in polymers (see Section 1.1.1.4, Chapters 8 and 13 and ref. 12). Note that intermolecular couplings tend to fluctuate more slowly than the intramolecular counterpart. As a consequence, they will reveal themselves particularly at low frequencies, and can then even dominate.

  3. If exchangeable hydrogen atoms are probed in the experiment – the simplest example of this sort is water – isotopic dilution in principle affects both intra- and intermolecular couplings. However, a closer analysis reveals that the effect in aqueous systems will be tendentially just the opposite of that discussed above. The isotope exchange after mixing light and heavy water will be complete after a few milliseconds at neutral pH (or pD). The distribution of protons and deuterons can then be assessed as follows: Let x be the fraction of H atoms. The fraction of D atoms is consequently 1−x. We thus have the respective fractions x2, 2x(1−x) and (1−x)2 of H2O, HDO and D2O molecules. A fraction x=1/4, for instance, results in a distribution ratio of 1 : 6 : 9 for H2O, HDO and D2O molecules, which means six times more HDO molecules than H2O. The proton spin–lattice relaxation rate of HD spin pairs is only a fraction of 1/42 of that of HH pairs as mentioned above. Proton–deuteron couplings can therefore be neglected for proton relaxation irrespective of the intra- or intermolecular cases, provided that the proton fraction x is not too small6. It remains to compare the contributions of intramolecular couplings in the residual H2O molecules with those of intermolecular interactions of all H nuclei in both HDO and H2O molecules. In the example above, the intramolecular H–H contribution is reduced by a factor of x2=1/16 relative to undeuterated water, and that of intermolecular H–H relaxation is diminished by a factor of 2x2+2x(1−x)=1/2. The first term refers to the likelihood of a given water proton finding a proton coupling partner in an H2O molecule in its vicinity and the second expression to that of facing a proton of an HDO molecule. That is, intermolecular H–H spin–lattice relaxation will dominate over the intramolecular H–H contribution. This holds in terms of numbers of available proton interaction partners, and is supported by the relaxation efficiency at sufficiently low frequencies. An exception to this rule is the RMTD process to be described in the next paragraph.

  4. A third scenario concerning intra- and intermolecular spin–lattice relaxation has an amazing consequence: rotational fluctuations of intramolecular couplings can also be the indirect consequence of translational diffusion. A typical example is the migration of adsorbate molecules along adsorbent surfaces. Being adsorbed, the molecules will adopt a certain preferential orientation relative to the local surface topology (compare Figure 1.7c, Chapter 12 and ref. 5, 12 and 13). Starting from the adsorbed state, a molecule can be desorbed and – after an excursion to the bulk medium – be readsorbed. This process can occur repeatedly during the interval τ considered for the autocorrelation function decay. The crucial point is now that the initial orientation will be reconstituted at the final position subject to the degree of topological correlation between the initial and final adsorption sites. This process is referred to as reorientation mediated by translational displacements (RMTD)7. The startling feature of this recovery process is that it selectively applies to the correlation of intramolecular spin interactions, but not to intermolecular interactions. The initial correlation of intermolecular couplings among adsorbate molecules will soon and finally decay via translational diffusion, while intramolecular couplings are re-established subject to readsorption at sites with correlated surface orientations. This is the explanation of why intermolecular correlations do not influence the proton spin–lattice relaxation dispersion of water in porous glasses, as already mentioned. In that example, the intermolecular correlation will decay on a time scale on the order of 10−11 s near room temperature, whereas the intramolecular correlation can persist over 10−5 s or more.13  Intramolecular relaxation will therefore dominate in the frequency window of the field-cycling technique. Further features of the RMTD process are discussed in Section 1.2.2.4.

As reviewed in ref. 12, field-cycling NMR relaxometry can be employed for the determination of mean-square displacements by translational diffusion. The time scale ranges from nanoseconds to milliseconds and – if combined with conventional field-gradient NMR diffusometry5,19,20  – up to seconds. The respective information is included in the intermolecular proton spin–lattice relaxation rate 1/T1inter(ω). The primary problem to be solved is therefore to extract the intermolecular rate from data for the total rate 1/T1(ω) [eqn (1.17)]. This objective can be reached with the aid of isotopic dilution experiments already discussed in the previous Section for non-exchanging proton systems. Diminishing intermolecular dipolar couplings in this way permits one to evaluate the intramolecular contribution 1/T1intra(ω) to proton spin–lattice relaxation. Subtracting this from the total rate eqn (1.17) provides the desired data sets for 1/T1inter(ω) (compare Chapters 8 and 13).

The autocorrelation function for intermolecular dipolar couplings can be defined as

formula
Equation 1.22

where c0=16π/5, c1=8π/15 and c2=32π/15 [see eqn (1.10)]. The (un-normalized) autocorrelation functions to be evaluated for intermolecular dipolar couplings are

formula
Equation 1.23

Under effectively isotropic conditions, we can equate

g0inter(τ)=g1inter(τ)=g2inter(τ)≡ginter(τ)
Equation 1.24

in analogy with eqn (1.13). The spectral densities associated with g0inter(τ) are given by

formula
Equation 1.25

Note that the symbols for the correlation function and the spectral density deviate from those used above for intramolecular interactions. This is due to the fact that these functions are not ‘normalized’ in the case of intermolecular couplings. The intermolecular proton–proton spin–lattice relaxation rate thus reads

formula
Equation 1.26

The version for protons (I=½) coupled to deuterons (S=1) is obtained by converting eqn (1.15) to

formula
Equation 1.27

At this point, we should add some comments on eqn (1.26) and (1.27) in addition to those on eqn (1.14) and (1.15):

  1. The fluctuations of intermolecular dipolar couplings are not exclusively of a translational character, but will also depend on rotational diffusion to some minor extent if the interacting dipoles are not centred in the molecules (eccentricity effect).12 

  2. Having acquired data for intermolecular spin–lattice relaxation rates from isotopic dilution experiments as outlined above, the following question arises: how can we express translational diffusion properties in terms of these relaxation rates? This in particular refers to the second moment of the propagator, i.e. the mean-square displacement 〈ρ2rel of the diffusing particles relative to each other. In cases where disordered microstructural constraints substantially limit translational displacements, subdiffusive time dependences characterized by power laws can be expected:5 
    ρ2rel=κτα (0<α<1)
    Equation 1.28
    where κ is a constant. Examples are random percolation networks in porous media21  and segment diffusion in polymer melts (see Chapters 8 and 13). Provided that the exponent obeys α<2/3, the power law eqn (1.28) will be reflected by a conjugated power law for the dispersion of the intermolecular spin–lattice relaxation rate:12 
    formula
    Equation 1.29
    The relation between the conjugated exponents α and β is
    formula
    Equation 1.30
    The restriction α<2/3 stipulates that both the time dependence of the mean-square displacement and the spin–lattice relaxation dispersion are power laws. In this case, one can directly relate the relative mean-square displacement and the spin–lattice relaxation time:
    formula
    Equation 1.31
    where Г(x) is Euler's gamma function and n is the number density of protons. The mean-square displacement of independently diffusing, free molecules relative to the laboratory frame is half of the relative mean-square displacement, i.e.ρ2(τ)〉=½〈ρrel2(τ)〉.

The conjugated variables of the Fourier transform between autocorrelation function and spectral density [eqn (1.1)] are time and the relevant angular spin transition frequency: . The parameter to be examined in NMR relaxometry is the angular Larmor frequency ω=|γ|B0 of the resonant spins. Spin–lattice relaxation of like-spin or single-spin systems results from single- and double-quantum transitions, as suggested by eqn (1.14) and (1.21), i.e. ω1=ω and ω2=2ω, respectively. For time-scale considerations, we may crudely equate Larmor and spin transition frequencies: ωωk=1,2. The time interval after which the autocorrelation function is probed can thus be estimated as

τω−1
Equation 1.32

In the case of ‘unlike’ spins [see eqn (1.15) and (1.27)], the situation is more complicated since two different Larmor frequencies count, ωI=|γI|B0 and ωS=|γS|B0. A typical example is coupled pairs of protons (spin I) and unpaired electrons (spin S). Since ωS ≈ 662ωI, we can approximate (|ωIωS|)≈(|ωI+ωS|)≈(ωS). The question is then which of the two spectral densities (ωI) and (ωS) dominates spin–lattice relaxation of the I spins at the measuring frequency ωI. Actually, this is a matter of the correlation time τc effective under the experimental conditions. (ωS) will dominate for ωSτc≤1 (which concomitantly means ωIτc≪1). The time interval probed in the experiment will then be

τωS−1
Equation 1.33

Likewise, if ωSτc≤1 applies while ωIτc≫1, the relevant time interval will be

τωI−1
Equation 1.34

In the light of the above, statements concerning time scales can be made straightaway from spin–lattice relaxation dispersion curves without any model consideration. For example, a finite dispersion slope at 10 kHz indicates that correlations persist for periods longer than τ≥(2π×10 kHz)−1≈1.6×10−5 s in the ‘like’ spin case. For ‘unlike’ spin pairs of resonant protons and unpaired electrons and if (ωS) dominates, the same dispersion features mean, however, τ≥2×10−8 s. A rule of thumb is that as long as there is a finite slope of the spin–lattice relaxation dispersion, some correlation of the fluctuating interactions is retained after intervals τωk−1, where the subscript k indicates the leading spin transition at the current value of the external magnetic flux density.

The main purpose of the field-cycling NMR relaxometry technique22–25  is to measure the frequency (or field) dependence of spin relaxation parameters in as wide a range as possible. Let us first describe the measuring principle and then turn to the limits and implications of such experiments.

The thermal equilibrium of an ensemble of spins at sufficiently high temperatures is characterized by Curie's law for the magnetization:

formula
Equation 1.35

The experimental variables are the external quantizing flux density B⃑0 (in principle as a vector) and the absolute temperature T. The quantity n is the number density of particles bearing spins with quantum numbers I and kB is Boltzmann's constant.

An NMR relaxation experiment begins after an abrupt perturbation of the equilibrium magnetization. That is, the initial magnetization deviates from the Curie magnetization: M⃑(0)≠M⃑0. The perturbation can be an rf pulse or – in the field-cycling case – a sharp change of B⃑0, or both in combination.

Figure 1.4 shows a scheme of a typical (pre-polarizing) field cycle of the external field B0=B0(t). Other variants are discussed in Chapters 4, 6 and 16. After polarization of the sample by a flux density Bp, the relaxation process of interest starts in the relaxation interval with a flux density Br. After a variable delay, the flux density is switched to the detection value Bd. An NMR signal is induced with the aid of a 90° rf pulse or a spin-echo pulse sequence. The signal amplitude will then be proportional to the magnetization retained at the end of the relaxation interval.

Figure 1.4

Typical specifications of the flux density variation in pre-polarized field-cycling NMR relaxometry experiments (the partial absence of quantitative numbers before the units is to be understood as ‘several’). A non-equilibrium magnetization is produced by rapid switching from the polarization to the relaxation field and – after a relaxation interval – to the detection field Bd. The relaxation curve for the flux density Br is probed point-by-point by varying the length of that interval. The signal is induced with the aid of a 90° rf pulse or a spin-echo pulse sequence. The signal amplitude is proportional to the magnetization retained at the end of the relaxation interval. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV.

Figure 1.4

Typical specifications of the flux density variation in pre-polarized field-cycling NMR relaxometry experiments (the partial absence of quantitative numbers before the units is to be understood as ‘several’). A non-equilibrium magnetization is produced by rapid switching from the polarization to the relaxation field and – after a relaxation interval – to the detection field Bd. The relaxation curve for the flux density Br is probed point-by-point by varying the length of that interval. The signal is induced with the aid of a 90° rf pulse or a spin-echo pulse sequence. The signal amplitude is proportional to the magnetization retained at the end of the relaxation interval. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV.

Close modal

The flux density of the detection field is chosen as high as possible and should be as homogeneous as technically feasible for better sensitivity (see Chapter 3). Since signal acquisition takes only a few milliseconds, the detection field period can accordingly be kept short. As a consequence, the detection flux density can be particularly strong without thermally overloading the magnet coil during its duty cycle.

After signal acquisition, the flux density is switched back to the polarization field value. Allowing for an equilibrium recovery delay, the field cycle can be re-run with incremented relaxation intervals τr as often as needed for the point-by-point acquisition of the relaxation curve at the flux density Br. To obtain the whole spin–lattice relaxation dispersion curve, Br is stepped through a series of discrete values spread over the desired range. Br is usually expressed in terms of the angular Larmor frequency ω=2πν=|γ|Br of the resonant spins.

Field cycling permits one to vary the magnetic flux density Br while the detection field, i.e. the carrier frequency of the NMR spectrometer, is kept constant. The rf unit remains permanently tuned to a fixed, predetermined frequency, i.e. to the resonance frequency νd=|γ|Bd/2π, where γ is the gyromagnetic ratio of the resonant nuclei. The advantage is obvious: the rf part of the system can be optimized for the resonance frequency at the flux density Bd, while the relaxation field Br is variable in the whole range down to lowest values feasible.

Neglecting relaxation losses during the switching down and settling time for the moment, the magnetization at the beginning of the relaxation interval is given by

M(0)=Mz(0)≈M0(Bp)
Equation 1.36

for the pre-polarizing field-cycle represented by Figure 1.4. M0(Bp) is the Curie magnetization for the flux density Bp. The magnetization then relaxes towards the new Curie magnetization in the relaxation field, M0(Br). Based on Bloch's equation for the z component, the magnetization decays according to

Mz(τr)=M0(Br)+[M0(Bp)−M0(Br)]exp[−τr/T1(Br)]
Equation 1.37

where Mz(τr) is the longitudinal magnetization at the end of the relaxation interval τr. This measurand decays from the Curie magnetization M0(Bp) in the polarization field Bp, i.e. Mz(τr=0)=M0(Bp), to the Curie magnetization M0(Br) in the relaxation field Br, that is, Mz(τr→∞)=M0(Br).

If the relaxation flux density Br of interest approaches the value of the flux density Bp, the dynamic range of magnetization variation, i.e. [M0(Bp)−M0(Br)], will become too small for sensitive recording of relaxation curves. In this case, it is more favourable to use the non-polarizing variant of field cycling. The polarization interval is then omitted, so that the initial magnetization in the relaxation field will be Mz(τr=0)≈0 instead of M0(Bp). In this case, eqn (1.37) takes the form

Mz(τr)=M0(Br){1−exp[−τr/T1(Br)]}
Equation 1.38

In eqn (1.37) and (1.38), the finite switching and settling times have not been taken into account explicitly. In the case of pre-polarization, the relaxation interval must in reality be extended from τr to τr+(Δt)down+(Δt)up, and eqn (1.37) should be modified to

formula
Equation 1.39

where c1 and c2 are constants. A derivation can be found in ref. 7, p. 140. The quantity to be acquired is then

formula
Equation 1.40

where Mz and ΔMzeff are constants implicitly defined by eqn (1.39). Together with the measurand of interest, T1(Br) or 1/T1(Br), they can be fitted to the experimental raw data. Relaxation losses in the finite switching and settling intervals obviously diminish the dynamic range of the variation of the relaxation decay and, hence, the experimental accuracy. However, they do not cause any systematic experimental error provided that the passages between the different field levels are reproducible when incrementing the relaxation interval τr for a given relaxation flux density Br. The limitation of field-cycling NMR relaxometry with respect to the finite switching intervals is thus given by the requirement that ΔMzeff, i.e. the dynamic range of signal variations, should be large enough for good signal acquisition sensitivity.

In representations of field-cycling NMR relaxometry data, it is most important that specifications characterizing the evaluated relaxation curves are included. This in particular refers to whether and how far the curves can indeed be represented by monoexponential decays anticipated in eqn (1.37)–(1.40). Reasons for deviations will be discussed in Section 1.2.1. If monoexponential fits are employed, the resulting relaxation data should be supplemented by specifying the range (in terms of orders of magnitude) over which the curves can be described by monoexponential functions, and with what standard deviation. Diagrams of data processed further than needed for the primary evaluation of relaxation curves may conceal the direct information derived from the measuring process and should therefore be used at the acquisition stage only if unavoidable.

Typical field-cycling magnet coils are made of diamagnetic materials. They are mounted in setups that do not contain any conducting loops that might give rise to eddy currents upon switching the field. The magnetic energy will essentially be deposited in the space in and around the magnet. All technical challenges that the design of field-cycling NMR relaxometers may demand thus originate from the need to transport large amounts of magnetic field energy

formula
Equation 1.41

from and to the magnetic-field filled space in a precise, fast and well-controlled way. Wmagn will be large for voluminous magnets and small for compact architectures. Large magnets favour good detection field homogeneities, large sample volume and efficient cooling devices. In the present context, good field homogeneity is mainly desirable for the sensitivity of signal detection. Signals of liquid-like samples can then be acquired with an accordingly narrow rf bandwidth serving the suppression of noise. On the other hand, compact magnets facilitate fast field switching. Desirable specifications are listed in the insets in Figure 1.4. Technical compromises developed for the optimization of such characteristics are described and discussed in detail in Chapters 3–5.

Good sensitivity requires polarization and detection flux densities, Bp and Bd, respectively, that are as high as possible with reasonable homogeneity and sufficient thermal stability. Field switching and settling times limit the range of relaxation times that can be measured. At the lowest fields, spin–lattice relaxation times can be less than 1 ms even in diamagnetic samples, depending on molecular dynamics and spin couplings. The field switching intervals must be correspondingly short. The problem is not so much to ensure high field slew rates. Rates of about 103 T s−1 are easy to reach in principle. The difficulty is rather to settle and stabilize the field with the desired precision after the relaxation flux density has been reached. For the relaxation interval, an accuracy of a few percent in a settling time of less than 1 ms after lowering the field is normally considered to be sufficient. A discussion of how this specification can technically be validated and calibrated is presented in Chapters 3 and 4.

Field-cycling NMR relaxometry requires instruments dedicated to this particular version of NMR experiments. To some limited extent, information on the low-frequency dispersion of spin–lattice relaxation can also be examined with the aid of rotating-frame techniques (see Chapter 7), which can be implemented on conventional high-field spectrometers. The accessible frequency range of ordinary on-resonance rotating-frame NMR relaxometry26  can be extended by an off-resonance variant.27  Moreover, a rotating-frame analogue of field-cycling relaxometry exists, termed SLOAFI (spin-lock adiabatic field-cycling imaging). It enables one to probe low-frequency rotating-frame spin–lattice relaxation in a certain frequency range without stepping the rotating rf flux density.28,29  As already mentioned, the application of rotating-frame techniques is of particular interest for samples with strongly restricted reorientation processes such as liquid crystals,17  where the relation given in eqn (1.18) is suspected to fail.

As demonstrated in Chapters 3–5, the technical difficulties concerning the lowest frequencies that can be reached, and the short field switching and settling times that are needed, appear to be largely overcome with the present state of the art. Hence the question remains of whether physical limits exist that restrict applications in these respects.

In principle, there are two physical, i.e. sample-dependent, reasons why measurements and interpretations at extremely low frequencies might become doubtful. The first reason, the violation of the Redfield condition requiring T1ωk−1 has already been referred to in the context of eqn (1.15), (1.21) and (1.27), where ωk is the angular spin transition frequency for which the spectral density (ωk) provides the leading contribution under the experimental conditions. This situation may arise if strong spin interactions, i.e. short spin–spin distances and/or efficient electron paramagnetic coupling partners. The consequence will be a low-frequency cut-off of the relaxation dispersion. Importantly, this must not be confused with the proper low-frequency plateau expected for ωτc≪1 (compare the exemplary spectral densities plotted in Figure 1.3). Therefore, some care is appropriate at proton frequencies of a few kilohertz if spin–lattice relaxation times turn out to be below a few milliseconds. The same limitation will be effective for spin–lattice relaxation in the rotating frame.

In certain systems, fast restricted fluctuation components, e.g. rotational diffusion about a preferential axis, are superimposed to slow isotropic reorientation processes. While the former tends to comply with the Redfield condition in the whole field-cycling frequency range, the latter may violate it. This can give rise to a further origin of low-frequency artefacts. It has to do with so-called local fields produced by secular spin interactions.

The attribute ‘secular’ means that no or at most spin energy-conserving transitions (i.e. zero-quantum transitions as illustrated in Figure 1.1a) are induced by the respective terms of the Hamiltonians given in eqn (1.8) and (1.19). For homonuclear spin pairs labelled with subscripts k and l, the secular part of the dipolar Hamiltonian eqn (1.8) is2,7 

formula
Equation 1.42

where Ikz and Ilz represent the z components of the spin vector operators I⃑k and I⃑l, respectively.

For rotationally symmetric electric field gradients, the secular part of the quadrupolar high-field Hamiltonian eqn (1.19) is likewise represented by2,7 

formula
Equation 1.43

where Iz is the z component of the spin vector operator I⃑.

For unrestricted and – relative to the spin–lattice relaxation rate – fast molecular motions, the secular Hamiltonians are effectively averaged to zero:

formula
Equation 1.44

The angular brackets indicate temporal averages on the time scale τT1, i.e. relative to the mean lifetime of spin states. This is in contrast to cases where molecular dynamics is strongly constrained, such as in liquid crystals or polymer systems. Motional averaging can then no longer be taken for granted, and residual local fields may arise.

The unaveraged dipolar magnetic fields δB⃑dip=δdip/γ from dipolar couplings can be represented by the mean angular precession frequency . Likewise, unaveraged electric field gradients suggest . The angular frequencies ωloc would be relevant for spin precession if solely these residual fields were to exist. The respective values can reach 105 rad s−1 for protons and 106 rad s−1 for deuterons in extreme cases. These local fields may exceed the external field B0 at low frequencies and, hence, govern quantization. Field-cycling NMR relaxometry must therefore comply with the high-field condition B0Bloc, where Bloc=ωloc/|γ|. Needless to say, spin–lattice relaxation in the rotating frame is restricted by analogous conditions, i.e. T1ρω1−1 and ω1ωloc (see Chapter 7). Further features of systems with motional restrictions are discussed in Section 1.3.3.

The difference in low-frequency artefacts due to violation of the Redfield condition on the one hand and to local fields on the other applies only to systems with motional restrictions on time scales longer than T1. In the case of isotropic fluctuations, motional averaging of local fields on the time scale of T1 is already warranted in the Redfield limit. Both sources of potential low-frequency artefacts will then be excluded concomitantly if T1ωk−1 is satisfied.

With respect to the dynamic signal detection range and concerning the measurability of extremely short relaxation times, one may conclude that ‘fast is always better than slow’. However, if the slew rate is too high and if the angular Larmor frequency vector ext=−γB⃑0 in the external field B⃑0 reaches magnitudes smaller than the arbitrarily oriented Larmor frequency loc in the local fields, so-called zero-field coherences23,30  can be excited. This is a spectroscopic phenomenon totally different from relaxation processes. In order to avoid such effects, an adiabatic crossover between the polarization and relaxation intervals should be approached. The field variation rate must be slow relative to the instantaneous Larmor precession. The condition for adiabatic field transitions is31,32 

formula
Equation 1.45

where =ext+loc.

Time scales are a key issue in field-cycling NMR relaxometry. This applies in particular to heterogeneous and multi-phase systems where exchange processes matter. Spin–lattice relaxation depends on material properties such as molecular mobilities, steric restrictions, strength of spin interactions, microstructural constraints, electron paramagnetic centres, etc. If these features are distributed inhomogeneously in the sample, the crucial question arises of whether levelling by exchange is effective or not. The problem to be dealt with is illustrated in Figure 1.5.

Figure 1.5

Schematic network of compartments or phases in a heterogeneous sample with different local spin–lattice relaxation rates. Depending on the molecular or spin exchange rates kij between ‘sites’ i, j, the spin–lattice relaxation curves will be monoexponential for fast exchange and multiexponential for slow exchange.

Figure 1.5

Schematic network of compartments or phases in a heterogeneous sample with different local spin–lattice relaxation rates. Depending on the molecular or spin exchange rates kij between ‘sites’ i, j, the spin–lattice relaxation curves will be monoexponential for fast exchange and multiexponential for slow exchange.

Close modal

The relevant exchange mechanisms are normally of a physicochemical nature. However, exchange between dipolar-coupled protons can also be mediated by immaterial spin transport. With solids or solid-like materials, one speaks of spin diffusion, whereas immaterial spin transport in liquids is better referred to as cross-relaxation.5 

In dipolar-coupled, homonuclear spin systems, pair-wise exchange between spins labelled with subscripts k and l is induced by the flip-flop Hamilton operator8:

formula
Equation 1.46

It produces zero-quantum transitions (compare Figure 1.1a) corresponding to an exchange of spin states between the two nuclei involved. Effectively, this means diffusive transport of spin states from spin-bearing nucleus to spin-bearing nucleus. Ik, Il and Il+, Ik+ are the respective lowering and raising spin operators.7 

Spin diffusion will not be effective for quadrupole nuclei such as deuterons, for which homonuclear dipolar coupling is relatively weak. Note, furthermore, that exchange between spatially extended phases will be controlled by translational diffusion and – if existing – by spin diffusion from and to the interfaces between the phases. It can therefore be much slower than expected for direct thermal activation. In the following, we will distinguish the time scale of the relaxation process from that of the autocorrelation function.

It is often taken for granted that relaxation curves are monoexponential. Fortunately, this is normally – but definitely not always – the case, even in heterogeneous or multi-phase samples. The criterion is the exchange rates between the phases relative to relaxation rates.

If molecular or spin exchange rates between sites of different relaxation efficiency are much greater than the local spin–lattice relaxation rates, i.e. kijR1(i), R1(j), the relaxation curves will be monoexponential:

formula
Equation 1.47

They decay with the average spin–lattice relaxation rate . The local rates R1j=1/T1(j) that would be effective at the ‘sites’ j in the absence of exchange are weighted by the respective populations pj (see Figure 1.5).

In the opposite limit, kijR1(i), R1(j), exchange will be too slow to level the local relaxation rates. The relaxation curves will then be composed of a distribution of exponentials:

formula
Equation 1.48

Slow exchange is relevant in composite media, where grains of different molecular mobility and/or spin couplings are larger than the root mean-square spin displacements on the time scale of spin–lattice relaxation, be it by chemical or by flip-flop exchange.

In principle, non-exponential relaxation curves of the type eqn (1.48) can be analysed in terms of superimposed exponential components using the inverse Laplace transform (ILT) evaluation procedure (see, e.g., Chapters 10, 18 and 19). Another approach that is independent of the dynamic signal range recorded or reached in the experiments is to evaluate directly the average relaxation rate from non-exponential relaxation curve data. The normalized distribution of (local) relaxation rates in the absence of exchange be g (R1). The spin–lattice relaxation curve is then expressed as

formula
Equation 1.49

Actually, this is the integral version of eqn (1.48). The slope of the relaxation curve is given by

formula
Equation 1.50

The initial slope

formula
Equation 1.51

obviously renders the exact average of the local relaxation rates. To obtain this information, there is no need to acquire the whole relaxation curve. The average in eqn (1.51) is moreover identical with the average obtained in the fast spin-exchange limit eqn (1.47): the result for fictitious levelling by fast exchange in the sample is the same as post-experimental averaging in the absence of exchange via the initial slope.

In practice, one can determine the initial slope of the relaxation curve by taking the numerical derivative of the experimental data set and extrapolating to the origin of the relaxation interval. Alternatively, even a simple fit of an exponential function to the first few data points should be sufficient for a reasonable approach. In cases where neither the slow- nor the fast-exchange limit applies, the situation may be less clear. However, the above evaluation protocol for the slow-exchange limit will nevertheless provide characteristic and reproducible values.

The fast exchange limit on the relaxation time scale, i.e. relative to local relaxation times, can be further subdivided into fast- and slow-exchange limits relative to the time scale on which correlation functions are probed. For discussion purposes, we will restrict ourselves to a system consisting of two phases in which molecules are subject of different correlation decays. As an illustrative – but certainly not exclusive – example, we will consider polar fluids in porous or colloidal media with polar surfaces where one can distinguish an adsorbed fluid phase and a bulk-like fluid phase (see Figure 1.6).

Figure 1.6

Schematic representation of the two-phase-exchange model of fluids confined in saturated porous matrices. The fluid adsorbed at surfaces (population pa and correlation time τa) is distinguished from the bulk-like phase (population 1−pa and correlation time τb). Molecular mobilities within and exchange kinetics between these phases determine the dynamics of fluid molecules. The mean desorption time in the adsorbed phase is denoted τdes. The limits T1(ω)≫ττdes (fast exchange on both the relaxation and correlation time scales) and T1(ω)≫τdesτ (fast exchange on the relaxation time scale, slow exchange on the correlation time scale) are of particular interest.

Figure 1.6

Schematic representation of the two-phase-exchange model of fluids confined in saturated porous matrices. The fluid adsorbed at surfaces (population pa and correlation time τa) is distinguished from the bulk-like phase (population 1−pa and correlation time τb). Molecular mobilities within and exchange kinetics between these phases determine the dynamics of fluid molecules. The mean desorption time in the adsorbed phase is denoted τdes. The limits T1(ω)≫ττdes (fast exchange on both the relaxation and correlation time scales) and T1(ω)≫τdesτ (fast exchange on the relaxation time scale, slow exchange on the correlation time scale) are of particular interest.

Close modal

Furthermore, we will restrict ourselves to intramolecular orientation correlation functions:

(τ)≈k(τ)=4π(−1)kY2,k(0) Y2−k(τ)〉
Equation 1.52

Four different scenarios can be distinguished. They are characterized by the following mutually exclusive probabilities: (a) fa,a (τ), fraction of molecules that happen to be initially (= time 0) and also finally (= time τ) in the adsorbed phase; (b) fa,b (τ), fraction of molecules that happen to be initially in the adsorbed phase and finally in the bulk-like phase; (c) fb,a (τ), fraction of molecules that happen to be initially in the bulk-like phase and finally in the adsorbed phase; and (d) fb,b (τ), fraction of molecules that happen to be initially and also finally in the bulk-like phase. Normalization requires

fa,a (τ) + fa,b (τ) + fb,a (τ) + fb,b (τ)=1
Equation 1.53

The subscripts a and b stand for ‘adsorbed’ and ‘bulk-like’, respectively. Cases (a) and (d) imply that a reference molecule will be still or again in the same phase as initially. This is in contrast to cases (b) and (c), where the initial and final phases are different.

From the statistical point of view, eqn (1.52) can be subdivided into four partial correlation functions for four sub-ensembles of molecules. The total correlation function effective for all molecules in both phases is then the weighted average9:

(τ)= fa,a (τ) a,a (τ) + fa,b (τ) a,b (τ) + fb,a (τ) b,a (τ) + fb,b (τ) b,b (τ)
Equation 1.54

The partial correlation functions i,j (τ) for i=a,b and j=a,b refer to sub-ensembles of molecules being initially in phase i and finally in phase j. Their contributions are weighted by the fractions fi,j.

The local correlation times in the adsorbed and bulk-like phases, that is, the time constants of a,a (τ) and b,b (τ), are denoted τa and τb, respectively. The correlation time in the bulk medium can be assumed to be short relative to that of the adsorbed phase: τbτa. Restricting ourselves to intervals τ>τb means that all correlation will have faded away if molecules reside in the bulk phase permanently or temporarily: a,b (τ>τb)≈0, b,a (τ>τb)≈0 and b,b (τ>τb)≈0. Eqn (1.54) is thus reduced to

(τ>τb)≈fa,a (τ)a,a (τ)
Equation 1.55

so that τa remains as the correlation time of particular interest here10.

With the mean desorption time τdes of molecules in the adsorbed phase, fast- and slow-exchange limits can be defined relative to the interval τ after which the correlation function a,a (τ) is considered:

formula
Equation 1.56

In the fast-exchange limit relative to the correlation time scale ττdes, the probabilities of finding the reference molecule in the adsorbed phase initially and finally are independent of each other. The fraction fa,a can therefore be approximated by

fa,a (ττdes) ≈ pa2
Equation 1.57

where pa is the (time independent) population in the adsorbed phase. Eqn (1.55) can thus be expressed by

(τ)≈pa2a,a (τ) for τb<τ≫τdes
Equation 1.58

In the opposite limit of slow exchange relative to the correlation time scale, ττdes, the adsorbate molecules will remain in their initial phase, so that

fa,a (ττdes)≈pa
Equation 1.59

The correlation function eqn (1.55) thus adopts the form

(τ〉≈paa,a(τ) for τb<τ≪τdes
Equation 1.60

The remarkable difference between eqn (1.58) and (1.60) is that the former has a quadratic and the latter a linear dependence on the population of the adsorbed phase. On the other hand, the decay of the effective correlation function of all particles in both phases will be dominated by the sub-ensemble residing both initially and finally in the adsorbed phase. That is, the function a,a (τ) matters in either case.

The spectral densities conjugate to eqn (1.58) and (1.60) are

formula
Equation 1.61

for fast exchange and

formula
Equation 1.62

for slow exchange. According to these limits, a distinction is possible via the proportionalities

formula
Equation 1.63

predicted for fast exchange on both the correlation and relaxation time scales, and

formula
Equation 1.64

for slow desorption on the correlation time scale but fast exchange on the relaxation time scale. In this respect, experiments and Monte Carlo simulations have been reported.17,33  Note that the relevant angular frequency in eqn (1.63) and (1.64) is ω≈|γ|B0 in the ‘like’ spin case, whereas it can be either ωI≈|γI|B0 or ωS≈|γS|B0 for ‘unlike’ spin systems depending on the spectral density dominating under the experimental conditions (see Section 1.1.2).

The solvation shell of paramagnetic particles can be identified with the ‘adsorbed phase’. Examples are aqueous solutions of paramagnetic ions or paramagnetic globular proteins. According to eqn (1.81), which is derived in Section 1.3.2, the effective correlation time for dipolar couplings in the solvation shells is34–36 

formula
Equation 1.65

where τrot is the correlation time for rotational diffusion of the salvation complex and τS is the flip time of the unpaired electron spin. Diamagnetic relaxation mechanisms in both the bulk and adsorbed phases are assumed to be negligible. Since normally τrotτS, τdes, the correlation time scale of interest will be τb<ττrotτdes. That is, the slow-exchange limit will be relevant for 1/τb>ω>1/τdes, and the linear relationship eqn (1.64) applies.

If paramagnetic particles are fixed at pore surfaces (see Chapters 18–20) or if scalar interaction2,7  dominates, rotational diffusion cannot contribute to fluctuations of the interactions between the dipoles in the solvent and those of the unpaired electrons. Eqn (1.65) is thus reduced to

formula
Equation 1.66

If τSτdes, the correlation time scale of interest will be τb<ττSτdes, so that the slow exchange limit will apply for 1/τb>ω>1/τdes, as before. Conversely, in the limit τSτdes, slow or fast exchange will be relevant depending on whether the correlation time scale is τb<τ<τdes or τb<τ>τdes. With increasing time interval τ or decreasing angular frequency ω, there will be a crossover from the slow to the fast exchange limit, i.e. from the linear relationship eqn (1.64) to the quadratic counterpart eqn (1.63).

The RMTD process13,37  of adsorbate molecules at surfaces refers to entirely diamagnetic materials. In addition to the discussion in Section 1.1.1.3 on the special intra- and intermolecular relaxation features of this mechanism, there is one more peculiar characteristic, namely the exchange behaviour. Desorption does not mean final loss of all rotational and translational correlations of spin interactions. After readsorption and subject to the surface topology, molecules can regain an orientation correlated to the orientation before desorption (for an illustration, see Figure 1.7c).

Figure 1.7

Exemplary model scenarios for molecular reorientation processes with and without restrictions. (a) Unrestricted isotropic rotational diffusion of more or less spherical molecules bearing two dipoles I and S. The cones symbolize precession about the external flux density B⃑0. A typical example is rotational diffusion of the hydration complexes of electron-paramagnetic ions in aqueous solutions (see, e.g., ref. 35 and 36). (b) Fast but restricted polymer segment reorientations by fluctuating rotational isomerism superimposed by slow Rouse chain modes (see Chapters 8 and 13 and ref. 18). (c) RMTD process: adsorbate molecules diffuse along a more or less rough surface of a solid adsorbent (see ref. 13 and 37). While being adsorbed at the surface, molecules are subject to fast and restricted rotational diffusion. This can be superimposed by a (slow) displacement process along the surface via excursions to the bulk fluid. After readsorption, the initial orientation u⃑i will be converted to the final orientation u⃑f controlled by the local surface topology. In a sequence of numerous ‘desorption/(diffusive bulk excursion)/readsorption’ cycles, adsorbate molecules can intermittently probe surfaces in this way over relatively long distances while – as per surface topology – they retain orientation correlations orders of magnitude longer than the actual surface residence times during the sporadic adsorption events. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV. (d) Order-director fluctuations collectively reorient molecules on a time scale much longer than restricted rotational diffusion about the long-axis of the molecules (see Chapter 11 and ref. 5).

Figure 1.7

Exemplary model scenarios for molecular reorientation processes with and without restrictions. (a) Unrestricted isotropic rotational diffusion of more or less spherical molecules bearing two dipoles I and S. The cones symbolize precession about the external flux density B⃑0. A typical example is rotational diffusion of the hydration complexes of electron-paramagnetic ions in aqueous solutions (see, e.g., ref. 35 and 36). (b) Fast but restricted polymer segment reorientations by fluctuating rotational isomerism superimposed by slow Rouse chain modes (see Chapters 8 and 13 and ref. 18). (c) RMTD process: adsorbate molecules diffuse along a more or less rough surface of a solid adsorbent (see ref. 13 and 37). While being adsorbed at the surface, molecules are subject to fast and restricted rotational diffusion. This can be superimposed by a (slow) displacement process along the surface via excursions to the bulk fluid. After readsorption, the initial orientation u⃑i will be converted to the final orientation u⃑f controlled by the local surface topology. In a sequence of numerous ‘desorption/(diffusive bulk excursion)/readsorption’ cycles, adsorbate molecules can intermittently probe surfaces in this way over relatively long distances while – as per surface topology – they retain orientation correlations orders of magnitude longer than the actual surface residence times during the sporadic adsorption events. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV. (d) Order-director fluctuations collectively reorient molecules on a time scale much longer than restricted rotational diffusion about the long-axis of the molecules (see Chapter 11 and ref. 5).

Close modal

As a consequence, the time scale τ on which the correlation function a,a(τ) is still finite is much longer than the desorption time τdes characterizing the intermittent periods that adsorbate molecules spend on the surface between adsorption and desorption. τdes can in principle be measured in a separate experiment with samples having electron paramagnetic centres incorporated in the surface (see the previous section). If these centres dominate spin–lattice relaxation, all diamagnetic processes including RMTD can be neglected. Values for the desorption time found under such conditions are of an order of magnitude similar to that of solutions of paramagnetic ions,34–36 i.e. τdes≈10−8–10−7 s (see Chapters 18–20)11. This can be compared with correlation times τa found in diamagnetic samples of the same porosity, which are several orders of magnitude longer.13,37  The RMTD process of adsorbate molecules must therefore consist of numerous desorption/bulk excursion/readsorption intermezzos before the autocorrelation function finally fades away subject to the surface topology. From the statistical point of view, this mechanism implies features of Lévy walks, as can nicely be demonstrated by computer simulations.38 

An order of magnitude τdes≈10−8 s means that for ω<τdes−1≈108 rad s−1 we have fast exchange and for ω>τdes−1≈108 rad s−1 slow exchange on the time scale τ to be probed in experiments. There will again be a crossover from a quadratic dependence on the population in the adsorbed phase at low frequencies to a linear relationship at higher values.

Let us consider an (unnormalized) autocorrelation function of the type defined in eqn (1.11) and (1.22):

G(τ)=CF(0)F*(τ)〉
Equation 1.67

where C is a constant, and where we have omitted all sub- and superscripts used in the formalisms above. The angular brackets in eqn (1.67) stand for ensemble averages, and the time scale is limited by τ<T1. All molecular motions and – nota bene – exchange processes taking place within this period are relevant and must therefore be considered. Generally, the evaluation of ensemble averages for the dynamic model under consideration is a matter of probability treatments.

For definitions, the reader is referred to the dipolar-coupling scenario illustrated in Figure 1.2. The conditional probability density for the initial and final values of the functions FiF [φ(0), ϑ(0), r(0)] and FfF [φ(τ), ϑ(τ), r(τ)], respectively, is termed Pc(Fi, Ff ; τ). In other words, Pc(Fi, Ff ; τ) is the probability density that the value after the interval τ will be Ff if the initial value was Fi. We speak of a probability density since it concerns a volume element dVf around the spherical coordinate triple φ(τ), ϑ(τ), r(τ).

Furthermore, let p(Fi) be the a priori probability density for the initial value Fi with regard to a volume element dVi around the starting coordinates φ(0), ϑ(0), r(0). The expression p(Fi)Pc(Fi, Ff ; τ)dVidVf is then the (unconditional) probability that the function F has the initial value Fi and the value Ff finally.

With these definitions, the ensemble average of the autocorrelation function G(τ) in eqn (1.67) is calculated by integrating over the sample volume according to

G(τ)=∫∫FiFf*P(Fi, 0; Ff, τ)dVidVf=∫∫FiFf*p(Fi)Pc(Fi, Ff ; τ)dVidVf
Equation 1.68

Given a certain concept for molecular dynamics, the task to be performed is the evaluation of the corresponding probability densities specific for that model.

An instructive – but definitely not ubiquitous – case is the intramolecular relaxation mechanism due to isotropic rotational diffusion (see the illustration in Figure 1.7a).2,5  The orientation of an inter-dipole vector at time t is defined by the unit vector u⃑(t)=r⃑(t)/r with the polar coordinates φ(t), ϑ(t), u=1 length unit. Let us now consider the time interval between t=0 and t=τ. With the identities φ(0)≡φi, ϑ(0)≡ϑi, u⃑(0)≡u⃑i and φ(τ)≡φf, ϑ(τ)≡ϑf, u⃑(τ)≡u⃑f for the respective initial and final orientations, the normalized autocorrelation function for dipolar coupling at fixed inter-dipole distance reads

formula
Equation 1.69

[see eqn (1.12)].

The ensemble average over all possible initial and final orientations can be calculated as suggested by the probability expression eqn (1.68). In this context, the term probability density refers to solid angles instead of volumes in the proper sense. The probability that the final unit vector u⃑f points in a solid-angle element dΩf=sin ϑfdϑfdφfand that the initial unit vector u⃑i points in the solid-angle element dΩi=sin ϑidϑidφi is thus equal to P(u⃑i, 0; u⃑f, τ) sin ϑf sin ϑidϑfdφfdϑidφi, where P(u⃑i, 0; u⃑f, τ) is the corresponding probability density. Likewise, the a priori probability that the unit vector u⃑i points in a solid-angle element dΩi=sin ϑidϑidφi equals p(u⃑i) sin ϑidϑidφi, where p(u⃑i) is the a priori probability density. Eventually, the conditional probability that the final unit vector u⃑f points in the solid-angle element dΩf=sin ϑfdϑfdφf if the initial unit vector is u⃑i is defined by Pc(u⃑i, u⃑f, τ) sin ϑfdϑfdφf, where Pc(u⃑i, u⃑f, τ) is the conditional probability density. In summary, we thus obtain

formula
Equation 1.70

Under isotropic conditions, the a priori probability for a certain orientation is given by the solid-angle ratio

formula
Equation 1.71

Inserting this in eqn (1.70) gives the expression to be evaluated:

formula
Equation 1.72

The crucial term in eqn (1.72) is the conditional probability density Pc(u⃑i, u⃑f, τ). For continuous rotational diffusion, it will be a solution of the rotational variant of the diffusion equation2,5 

formula
Equation 1.73

where Dr is the rotational diffusion coefficient and ∇2 is the Laplace differential operator for polar coordinates r, φ, ϑ. With the Dirac delta function, the initial condition can be expressed as

Pc(u⃑i, u⃑f, 0)=δ(u⃑fu⃑i)
Equation 1.74

Expanding in terms of spherical harmonics, expressing the Laplace operator in spherical coordinates and exploiting the orthonormal properties of spherical harmonics leads – after some lengthy but straightforward calculus – to the monoexponential autocorrelation function

formula
Equation 1.75

with the rotational correlation time τrot=(6Drot)−1 and the rotational diffusion coefficient Drot.5 Note that this result does not depend on the order k, as already stated by eqn (1.13).

Isotropic rotational diffusion is expected for spherical molecules such as certain globular proteins39–41  and hydration complexes of paramagnetic ions34–36  [see eqn (1.80)], and – to some extent – cyclohexane in the plastic phase,42  for instance. Exponential correlation functions and hence Lorentzian spectral densities are also observed in cases where rotational diffusion is restricted to a fixed rotation axis within the time scale of spin–lattice relaxation. Examples are methyl side groups of amino acids and polypeptides43  and benzene crystals.44  Finally, simple two-site exchange processes as found in gypsum26  also reveal exponential autocorrelation functions.

With less symmetric and more complex scenarios of molecular dynamics, autocorrelation decays can be far from monoexponential. Most applications reported in this book actually refer to complex systems. Examples are chain modes of polymers (see Figure 1.7b and Chapters 8 and 13), diffusion of adsorbate molecules along adsorbent surfaces (see Figure 1.7c and Chapters 9, 12, 18, 19 and 20) and order director fluctuations in liquid crystals5  (see Figure 1.7d and Chapter 11).

A more general approach to formulate correlation functions is to interpret them as probabilities that the dynamic processes in question have not yet taken place after an interval τ. Usually, the complementary probability is primarily available, namely the probability W(τ) that the corresponding process has occurred. The (normalized) correlation function is then

(τ)≈1−W(τ)
Equation 1.76

Examples of such treatments can be found in ref. 45 for polymer segment reorientation by reptation and in ref. 14 and 15 for defect diffusion models in solid-like or ordered structures. In the case of reptation of polymer chains in a fictitious tube, the autocorrelation function for segment reorientation is identified with the probability that the polymer segment is still (or again) at the same position of the tube after the interval τ, and has not yet diffused away. That is, the tube is supposed to determine the segment orientation on the time scale relevant for reptation. Likewise, translational diffusion of structural (reorienting or structurally dilating) defects means that local spin couplings in molecules or molecular groups can only fluctuate subject to the arrival of such defects. The autocorrelation function is then the probability that no such defects have arrived during τ. Note that molecular fluctuations are the consequence of translational displacements in these particular model concepts.

One last remark with regard to autocorrelation functions: there may be a distribution of relaxation times (or functions) in heterogeneous systems if exchange is slow enough, as discussed in Section 1.2.1.2. However, for a single monoexponential relaxation scenario (i.e. for fast exchange on the relaxation time scale), there is strictly no such thing as a distribution of correlation functions. All molecular dynamics occurring on the time scale of spin–lattice relaxation, τT1, is to be represented by a single autocorrelation function, potentially implying a distribution of components with different local correlation and exchange time constants. We will return to this subject in the next section.

Regarding eqn (1.14)–(1.17), (1.21), (1.26) and(1.27), one finds that all these expressions for spin–lattice relaxation rates are linear combinations of (normalized) spectral densities for intramolecular interactions and (unnormalized) spectral densities for intermolecular couplings:

formula
Equation 1.77

Weighted with coefficients ak and bk, spectral densities for zero-, single- and double-quantum transitions are simply added up as far as relevant. Intra- and intermolecular relaxation rates are plainly added [see eqn (1.17)]. Relaxation in multi-spin systems is approached by sums of two-spin terms [see eqn (1.16)]. This analytical simplicity may be the reason why most researchers prefer to plot dispersions of relaxation rates rather than relaxation times.

Zero-, single- and double-quantum transitions and intra- and intermolecular interactions are independent sources of relaxation. According to eqn (1.77), the effective relaxation function [see eqn (1.37)] is therefore a product of the individual relaxation contributions:

formula
Equation 1.78

There is a certain parallelism between relaxation and correlation functions provided that correlation functions are monoexponential: Let us label the correlation loss rates 1/τm for superimposed stochastic processes labelled with the subscript m. The analogue to eqn (1.78) for the effective correlation function is then

formula
Equation 1.79

A well-known example is proton spin–lattice relaxation in aqueous solutions of electron paramagnetic ions, where dipolar couplings with the spins of the unpaired electrons dominate.35,36  Dipolar interactions fluctuate owing to rotational diffusion of the ion hydration complex (correlation time τrot), by electron spin flips (correlation time12τS) and by exchange between hydration shells and bulk solvent (correlation time τdes). The individual correlation functions of all these processes are monoexponential. In total, this leads to the autocorrelation function

formula
Equation 1.80

with the correlation loss rate

formula
Equation 1.81

Eqn (1.80) is the probability that none of the three processes has taken place in the interval τ.

One is tempted to extend this additive property generally to the superposition of different dynamic processes. Take, for instance, a nematic liquid crystal domain, in which molecules on the one hand rotate about their long axes and on the other are independently subjected to collective order-director fluctuations.5  Let the correlation functions of the respective components be (rot)(τ) and (ODF)(τ). The probability considerations outlined in the previous section suggest that the correlation function (τ) of superimposed, stochastically independent molecular dynamics fluctuations results as the product of their component correlation functions, i.e.

(τ)=rot(τ)(ODF)(τ)
Equation 1.82

The Fourier transform of this product is definitely not a sum or linear combination of the component spectral densities. However, under certain conditions the resulting relaxation rate can nevertheless be approached by a linear combination. This will be pointed out in the following section.

The three types fluctuations assumed in eqn (1.80) are characterized by different correlation loss rates. However, they are all assumed to be unrestricted, so that no constraints with respect to molecular orientations or electron spin state are effective. In contrast, superpositions of different fluctuation processes are of particular interest if molecular motions are partially or entirely subject to steric or microstructural constraints. Such restricted fluctuations are characterized by correlation functions of the type

(τ)=g(τ)+()
Equation 1.83

where g(∞)=0 and (∞) is a finite constant, that is, the function does not decay to zero but rather leaves a residual correlation (∞) in the long-time limit.

Typical examples for such superpositions of more or less restricted fluctuations are (i) rotational diffusion versus order-director fluctuations in liquid crystals (as already mentioned), (ii) local polymer segment reorientations versus global chain modes, (iii) rotational diffusion of side groups of proteins versus tumbling of the whole macromolecule and (iv) rotational diffusion of adsorbate molecules in porous or colloidal media versus surface diffusion (i.e. the RMTD process referred to in Sections 1.1.1.3 and 1.2.2.6).

Assume, for instance, a stochastically independent superposition of fast restricted and slow unrestricted components. The effective correlation function then consists of the product of the correlation functions of the two components:

formula
Equation 1.84

where the superscripts f,r and s,u stand for ‘fast and restricted’ and ‘slow and unrestricted’, respectively. The corresponding correlation times [in the sense of eqn (1.5)] are assumed to be related as τc(s,u)τc(f,r). The reorientational restriction manifests itself by a finite residual correlation for the fast restricted process:

(f,r)(τ)=g(f,r)(τ)+(f,r)(τ→∞)
Equation 1.85

where g(f,r)(τ→∞)=0 and (f,r)(τ → ∞)=constant  A. Inserting eqn (1.85) in eqn (1.84) gives

formula
Equation 1.86

On the time scale ττc(s,u), i.e. where g(f,r)(τ) is still finite, the component for slow unrestricted reorientations can be set as (s,u)(ττc(s,u))≈1. According to this different time-scale approach, the total autocorrelation function eqn (1.86) is reduced to

(τ) ≈ g(f,r)(τ)+A(s,u)(τ)
Equation 1.87

In this approximation, we have a sum expression indeed, and the spectral density obeys

(ω) ≈ (f,r)(ω)+A(s,u)(ω)
Equation 1.88

The corresponding spin–lattice relaxation rate is

formula
Equation 1.89

Figure 1.7b–d illustrate a number of model scenarios for which such superpositions of fast restricted and slow restricted or unrestricted processes can be expected.

A further class of systems characterized by restricted fluctuations is liquid crystals. In this context, we have already postulated eqn (1.82) as an example for superposition of two stochastically independent restricted fluctuation components. The expression given there can be expanded according to

formula
Equation 1.90

where g(rot)(∞)=0, g(ODF)(∞)=0, (rot)(∞)=constant and (ODF)(∞)=constant. If the correlation times of the components are very different – for the present case, e.g. τrotτODFeqn (1.90) can be approached by a linear combination

formula
Equation 1.91

where a, b and c are constants resulting from multiplying out the binomial brackets and applying the relevant approximations for the mixed terms in the short- and long-time limits ττODF and ττrot, respectively. Under the above conditions, the spin–lattice relaxation rate can then be approached by 1/T1≈[a/T1(rot)]+[b/T1(ODF)].

Taken together, the necessary conditions permitting one to approximate correlation functions of superimposed fluctuation components,

(τ) ≈ a0+a1g1(τ)+a2g2(τ)+⋯
Equation 1.92

and the corresponding spin–lattice relaxation rate,

formula
Equation 1.93

by linear combinations are (i) the fluctuation components must be stochastically independent, (ii) the fluctuation components must be spatially restricted in general, but one of them may be unrestricted, and (iii) the correlation times of the components must substantially differ from each other, the more the better.

In this chapter, we have outlined the framework conditions of field-cycling NMR relaxometry. These include (i) technical specifications required for successful field-cycling experiments, (ii) physical limits and (iii) the relevant theoretical background, interpretation standards and elements of the calculus of autocorrelation functions and spectral densities. The following 20 chapters delineate a wealth of knowledge concerning solutions of instrumental problems, applications in the full range of accessibility by the technique and the explanatory power of theoretical model treatments. In this sense, the present book may serve as a source of inspiration for the interested reader who is looking forward to carrying out innovative field-cycling studies.

1

Under the conditions relevant here, both functions, and , are mandatorily real as required for observables.

2

Note the strict distinction between absolute time t and time interval τ here and in the following.

3

As an introduction into the statistical physics of stochastic processes in general and the Wiener/Khinchine theorem in particular, the monograph by Heer1  can be recommended, for instance.

4

As an example of non-Brownian fluctuations, quantum-mechanical tunneling will be discussed in Chapter 16.

5

If eqn (1.15) is to refer to spin–lattice relaxation of nuclear spins interacting with unpaired electrons, scalar coupling2  as a further, additional mechanism for nuclear spin relaxation may also be relevant.

6

For extremely small proton fractions – especially if relaxation rates are extrapolated to x→0 in concentration series – dipolar couplings between protons and deuterons will become significant despite their low efficiency.

7

Other examples of RMTD in a more general sense are translational diffusion of molecules in ordered phases such as liquid crystals (see Chapter 11) and reptation of polymer segments under entanglement conditions (see Chapters 8 and 13).

8

Eqn (1.46) is the flip-flop part of the secular Hamiltonian given in eqn (1.42). Note that homonuclear flip-flop spin transitions are an intrinsic part of transverse relaxation,2,3,5,7  so that they do not contribute to exchange averaging in this case.

9

Remember that we are dealing with the fast-exchange limit on the relaxation time scale.

10

The term ‘correlation time’ is understood as defined in eqn (1.5).

11

Here we tacitly identify hydration shells of ions in solution with the adsorption phase of porous media.

12

From the point of view of electron spin resonance, τS is actually a relaxation time.

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Figures & Tables

Figure 1.1

Zeeman energy levels of spin systems in a quantizing field B⃑0 (assumed to point upwards). The double arrows between the levels indicate the (allowed) zero-, single-, and double-quantum transitions relevant for spin relaxation. The spectral densities (ωk) of the fluctuations inducing these transitions are indicated. In the schemes, all gyromagnetic ratios are assumed to be positive. (a) Dipolar coupled ‘like’ spin pairs with quantum numbers I=½, S=½, magnetic quantum numbers mI, mS and a common gyromagnetic ratio γ. The spin eigenstates are symbolized by kets |↑↑〉, |↑↓〉, |↓↑〉 and |↓↓〉 for the diverse combinations of spin-up and spin-down states relative to the vector B⃑0. The Zeeman eigenenergies are , where ω=γB0 is the angular Larmor frequency. The angular transition frequencies are ωk= for zero- (k=0), single- (k=1) and double- (k=2) quantum transitions. Typical examples are protons in organic materials. (b) Pairs of dipolar coupled ‘unlike’ spins ½ having different gyromagnetic ratios γIγS and Larmor frequencies ωI=γIB0 and ωS=γSB0. The Zeeman eigenenergies are . The angular transition frequencies are ωk=|ΔEk|/ħ for zero- (ω0=|ωSωI|), single- (ω1=ωI) and double- (ω2=ωS+ωI) quantum transitions. Typical examples are protons with spins I coupled to unpaired electrons with spin S. (c) (Single) spins 1 subjected to quadrupole interaction in the high-field limit. Quadrupolar coupled spin-1 particles have three Zeeman eigenstates with the kets |m=1〉, |m=0〉 and |m=−1〉 and energies Em=−mħω. The angular Larmor frequency is ω=γB0 as before. The angular transition frequencies are ωk=|ΔEk|/ħ= for single- (k=1) and double- (k=2) quantum transitions. Typical examples are deuterons.

Figure 1.1

Zeeman energy levels of spin systems in a quantizing field B⃑0 (assumed to point upwards). The double arrows between the levels indicate the (allowed) zero-, single-, and double-quantum transitions relevant for spin relaxation. The spectral densities (ωk) of the fluctuations inducing these transitions are indicated. In the schemes, all gyromagnetic ratios are assumed to be positive. (a) Dipolar coupled ‘like’ spin pairs with quantum numbers I=½, S=½, magnetic quantum numbers mI, mS and a common gyromagnetic ratio γ. The spin eigenstates are symbolized by kets |↑↑〉, |↑↓〉, |↓↑〉 and |↓↓〉 for the diverse combinations of spin-up and spin-down states relative to the vector B⃑0. The Zeeman eigenenergies are , where ω=γB0 is the angular Larmor frequency. The angular transition frequencies are ωk= for zero- (k=0), single- (k=1) and double- (k=2) quantum transitions. Typical examples are protons in organic materials. (b) Pairs of dipolar coupled ‘unlike’ spins ½ having different gyromagnetic ratios γIγS and Larmor frequencies ωI=γIB0 and ωS=γSB0. The Zeeman eigenenergies are . The angular transition frequencies are ωk=|ΔEk|/ħ for zero- (ω0=|ωSωI|), single- (ω1=ωI) and double- (ω2=ωS+ωI) quantum transitions. Typical examples are protons with spins I coupled to unpaired electrons with spin S. (c) (Single) spins 1 subjected to quadrupole interaction in the high-field limit. Quadrupolar coupled spin-1 particles have three Zeeman eigenstates with the kets |m=1〉, |m=0〉 and |m=−1〉 and energies Em=−mħω. The angular Larmor frequency is ω=γB0 as before. The angular transition frequencies are ωk=|ΔEk|/ħ= for single- (k=1) and double- (k=2) quantum transitions. Typical examples are deuterons.

Close modal
Figure 1.2

Schematic representation of the interrelations of molecular motions, field-cycling NMR relaxometry experiments and theoretical model concepts. Dipolar couplings between two dipoles with the vector operators I=γIħI⃑ and S=γSħS⃑ depend on the inter-dipole vector r⃑. If I and S are identical, one speaks of ‘like’ spins. The cones symbolize precession about the external flux density B⃑0. r⃑ can be expressed in spherical coordinates r(t), ϕ(t), ϑ(t) fluctuating as a consequence of molecular dynamics. For dipolar couplings, the autocorrelation function (τ) is formed on the basis of the functions Fd(k){r(t), φ(t), ϑ(t)} [see eqn (1.10)]. Note that the functions Fd(k) depend on the absolute time t whereas the autocorrelation function varies with the time interval τ. The spectral densities (ωk) are Fourier transforms of (τ) for the angular frequencies ωk. They determine the spin–lattice relaxation rate as a linear combination for all allowed angular transition frequencies ωk in the spin system under consideration. Predictions based on a theoretical model can be compared with experimental field-cycling NMR relaxometry data. The other way round, dispersion features of the spectral density deduced from experimental data can be taken as conditions to be fulfilled by dynamic models in question.

Figure 1.2

Schematic representation of the interrelations of molecular motions, field-cycling NMR relaxometry experiments and theoretical model concepts. Dipolar couplings between two dipoles with the vector operators I=γIħI⃑ and S=γSħS⃑ depend on the inter-dipole vector r⃑. If I and S are identical, one speaks of ‘like’ spins. The cones symbolize precession about the external flux density B⃑0. r⃑ can be expressed in spherical coordinates r(t), ϕ(t), ϑ(t) fluctuating as a consequence of molecular dynamics. For dipolar couplings, the autocorrelation function (τ) is formed on the basis of the functions Fd(k){r(t), φ(t), ϑ(t)} [see eqn (1.10)]. Note that the functions Fd(k) depend on the absolute time t whereas the autocorrelation function varies with the time interval τ. The spectral densities (ωk) are Fourier transforms of (τ) for the angular frequencies ωk. They determine the spin–lattice relaxation rate as a linear combination for all allowed angular transition frequencies ωk in the spin system under consideration. Predictions based on a theoretical model can be compared with experimental field-cycling NMR relaxometry data. The other way round, dispersion features of the spectral density deduced from experimental data can be taken as conditions to be fulfilled by dynamic models in question.

Close modal
Figure 1.3

Graphical representation of a Lorentzian spectral density, eqn (1.7), as the Fourier transform of monoexponential autocorrelation functions, eqn (1.6), for different values of the correlation time τc. The crossover from the plateau (ωkτc≪1)≈2τc at low angular frequencies to the limit (ωkτc≫1)≈2/(ωk2τc) at high angular frequencies occurs around the positions ωk=τc−1. Note that for ωk<τc−1, the spectral density (ωk) increases with increasing values of τc and decreases in the opposite case ωk>τc−1. This is exemplified by the vertical lines and the dots at two angular frequencies complying with the respective conditions ωa<τc−1 and ωb>τc−1 in the frame of consideration here. Qualitatively, this behaviour applies generally to all stochastic processes irrespective of the actual shape of the autocorrelation function. With respect to field-cycling NMR relaxometry, this means that spin–lattice relaxation rates 1/T1increase with longer τc values (i.e. slower fluctuations) for ωkτc<1 whereas they decrease for ωkτc>1.

Figure 1.3

Graphical representation of a Lorentzian spectral density, eqn (1.7), as the Fourier transform of monoexponential autocorrelation functions, eqn (1.6), for different values of the correlation time τc. The crossover from the plateau (ωkτc≪1)≈2τc at low angular frequencies to the limit (ωkτc≫1)≈2/(ωk2τc) at high angular frequencies occurs around the positions ωk=τc−1. Note that for ωk<τc−1, the spectral density (ωk) increases with increasing values of τc and decreases in the opposite case ωk>τc−1. This is exemplified by the vertical lines and the dots at two angular frequencies complying with the respective conditions ωa<τc−1 and ωb>τc−1 in the frame of consideration here. Qualitatively, this behaviour applies generally to all stochastic processes irrespective of the actual shape of the autocorrelation function. With respect to field-cycling NMR relaxometry, this means that spin–lattice relaxation rates 1/T1increase with longer τc values (i.e. slower fluctuations) for ωkτc<1 whereas they decrease for ωkτc>1.

Close modal
Figure 1.4

Typical specifications of the flux density variation in pre-polarized field-cycling NMR relaxometry experiments (the partial absence of quantitative numbers before the units is to be understood as ‘several’). A non-equilibrium magnetization is produced by rapid switching from the polarization to the relaxation field and – after a relaxation interval – to the detection field Bd. The relaxation curve for the flux density Br is probed point-by-point by varying the length of that interval. The signal is induced with the aid of a 90° rf pulse or a spin-echo pulse sequence. The signal amplitude is proportional to the magnetization retained at the end of the relaxation interval. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV.

Figure 1.4

Typical specifications of the flux density variation in pre-polarized field-cycling NMR relaxometry experiments (the partial absence of quantitative numbers before the units is to be understood as ‘several’). A non-equilibrium magnetization is produced by rapid switching from the polarization to the relaxation field and – after a relaxation interval – to the detection field Bd. The relaxation curve for the flux density Br is probed point-by-point by varying the length of that interval. The signal is induced with the aid of a 90° rf pulse or a spin-echo pulse sequence. The signal amplitude is proportional to the magnetization retained at the end of the relaxation interval. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV.

Close modal
Figure 1.5

Schematic network of compartments or phases in a heterogeneous sample with different local spin–lattice relaxation rates. Depending on the molecular or spin exchange rates kij between ‘sites’ i, j, the spin–lattice relaxation curves will be monoexponential for fast exchange and multiexponential for slow exchange.

Figure 1.5

Schematic network of compartments or phases in a heterogeneous sample with different local spin–lattice relaxation rates. Depending on the molecular or spin exchange rates kij between ‘sites’ i, j, the spin–lattice relaxation curves will be monoexponential for fast exchange and multiexponential for slow exchange.

Close modal
Figure 1.6

Schematic representation of the two-phase-exchange model of fluids confined in saturated porous matrices. The fluid adsorbed at surfaces (population pa and correlation time τa) is distinguished from the bulk-like phase (population 1−pa and correlation time τb). Molecular mobilities within and exchange kinetics between these phases determine the dynamics of fluid molecules. The mean desorption time in the adsorbed phase is denoted τdes. The limits T1(ω)≫ττdes (fast exchange on both the relaxation and correlation time scales) and T1(ω)≫τdesτ (fast exchange on the relaxation time scale, slow exchange on the correlation time scale) are of particular interest.

Figure 1.6

Schematic representation of the two-phase-exchange model of fluids confined in saturated porous matrices. The fluid adsorbed at surfaces (population pa and correlation time τa) is distinguished from the bulk-like phase (population 1−pa and correlation time τb). Molecular mobilities within and exchange kinetics between these phases determine the dynamics of fluid molecules. The mean desorption time in the adsorbed phase is denoted τdes. The limits T1(ω)≫ττdes (fast exchange on both the relaxation and correlation time scales) and T1(ω)≫τdesτ (fast exchange on the relaxation time scale, slow exchange on the correlation time scale) are of particular interest.

Close modal
Figure 1.7

Exemplary model scenarios for molecular reorientation processes with and without restrictions. (a) Unrestricted isotropic rotational diffusion of more or less spherical molecules bearing two dipoles I and S. The cones symbolize precession about the external flux density B⃑0. A typical example is rotational diffusion of the hydration complexes of electron-paramagnetic ions in aqueous solutions (see, e.g., ref. 35 and 36). (b) Fast but restricted polymer segment reorientations by fluctuating rotational isomerism superimposed by slow Rouse chain modes (see Chapters 8 and 13 and ref. 18). (c) RMTD process: adsorbate molecules diffuse along a more or less rough surface of a solid adsorbent (see ref. 13 and 37). While being adsorbed at the surface, molecules are subject to fast and restricted rotational diffusion. This can be superimposed by a (slow) displacement process along the surface via excursions to the bulk fluid. After readsorption, the initial orientation u⃑i will be converted to the final orientation u⃑f controlled by the local surface topology. In a sequence of numerous ‘desorption/(diffusive bulk excursion)/readsorption’ cycles, adsorbate molecules can intermittently probe surfaces in this way over relatively long distances while – as per surface topology – they retain orientation correlations orders of magnitude longer than the actual surface residence times during the sporadic adsorption events. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV. (d) Order-director fluctuations collectively reorient molecules on a time scale much longer than restricted rotational diffusion about the long-axis of the molecules (see Chapter 11 and ref. 5).

Figure 1.7

Exemplary model scenarios for molecular reorientation processes with and without restrictions. (a) Unrestricted isotropic rotational diffusion of more or less spherical molecules bearing two dipoles I and S. The cones symbolize precession about the external flux density B⃑0. A typical example is rotational diffusion of the hydration complexes of electron-paramagnetic ions in aqueous solutions (see, e.g., ref. 35 and 36). (b) Fast but restricted polymer segment reorientations by fluctuating rotational isomerism superimposed by slow Rouse chain modes (see Chapters 8 and 13 and ref. 18). (c) RMTD process: adsorbate molecules diffuse along a more or less rough surface of a solid adsorbent (see ref. 13 and 37). While being adsorbed at the surface, molecules are subject to fast and restricted rotational diffusion. This can be superimposed by a (slow) displacement process along the surface via excursions to the bulk fluid. After readsorption, the initial orientation u⃑i will be converted to the final orientation u⃑f controlled by the local surface topology. In a sequence of numerous ‘desorption/(diffusive bulk excursion)/readsorption’ cycles, adsorbate molecules can intermittently probe surfaces in this way over relatively long distances while – as per surface topology – they retain orientation correlations orders of magnitude longer than the actual surface residence times during the sporadic adsorption events. Reproduced from ref. 12 with permission. Copyright 2017 Elsevier BV. (d) Order-director fluctuations collectively reorient molecules on a time scale much longer than restricted rotational diffusion about the long-axis of the molecules (see Chapter 11 and ref. 5).

Close modal

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