Pierre Thureau

Simone Sturniolo

Miri Zilka

Fabio Ziarelli

Stéphane Viel

Jonathan R Yates

PhD Giulia Mollica
email: giulia.mollica@univ-amu.fr

| Simone Sturniolo

| Miri Zilka

| Stéphane Viel

Reducing the computational cost of NMR crystallography of organic powders at natural isotopic abundance with the help of 13 C-13 C dipolar couplings

Keywords: density functional theory; CSD, Cambridge structural NMR, 13 C, dipolar coupling, crystal structure prediction, crystallography, natural abundance

de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

Structure determination of functional organic compounds remains a formidable challenge when the sample exists as a powder. NMR crystallography approaches based on the comparison of experimental and DFT-computed 1 H chemical shifts have already demonstrated great potential for structure determination of organic powders, but limitations still persist. In this study we discuss the possibility of using 13 C- 13 C dipolar couplings quantified on powdered theophylline at natural isotopic abundance with the help of dynamic nuclear polarisation, to realize a DFT-free, rapid, screening of a pool of structures predicted by AIRSS. We show that while 13 C- 13 C dipolar couplings can identify structures possessing long-range structural motifs and unit cell-parameters close to those of the true structure, it must be complemented with other data to recover information about the presence and the chemical nature of the supramolecular interactions.

| INTRODUCTION

The knowledge of the atomic-level structure of a material is fundamental to understand the origin of its end-use properties. Although single-crystal diffraction techniques have empowered the determination of three-dimensional molecular structure for a wide range of molecules and materials, diffraction techniques are much less efficient when the system under study does not form large crystals (>100 nm). Interestingly, recent progress in crystal structure prediction (CSP) methods demonstrated that crystal structures of powders can be computed ab initio with great accuracy. [1,2,3,4,5,6,7,8,9,10,11,12] CSP consists in exploring the crystal energy landscape of a molecule, i.e. finding "the set of crystal structures that are sufficiently low in energy to be thermodynamically plausible polymorphs". [2] Exploring the energy landscape of an organic molecule is computationally demanding, and its complexity depends on the molecule itself. For a given molecule, CSP usually generates a pool of candidate structures, which are ranked and then selected uniquely on the basis of their global lattice energy (instead of the more meaningful, but computationally more intensive, free energy). Therefore, structure ranking may prove difficult, especially in the case where the lattice energy difference between two candidate structures corresponds to the uncertainty of the CSP calculation (typically, a few kJ/mol). [13,14,2] This aspect is particularly important for organic molecular crystals, in which observed (or potentially observable) polymorphs typically correspond to different local minima on the energy landscape whose energy differs by only a few kJ/mol. [13] In this respect, it has been recently shown that the use of ab initio random structure search (AIRSS) method [15] can be beneficial because the random search algorithm ensures an unbiased sampling of the energy landscape, limiting the risk of missing energetically similar structures sitting far away on the energy landscape. For example in a recent study a set of 600 structures generated using the AIRSS method were used to identify two polymorphic forms of mABA. [16] A solution to the structure-ranking problem is to combine CSP computation with experimental data, such as X-ray diffraction patterns (XRD) or data obtained from solid-state nuclear magnetic resonance (SSNMR) experiments [17,18], to select the most relevant structure(s) from the pool of candidate structures generated by CSP. [19] For example, Xu et al. have combined CSP and X-ray diffraction to establish the crystal structure of glycine dihydrate. [20] Brus and coworkers showed that covariance between experimental and theoretical chemical shifts can be used to assign 2D heteronuclear correlation spectra and to refine the structure of organic solids by identifying the space group of the crystal in the absence of powder diffraction data. [21] Moreover, Baias et al. have used a combination of CSP and SSNMR to investigate the crystal structure of several organic molecules as powders. [22,23] Notably, it has been shown that 1 H chemical shifts can be computed by density functional theory (DFT) for each candidate structure and that the comparison between computed and experimental chemical shifts could yield the correct structure.

In some cases, however, XRD and SSNMR cannot be exploited to assess the candidate structures generated by CSP.

In fact, slight differences between a predicted structure and the correct structure may lead to important differences between experimental and calculated powder XRD data. [24,16] Furthermore, SSNMR may be hampered by the low resolution often encountered for 1 H SSNMR spectra, the limited 1 H chemical shift range, and by the sparse number of distinguishable 1 H resonances. In addition, although chemical shift is certainly the most easily accessible NMR observable, its isotropic value is not directly related to the crystal structure. An additional limitation is that DFT computation of chemical shifts is a lengthy process, which further restricts the potential of SSNMR for structural elucidation of organic powders. [22] Consequently, there is a requirement for additional SSNMR observables to improve the validation or the refinement of CSP candidate structures. Interestingly, some of us have shown that double-quantum (DQ) experiments based on 13 C-13 C dipolar couplings can yield quantitative information about the molecular conformation and the crystal packing of organic powders at natural abundance (NA). [25] The main advantage of this approach is that dipolar couplings are accessed through the corresponding dipolar curves, whose analytical expression is known from theory [26]. With the sole knowledge of the interatomic distances between the atoms in the crystal structure, dipolar curves can be straightforwardly calculated and plotted on a personal computer, with no need for first principle calculations. Therefore, an analytical function can be used to rapidly compare DQ 13 C-13 C dipolar data obtained experimentally with those calculated for model crystal structures. Because of the low natural abundance of 13 C nuclear spins, the detection and measurement of 13 C-13 C dipolar couplings in NA samples was only possible thanks to the optimal sensitivity enhancement provided by modern high-field dynamic nuclear polarization (DNP). [27,28,29,30,31,32,33,34,35,36,37,38,39] Importantly, DQ 13 C- 13 C dipolar data recorded on NA samples are directly related to short and long-range internuclear distances between the coupled spins because dipolar truncation can be neglected in the presence of naturally diluted 13 C-13 C spin pairs. [40,41,42,43] In this contribution, we show that a pool of structures generated using AIRSS [15] can be screened at minimal computational cost with the help of DQ dipolar 13 C-13 C build-up curves obtained by DNP SSNMR, demonstrating the sensitivity of these curves to a broad range of structural variations. We illustrate that this procedure can serve as a preliminary screening of the structures able to identify long range structural motifs that resemble those of the correct structure, allowing a coarse analysis of the structures before engaging in lengthy and computationally intensive structure refinement and chemical shift first principles calculations. The procedure is illustrated on theophylline, a molecular system that requires laborious crystallization work for XRD measurements, [44,45,46,47] and for which the structural determination method based on the combination of CSP and 1 H chemical shifts failed. [22]

| MATERIALS AND METHODS

| Sample preparation

Powdered anhydrous theophylline was purchased from Sigma Aldrich and used as received. The powder was identified as being polymorph II of theophylline by both SSNMR and X-ray diffraction, corresponding to BAPLOT06 refcode in the crystallographic structural database (CSD). The sample for DNP analysis was prepared through incipient wetness impregnation [48] of the powder with a 66 mM solution of the polarising agent TEKPol [49] in 1,1,2,2 -tetrabromoethane, as previously reported [25]. The wet powder was then transferred into a 3.2 mm sapphire rotor for DNP analysis.

Absence of structural modifications induced by the preparation procedure was confirmed by comparing the SSNMR 13 C spectra and X-ray diffraction patterns collected before and after the impregnation.

| Acquisition of quantitative dipolar data by DNP NMR

All the DNP NMR experiments were performed using a Bruker 9.4 T wide bore magnet operated by a Avance-III console and equipped with a 3.2 mm low-temperature double-channel 1 H-13 C, 29 Si CP-MAS probe. The sample temperature was 105 K and the spinning speed was 8 kHz. The NMR spectrometer was connected through a waveguide to a 263 GHz gyrotron delivering a micro-wave (MW) irradiation beam of 4 W at the probe. The field sweep coil of the NMR magnet was calibrated so that the MW irradiation of the gyrotron could match the maximum DNP enhancement obtained for TOTAPOL (263.3 GHz).

The NMR pulse sequence used to obtain the double quantum (DQ) 13 C-13 C dipolar build-up curves is shown in Fig. S4 (ESI). The POSTC7 scheme [50] was used to reintroduce the dipolar coupling interaction during both the excitation and the reconversion times τ D Q of a 2D DQ correlation experiment. Each 2D experiment consisted of 34 t 1 increments, each with 64 acquisitions. The total evolution time for the indirect dimension was 0.6 ms, and the spectral width 56 kHz.

States-TPPI procedure was applied to obtain pure-absorption spectra. A total of 9 2D DQ experiments were acquired by increasing the τ D Q between 0.25 and 8 ms, each of which lasted 7 h (total experimental time: 63 h).

| Experimental NMR data

To obtain the DQ build-up curves, several 2D DQ experiments were acquired by increasing the τ D Q , all the other parameters being unchanged. The volumes of the correlation peaks in each 2D spectrum (not shown here: see Ref. [25] for a full discussion of the data) were extracted and plotted as a function of τ D Q , providing DQ build-up curves for each 13 C- 13 C pair of theophylline. The DQ signals were scaled with respect to both the number of 13 C sites contributing to the correlation peak analysed and the intensity of the corresponding correlation peaks in the CP-MAS spectrum acquired under equivalent experimental conditions (i.e. same nutation frequency and contact time). The confidence intervals for the DQ intensity measurements were determined by integrating several signal-free regions of the spectra of the same frequency bandwidths as those used to estimate the peak integrals. The standard deviation of these noise integrals were taken as the experimental confidence limits.

| Theoretical dipolar build-up curves

For a full derivation of the analytical function describing the evolution of DQ signal amplitudes under a γ-encoded pulse sequence, the reader is referred to Ref. [26]. Briefly, the theoretical DQ build-up curves for an isolated spin pair interacting through dipolar coupling is the following:

s i j (τ D Q ) = 1 2 - 1 x √ 8 F c (x √ 2)cos2θ + F s (x √ 2)si n2θ (1)
where:

x = 2θ π (2) θ = 3 2 k C 7 b i j τ D Q (3)
In Eq. 1, F c and F s represent the cosine and sine Fresnel integrals, respectively, while in Eq. 3, k C 7 indicates the POSTC7 scaling factor, equal to 0.155, and b i j is the dipolar coupling constant between the spins i and j , which depends on their internuclear distance.

The analytical function used to mimic the DQ evolution of a carbon atom pair i j in a crystal has the following form:

S i j (τ D Q ) = A ex p(-2k • τ D Q ) N j j =1 s i j (τ D Q ) (4)
Importantly, Eq. 4 only requires the knowledge of the DQ recoupling scaling factor and the list of internuclear distances between the sites i and j in the crystal structure to allow the theoretical dipolar build-up curves to be calculated for this atom pair. The factor A defines the intensity of DQ signal, while k accounts for relaxation effects.

Relying on a purely analytical function, the calculation of the DQ build-up curves is immediate and allows a rapid screening of a large number of structures. For each crystal structure, the theoretical curves were obtained from Eq. 4 by taking into account all the internuclear distances between two chosen carbon atoms in the crystal up to a maximum distance of 6.5 Å. In the case of theophylline, including distances longer than 6.5 Å did not bring any significant modification to the curves. Importantly, these curves receive contributions from both intra-and intermolecular dipolar interactions: therefore, although they cannot be interpreted in terms of a single, specific, internuclear distance, they encode the crystal structure.

| Dipolar χ 2 calculation

For each computed structure, the 13 C-13 C dipolar χ 2 for the atom pair i j was calculated as:

χ 2 i j = τ D Q S ex p i j (τ D Q ) -S i j (τ D Q ) 2 (5)
where the experimental signal amplitude S ex p i j (τ D Q ) was obtained as described in Section 2.2. The global χ 2 discussed in the text was obtained by adding up the χ 2 i j for the 8 distinct i j atom pairs listed in Table S2 (ESI).

| Crystal structure prediction

A set of 33 candidate crystal structures was generated using the Ab Initio Random Structure Search (AIRSS) approach [51,15]. Structure prediction using AIRSS involves two stages: trial structures are first generated through the random search component, and then they are geometry optimised through a fully periodic DFT calculation (see next section).

For theophylline, the first stage of calculation used neutral theophylline molecules with fixed conformation taken from the X-ray determined structure (CSD entry code: BAPLOT06). All the structures were generated by constraining the space group to Pn21, corresponding to the space group observed for the CSD structure of theophylline. Therefore, all the structures had 4 molecules in the unit cell, whose dimensions were allowed to vary.

No constraints were applied to the unit-cell, ensuring that the resulting structures have different long-range arrangement in terms of mutual molecular distance and orientation. It is worth noting that, except for the reorientation of the methyl groups about their ternary axis, the (planar) molecule of theophylline does not have any degrees of freedom, therefore only minimal conformational differences are observed among the predicted structures. It follows that all the candidate structures predicted by AIRSS are pure packing polymorphs of theophylline.

| First-principles calculations

Geometry optimization of the structures predicted by AIRSS was carried out using the DFT code CASTEP, [52,53,54,55] which uses a plane wave basis set together with pseudo potentials to represent the core-valence interaction. All calculations used the PBE functional [56] and the dispersion correction scheme of Tkatchenko-Scheffler [57]. Calculations carried out on trial structures generated in the first stage of AIRSS search used a plane wave cut-off energy of 500 eV and a Brillouin zone sampling of 0.1×2π Å -1 . The convergence criteria were 0.05 eV Å -1 for forces, 0.1 GPa for stresses, 0.00002 eV per atom for energy and 0.001 Å for atomic displacements. Although the resulting structures are energetically stable, and can hence be considered physically meaningful, their energy lie within 60 kJ/mol from the lowest energy structure. These values are higher than the threshold of 10 kJ/mol typically reported in NMR crystallography studies. However, the scope of the present work was not to solve the structure of theophylline, but to propose a new approach to rapidly screen a set of physically meaningful candidate crystal structures. Therefore, no particular effort was put into obtaining refined structures that could resemble the true structure of theophylline. The lattice energies of each structure relative to the energy of the known crystal structure (referred to as CSD in the following) are reported in Table S1 and Fig. S1 (ESI).

| RESULTS AND DISCUSSION

The trial set of 33 candidate structures of anhydrous theophylline, generated by the AIRSS method, are indicated in the following with the corresponding number (1 to 33). In addition, the CSD structure is used here as a structural reference.

Structure numbering does not reflect increasing lattice energy. Because of the minimal conformational rearrangement allowed for theophylline molecules, variations in the 13 C-13 C dipolar curves between different predicted structures should be uniquely ascribed to differences in the mutual position and/or orientation of the -rigid -theophylline molecules, and not to intramolecular differences. Considering that dipolar couplings scale as 1/r 3 , intermolecular contributions are typically 1 order of magnitude smaller than intramolecular contributions (~100 Hz vs ~2000 Hz).

It follows that theophylline is an appropriate, yet challenging, system to probe the ability of such small variations in 13 C- 13 C dipolar couplings to provide a reliable tool to assess the quality of crystal structure candidates.

Fig. 1 shows experimental 13 C-13 C DQ dipolar build-up curves measured by MAS DNP for different carbon atom pairs of NA powdered anhydrous theophylline [25]. These data are compared with the theoretical build-up curves calculated for some of the candidate structures predicted by AIRSS. Specifically, in Fig. 1 the dipolar build-up curves calculated for the CSD structure are only compared with the curves for structures n.12 and 21 (panels b, d and f), and for structures n.32 and 33 (panels c, d and g) for the sake of clarity. The C-C pairs shown in Fig. 1 are representative of the variability of information contained in 13 C-13 C dipolar curves. For instance, while the C1-C6,7 pair (Fig. 1b andc) receives contributions from both intra and intermolecular couplings, only intermolecular interactions contribute to the C5-C5 pair (Fig. 1d ande). For all the spin pairs considered, the curves calculated for the CSD structure are in very good agreement with the experimental data. Interestingly, the dipolar curves plotted for the C1-C6,7 pair in Fig. 1b andc are in good agreement with the experimental data for the four predicted structures n.12, 21, 32 and 33.

Nonetheless, Fig. 1 also confirms that different crystal structures can have dipolar curves that almost superpose for certain atom pairs but differ significantly for others. This is illustrated in Fig. 1d ande, showing that the intermolecular C5-C5 distances are in agreement with the experimental data for structures n.12, 32 and 33, but not for n.21. The case of C6-C7 pair is particularly worth discussing. In fact, although the dipolar curves for C6-C7 are built from both intraand intermolecular contacts, in most of the candidate structures of theophylline considered in this analysis (including the case of the CSD structure) we observed that the shortest intermolecular contribution to the C6-C7 coupling was stronger than the intramolecular one. Dipolar data corresponding to atom pairs such as the C6-C7 of theophylline are hence expected to show high sensitivity to the crystal packing and are very precious for structure determination purposes. This is illustrated in Fig. 1f andg, showing that dipolar build-up curves calculated for the C6-C7 pair varied sensibly from one structure to the other, and only agrees with experimental data for structures n.32 and 33. Importantly, C1-C6,7

C5-C5 C6-C7

C6-C7

F I G U R E 1 DQ build-up curves for selected pairs of 13 C dipolar correlation peaks. Selected pairs of peaks are indicated by numbers on the top right of each graph, with the number in bold referring to the detected resonance. Labeling refers to the scheme in a). The experimental data points (blue plain circles) were obtained by integrating the corresponding correlation peaks obtained in a series of DNP-enhanced 2D 13 C-13 C DQ correlation spectra (see Ref. [25] and ESI) recorded for different τ DQ values on anhydrous theophylline. The solid lines represent the analytical function of the DQ build-up, which depends on the 13 C-13 C interatomic distances in structures: n.12 and 21 (b, d and f) and n.32 and 33 (c, e and g). The curve corresponding to the CSD structure is also shown as a dashed grey line. Evaluation of the deviation of each of these curves from the experimental data points (χ 2 ) allows easy screening of the whole set of trial structures at no computational cost.

for this atom pair, the best agreement between experimental data and calculated curves was neatly observed for the CSD structure. The method is hence sensitive to recurrent supramolecular motifs in molecular crystals, as it happens for ππ stacking in the case of theophylline. (a.u.)

Structure n.

CSD

F I G U R E 2 13 C-13 C global dipolar χ 2 for the crystal structures of theophylline generated using AIRSS. The χ 2 for the CSD structure is also shown for comparison. The dotted line is a guide to the eyes to identify the χ 2 minimum. The predicted structures showing the best agreement are highlighted in gray (n. 32 and 33).

The deviation between the experimental and theoretical 13 C-13 C DQ dipolar build-up curves can be used to quantify the goodness of a candidate structure. As described in section 2.2.3, such deviation can be expressed as the global dipolar χ 2 , reported in Fig. 2 for the all the predicted structures and the CSD structure. This value is obtained by adding the dipolar χ 2 obtained for 8 different 13 C- 13 C pairs (see Table S2, ESI). The results show that the best agreement is obtained for AIRSS structures n. 32 and 33. It must be noted that, differently from the RMSD typically used for evaluating the goodness of a structure based on the difference between calculated and experimental chemical shifts, χ 2 takes into account the experimental error in the determination of the dipolar coupling. Although a confidence interval for χ 2 values cannot be established using a set of dipolar data obtained on a single compound, structures n. 32 and 33 can be retained as good candidate structures in the present analysis because they show nearly identical values of χ 2 , which essentially coincide with the χ 2 for the CSD structure.

Importantly, the best agreement between the experimental and theoretical 13 C-13 C DQ dipolar build-up curves is obtained for two candidate structures of theophylline, n.32 and 33, that show structural features close to the CSD structure. In fact, as shown in Fig. 3 It is also interesting to consider the similarities of the selected structures in terms of their unit-cell parameters.

As shown in Fig. 4, three clusters of structures can be identified considering their unit-cell values. The distribution of the structures in the three clusters does not follow an energy criterium, since both high and low energy structures populate each of the clusters (Fig. S3, ESI). However, Fig. 4 shows that the unit cell parameters of structures n. 32 and 33 are nearly identical to those of the CSD structure. Globally, this analysis demonstrates that 13 C dipolar data are sensitive to both unit-cell dimensions and supramolecular arrangement. However, when carbon diluted, planar, molecules such as theophylline are investigated, 13 C dipolar data appear not sensitive enough to efficiently discriminate between structures only differing by in-plane rotation of one of the molecules. [58] Nonetheless, using dipolar data, the pool of AIRSS structures of theophylline examined in this study could be assessed within a few seconds on a standard computer, without the need of first principle calculations, i.e. at essentially no computational cost. Contrastingly, computation of chemical shifts from first principles would have required about 1.5 CPU hours per structure on 12 cores, plus the time necessary for full geometry optimization. Differently from previously proposed methods, the approach proposed here does not rank structures based on their lattice energy, but relies on purely structural parameters (i.e. the internuclear distances in the crystal) to identify likely candidate structures. It is important to point out that 13 C dipolar data such as those analysed in the present study does not contain any chemical information, such as the presence/type of chemical bonds or the occurrence of interatomic interactions. Notwithstanding these limitations, dipolar data helped identify a sub-set of candidate structures close to the global minimum without any preliminary structural assumption, reducing the initial pool of candidate structures of theophylline by more than 90%. The proposed method might be of interest in the perspective of investigating larger pools of candidate structures, for which NMR crystallography approaches based on chemical shifts are time consuming and computationally intensive. We suggest that, in the presence of large pools of properly geometryoptimized structures, calculation of chemical shifts could be restrained to the sub-set of structures identified by dipolar data, as a structure refinement. Therefore, the combination of dipolar data and chemical shifts could constitute a new protocol for faster and reliable structure determination of organic compounds at natural isotopic abundance that could considerably simplify the analysis of large sets of trial crystal structures. This work is currently in progress in our group and will make the object of a future study.

| CONCLUSIONS

The results presented here suggest that 13 C-13 C DQ dipolar build-up curves acquired at natural isotopic abundance using DNP SSNMR might be used to sort out a pool of predicted crystal structures within a few seconds. This procedure is complementary to approaches based on NMR chemical shifts and PXRD experiments and a combination of all these techniques should allow in the future to solve the structure of unknown organic compounds at natural isotopic abundance. It should be noted that 13 C-13 C DQ dipolar experiments can also be used to establish 13 C-13 C connectivities, [59] which ensures that the chemical structure of the predicted structures is reliable. In this form, we believe that DNP SSNMR experiments performed on natural abundance samples will be helpful to investigate the structure of powders that cannot be characterized using standard techniques.

  , theophylline molecules in these structures have very similar supramolecular arrangement (i.e. similar mutual arrangement, similar location in the unit cell) but differ in their mutual orientation, leading to different intermolecular interactions. Notably, while in the CSD structure the amino proton is hydrogen bonded to the aldimine nitrogen (NH• • • N), the amino proton in structure n.33 is hydrogen bonded to the carbonyl oxygen neighboring the imidazole ring (NH• • • O).

3

 3 View down one of the crystallographic axes of three candidate crystal structures of theophylline. Structure a) corresponds to CSD structure and coincides with the structure deposited in the CSD. This structure is stabilized by the presence of N-H• • • N hydrogen bonds. In b), corresponding to predicted structure n.33, this pattern is substituted by N-H• • • O hydrogen bonds. These two crystal structures are virtually indistinguishable on the basis of their global dipolar χ 2 . Despite being very similar to a), structure c), corresponding to predicted structure n.32, does not allow the formation of hydrogen bonds: this structure is characterized by slightly larger dipolar χ 2 than a) and b). Crystal structures n.12 and 21 are also shown in Fig. S2 (ESI) for comparison.

  U R E 4 Unit-cell parameters for structures resulting from the AIRSS calculation on theophylline. Three clusters of structures can be identified based on the values of the unit-cell axes, indicated by the grey areas. Unit-cell parameters of structures n. 32 and 33, selected on the basis of dipolar data, are very similar to those of the CSD structure.

Funding information

European Research Council, Grant/Award Number: 758498, EPRSC, Grant/Award Number: EP/M022501/

A C K N O W L E D G E M E N T S

GM acknowledges funding from the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (grant agreement No 758498). First principles calculations were performed using the HPC resources of Aix-Marseille Université financed by the project Equip@Meso (ANR-10-EQPX-29-01) of the program "Investissements d'Avenir" supervised by the Agence Nationale de la Recherche. JRY and SS are supported by the Collaborative Computational Project for NMR Crystallography funded by the EPSRC (UK) Grant No. EP/M022501/1.

C O N FL I C T O F I N T E R E S T

The authors declare no conflict of interest.

E N D N O T E S R E F E R E N C E S GRAPHICAL ABSTRACT

AIRSS


Pierre Thureau

Marie Juramy

Fabio Ziarelli

Stéphane Viel

Giulia Mollica
email: giulia.mollica@univ-amu.fr

Brute-force solvent suppression for DNP studies of powders at natural isotopic abundance

Keywords: Dynamic nuclear polarization, Natural isotopic abundance, Sample preparation, Powder, Solvent suppression Highlights

come

Introduction

The recent development of high-field dynamic nuclear polarization (DNP) has enabled a wide range of molecular systems to be thoroughly investigated. [1][START_REF] Blanc | Dynamic Nuclear Polarization NMR Spectroscopy Allows High-Throughput Characterization of Microporous Organic Polymers[END_REF][START_REF] Mollica | Observing Apparent Nonuniform Sensitivity Enhancements in Dynamic Nuclear Polarization Solid-State NMR Spectra of Polymers[END_REF][START_REF] Lilly Thankamony | Insights into the Catalytic Activity of Nitridated Fibrous Silica (KCC-1) Nanocatalysts from 15N and 29Si NMR Spectroscopy Enhanced by Dynamic Nuclear Polarization[END_REF][START_REF] Jantschke | Insight into the Supramolecular Architecture of Intact Diatom Biosilica from DNP-Supported Solid-State NMR Spectroscopy[END_REF][START_REF] Geiger | Studying the Conformation of a Silaffin-Derived Pentalysine Peptide Embedded in Bioinspired Silica using Solution and Dynamic Nuclear Polarization Magic-Angle Spinning NMR[END_REF][START_REF] Hirsh | 35Cl dynamic nuclear polarization solid-state NMR of active pharmaceutical ingredients[END_REF][START_REF] Kobayashi | DNP-Enhanced Ultrawideline Solid-State NMR Spectroscopy: Studies of Platinum in Metal-Organic Frameworks[END_REF][START_REF] Smith | Dynamic Nuclear Polarization as an Enabling Technology for Solid State Nuclear Magnetic Resonance Spectroscopy[END_REF][START_REF] Mehler | Chromophore Distortions in Photointermediates of Proteorhodopsin Visualized by Dynamic Nuclear Polarization-Enhanced Solid-State NMR[END_REF][11][START_REF] Lilly Thankamony | Dynamic nuclear polarization for sensitivity enhancement in modern solid-state NMR[END_REF][START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF][START_REF] Leroy | Hydroxyapatites: Key Structural Questions and Answers from Dynamic Nuclear Polarization[END_REF][START_REF] Caracciolo | Proton and Carbon-13 Dynamic Nuclear Polarization of Methylated β-Cyclodextrins[END_REF][START_REF] Rossini | Materials Characterization by Dynamic Nuclear Polarization-Enhanced Solid-State NMR Spectroscopy[END_REF] These results have been obtained thanks to the development of hardware, [START_REF] Gerfen | High frequency (140 GHz) dynamic nuclear polarization: Polarization transfer to a solute in frozen aqueous solution[END_REF][START_REF] Ni | High Frequency Dynamic Nuclear Polarization[END_REF][START_REF] Bouleau | Pushing NMR sensitivity limits using dynamic nuclear polarization with closed-loop cryogenic helium sample spinning[END_REF][START_REF] Kemp | Dynamic Nuclear Polarization enhanced NMR at 187GHz/284MHz using an Extended Interaction Klystron amplifier[END_REF][START_REF] Saliba | Pulsed Electron Decoupling and Strategies for Time Domain Dynamic Nuclear Polarization with Magic Angle Spinning[END_REF][START_REF] Can | Frequency-Swept Integrated and Stretched Solid Effect Dynamic Nuclear Polarization[END_REF][START_REF] Rosay | Instrumentation for solid-state dynamic nuclear polarization with magic angle spinning NMR[END_REF][START_REF] Thurber | Low-temperature dynamic nuclear polarization with heliumcooled samples and nitrogen-driven magic-angle spinning[END_REF][START_REF] Matsuki | Advanced instrumentation for DNP-enhanced MAS NMR for higher magnetic fields and lower temperatures[END_REF] polarizing agents, [START_REF] Corzilius | High-Field Dynamic Nuclear Polarization with High-Spin Transition Metal Ions[END_REF][START_REF] Sauvée | Highly Efficient, Water-Soluble Polarizing Agents for Dynamic Nuclear Polarization at High Frequency[END_REF][START_REF] Zagdoun | Large Molecular Weight Nitroxide Biradicals Providing Efficient Dynamic Nuclear Polarization at Temperatures up to 200 K[END_REF][START_REF] Mathies | Efficient Dynamic Nuclear Polarization at 800 MHz/527 GHz with Trityl-Nitroxide Biradicals[END_REF][START_REF] Bothe | Novel Biradicals for Direct Excitation Highfield Dynamic Nuclear Polarization[END_REF][START_REF] Geiger | Efficiency of Water-Soluble Nitroxide Biradicals for Dynamic Nuclear Polarization in Rotating Solids at 9.4 T: bcTol-M and cyolyl-TOTAPOL as New Polarizing Agents[END_REF][START_REF] Mentink-Vigier | Computationally Assisted Design of Polarizing Agents for Dynamic Nuclear Polarization Enhanced NMR: The AsymPol Family[END_REF][START_REF] Wisser | BDPA-Nitroxide Biradicals Tailored for Efficient Dynamic Nuclear Polarization Enhanced Solid-State NMR at Magnetic Fields up to 21[END_REF] and theory. [START_REF] Mentink-Vigier | Fast passage dynamic nuclear polarization on rotating solids[END_REF][START_REF] Thurber | Perturbation of nuclear spin polarizations in solid state NMR of nitroxide-doped samples by magic-angle spinning without microwaves[END_REF][START_REF] Mentink-Vigier | Theoretical aspects of Magic Angle Spinning -Dynamic Nuclear Polarization[END_REF][START_REF] Mance | The magnetic field dependence of cross-effect dynamic nuclear polarization under magic angle spinning[END_REF][START_REF] Daube | Heteronuclear Cross-Relaxation under Solid-State Dynamic Nuclear Polarization[END_REF][START_REF] Perras | In Silico Design of DNP Polarizing Agents: Can Current Dinitroxides Be Improved?[END_REF][START_REF] Hoffmann | Unusual Local Molecular Motions in the Solid State Detected by Dynamic Nuclear Polarization Enhanced NMR Spectroscopy[END_REF][START_REF] Kundu | Electron-Electron Cross-Relaxation and Spectral Diffusion during Dynamic Nuclear Polarization Experiments on Solids[END_REF] Among these developments, the study of samples at natural isotopic abundance thrived, [START_REF] Takahashi | Rapid Natural-Abundance 2D 13C-13C Correlation Spectroscopy Using Dynamic Nuclear Polarization Enhanced Solid-State NMR and Matrix-Free Sample Preparation[END_REF][START_REF] Rossini | Dynamic Nuclear Polarization Enhanced NMR Spectroscopy for Pharmaceutical Formulations[END_REF][START_REF] Mollica | Quantitative Structural Constraints for Organic Powders at Natural Isotopic Abundance Using Dynamic Nuclear Polarization Solid-State NMR Spectroscopy[END_REF][START_REF] Smith | Structural Fingerprinting of Protein Aggregates by Dynamic Nuclear Polarization-Enhanced Solid-State NMR at Natural Isotopic Abundance[END_REF][START_REF] Perras | Optimal sample formulations for DNP SENS: The importance of radical-surface interactions[END_REF] especially as a result of efficient methodologies for sample preparation. Indeed, Rossini et al. have demonstrated that powders can be impregnated with a radical-containing solution, [START_REF] Rossini | Dynamic Nuclear Polarization NMR Spectroscopy of Microcrystalline Solids[END_REF] where the polarizing agent (PA) remains at the surface of the powder particles and the DNP-enhanced 1 H polarization is transferred to the core of the particle through 1 H-1 H spin-diffusion. [START_REF] Pinon | Measuring Nano to Micro Structures from Relayed DNP NMR[END_REF] This approach yielded large DNP sensitivity enhancements, which allowed samples at natural isotopic abundance to be fully characterized. Morevover, this methodology preserves the resolution of the NMR spectra because the PA remains distant from the nuclear spins that eventually contribute to the detected NMR signal.

A drawback of this method is that the solvent used to dissolve the PA is strongly enhanced by DNP, which usually results in a large solvent signal observed in the DNP-enhanced 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CP NMR spectra of impregnated samples. Several methods have been proposed to suppress this signal, such as solvent-free preparation methods, [START_REF] Takahashi | Matrix-free dynamic nuclear polarization enables solid-state NMR 13C-13C correlation spectroscopy of proteins at natural isotopic abundance[END_REF][START_REF] Le | Optimizing Sample Preparation Methods for Dynamic Nuclear Polarization Solid-state NMR of Synthetic Polymers[END_REF][START_REF] Kobayashi | Improved strategies for DNP-enhanced 2D 1H-X heteronuclear correlation spectroscopy of surfaces[END_REF] relaxation filters [START_REF] Yarava | Solvent suppression in DNP enhanced solid state NMR[END_REF] or dipolar dephasing. [START_REF] Lee | Solvent signal suppression for high-resolution MAS-DNP[END_REF] The main advantage of these solvent-suppression methods is their versatility (although the method based on dipolar dephasing requires a triple-channel probe). In fact, these methods can be applied to different kinds of samples, such as frozen solutions, [START_REF] Hall | Polarization-Enhanced NMR Spectroscopy of Biomolecules in Frozen Solution[END_REF] porous materials [START_REF] Kobayashi | Measuring Long-Range 13C-13C Correlations on a Surface under Natural Abundance Using Dynamic Nuclear Polarization-Enhanced Solid-State Nuclear Magnetic Resonance[END_REF] or impregnated powders.

Unfortunately, methods based on relaxation filters or dipolar diphasing not only affect the intensity of the solvent signal but also the intensity of the signals belonging to the sample under study. The resulting loss in signal intensity may be significant and could prove detrimental to the overall signal-to-noise ratio of the DNP-enhanced NMR spectra, especially for samples at natural isotopic abundance. In addition, this intensity reduction may not be uniform for the different resonances of the sample under study.

In this contribution, we investigate the influence of using highly concentrated radical solutions when recording DNP-enhanced spectra on impregnated powders. More specifically, we demonstrate that increasing the concentration of radicals allows the signal of the solvent to be effectively suppressed, without affecting the intensity or the resolution of the signals due to the sample of interest. The methodology proposed here is illustrated on samples that were impregnated either with an organic or an aqueaous radical-containing solution.

Results and Discussion

Figure 1 shows the 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CP MAS spectra of 3-aminobenzoic acid, which has been impregnated with two distinct solutions of TEKPol in 1,1,2,2-tetrachlroethane (TCE), namely 17 mM and 80 mM. Typically, the usual concentration of a radical solution used for incipient wetness impregnation is ~16 mM, [START_REF] Rossini | Dynamic Nuclear Polarization NMR Spectroscopy of Microcrystalline Solids[END_REF][START_REF] Pinon | Polymorphs of Theophylline Characterized by DNP Enhanced Solid-State NMR[END_REF] which is significantly less than the concentration of 80 mM used in this study. Interestingly, both spectra shown in Figure 1 have comparable resolution but the signal arising from TCE is not observable in the spectrum recorded for the sample impregnated with the TCE solution of TEKPol at 80 mM (Figure 1a). In fact, the high concentration of the PA leads to strong paramagnetic relaxation that shortens the apparent relaxation times of the nuclear spins close to the PA. In the case of incipient wetness impregnation of powders, the relaxation of the nuclei belonging to the solvent is thus considerably affected by this paramagnetic relaxation, while the relaxation of the remote nuclei belonging to the core of the powder particle is much less altered.

The 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CP MAS spectra of 3-aminobenzoic acid recorded at a temperature of ~108 K and shown in Figure 2 underline the impact of the solvent and PA on the spectral linewidths of the NMR signal after incipient wetness impregnation. Interestingly, Figure 2 shows that the broadening of the NMR signal arises mainly from the presence of the solvent rather than from the presence of the PA. In fact, the 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CP MAS spectra shown in Figure 2a and 2b, which were both recorded after impregnation with TCE, have nearly identical spectral linewidths, despite the fact that the spectrum shown in Figure 2b has been recorded in the absence of PA. It should also be noted that the 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CP MAS spectrum shown in Figure 2c and recorded for the pure powder displays slightly narrower linewidths. This results confirms the minor impact of the PA on the [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C coherence lifetimes (T2'), and hence on the resolution of NMR spectra of samples that have been impregnated with a high concentration of PA. It should also be noted that identical measurements have been performed for two additional radical concentrations, namely 30 mM and 55 mM. Interestingly, the signal of TCE was quenched for the sample impregnated with the 55 mM radical solution while the signal of TCE was still observed in the spectrum recorded for the sample impregnated with the 30 mM solution.

Furthermore, we observed that the highest sensitivity per unit time was obtained for the sample impregnated with the 80 mM radical solution. The evolution of the DNP enhancement eCP and the S/N per experimental time are given in the Supporting Information.

These favorable experimental conditions allowed two-dimensional NMR experiments to be carried out for the sample of 3-aminobenzoic acid at natural isotopic abundance. For example, Figure 3 shows the double quanta (DQ)-single quantum (SQ) [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C correlation 2D spectrum obtained for the sample impregnated with the 80 mM TEKPol in TCE. In this experiment, the method of Dekhil et al. has been used to reintroduce the 13 C- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C dipolar coupling during a short dipolar recoupling time (0.4 ms), allowing carbon-carbon connectivities to be established. [START_REF] Dekhil | Determining carbon-carbon connectivities in natural abundance organic powders using dipolar couplings[END_REF] This spectrum was recorded in less than 8 hours and displays excellent signal-to-noise ratio and resolution. Finally, it should be noted that the herein proposed solvent suppression method does not exclusively work with TCE. In fact, as examplified in Figure 4, similar results can be obtained for samples impregnated with a glycerol/water solution. In this case, a sample of L-aspartic acid at natural isotopic abundance was impregnated with a solution of 45 mM of AMUPol in glycerol/D2O/H2O (60/30/10). At a temperature of 108 K, two intense and broad [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C resonances are usually observed at 72 and 62 ppm for the frozen glycerol. [START_REF] Pinon | Polymorphs of Theophylline Characterized by DNP Enhanced Solid-State NMR[END_REF] In the spectrum shown in Figure 4, the intensity of the signals arising from glycerol were effectively reduced and are now more than 50 times less intense than the signals arising from L-aspartic acid. It should also be noted that the overall signal-to-noise ratio of the spectrum shown in Figure 4 is excellent, thanks to a DNP enhancement eCP=65 and a T1( 1 H) of 16 s (estimated with a mono-exponential-fitting), and that the high resolution of the 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CP spectrum has been preserved. The L-aspartic sample acid at natural isotopic abundance was obtained from Sigma Aldrich and used without further purification. Several polymorphic forms exist for 3-aminobenzoic acid. Here we prepared and studied the polymorphic form III. [START_REF] Williams | Discovery of a New System Exhibiting Abundant Polymorphism: m-Aminobenzoic Acid[END_REF] Before impregnation, the samples were finely ground by hand in a mortar and pestle for several minutes. The 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CPMAS spectra were inspected before and after grinding and impregnation to monitor a potential evolution of the polymorphic form III of 3-aminobenzoic acid; all 1 H-13 C CPMAS spectra were identical to the 1 H- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CPMAS spectrum reported by Hughes et al. for form III of 3-aminobenzoic acid. [START_REF] Hughes | CLASSIC NMR": An In-Situ NMR Strategy for Mapping the Time-Evolution of Crystallization Processes by Combined Liquid-State and Solid-State Measurements[END_REF] The organic solvents utilized for impregnation were visually screened to evaluate whether the powder samples were insoluble.

Material and methods

Sample preparation

The sample of 3-aminobenzoic acid (~ 40 mg) was impregnated with 20 µL of a solution of TEKPol in TCE/TCE-d2 (20/80; v/v). Different concentrations of TEKPol were used and the concentration of 80 mM yielded optimal overall sensitivity enhancement (see Figure S2 of the Supporting Information). The PAs were dissolved in the desired solvent and no undissolved PA was observed by visual inspection of the solution. The wet powder was then mixed with a spatula and transferred to a sapphire rotor. The weight of the impregnated powder transferred into the rotor was 34 mg. Freeze-thaw (i.e. insert-eject) cycling did not lead to significant modifications of the eCP for the sample of 3-aminobenzoic acid.

For L-aspartic acid, 30 mg of powder was impregnated with 11 µL of a solution of 45 mM of AMUPol in glycerol-d8/D2O/H2O (60/30/10; v/v/v); the weight of the impregnated powder transferred into the rotor was 29 mg.

NMR experiments

All DNP NMR experiments were recorded on a Bruker 9.4 T wide-bore magnet (400 and 100 MHz for the 1 H and [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C Larmor frequency, respectively) operated by an AVANCE-III NMR spectrometer and equipped with a Bruker 3.2 mm low-temperature double-resonance DNP 1 H/ [START_REF] Mathies | Efficient Dynamic Nuclear Polarization at 800 MHz/527 GHz with Trityl-Nitroxide Biradicals[END_REF] Si/ 13 C CPMAS probe. The spectrometer was equipped with a gyrotron that allowed microwave (MW) irradiation of the sample. Specifically, the field sweep coil of the NMR magnet was set so that MW irradiation occurred at the maximum DNP enhancement of TOTAPOL (263.334 GHz). The estimated power of the MW beam at the output of the probe waveguide was 4 W.

The sample temperature was ~108 K for the 1 H-13 C CP MAS spectra recorded under microwave irradiation. The spinning frequency was set to 10 kHz for the experiments performed on 3aminobenzoic acid and 8 kHz for the experiments performed on L-aspartic acid. The amplitude of the 1 H rf field was ramped during the contact time to improve efficiency. The recycle delay was 10 s and the CP contact time was 2.5 ms. The 1D spectra were were recorded with 4 repetitions and 2 dummy scans. The 1 H T1 relaxation times were measured with the saturation/recovery pulse sequence and the [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C cohenrece lifetimes (T2') were measured using the CPMAS spin echo experiment.

The 13 C- [START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C DQ/SQ dipolar correlation 2D spectrum was recorded using the R20 9 2 symmetry. [START_REF] Carravetta | Estimation of Carbon-Carbon Bond Lengths and Medium-Range Internuclear Distances by Solid-State Nuclear Magnetic Resonance[END_REF][START_REF] Edén | Advances in Symmetry-Based Pulse Sequences in Magic-Angle Spinning Solid-State NMR[END_REF] The DQ excitation and reconversion delays were set to 0.4 ms in order to select dipolar 13 C-13 C couplings beteween covalently bonded 13 C-13 C spin pairs; [START_REF] Dekhil | Determining carbon-carbon connectivities in natural abundance organic powders using dipolar couplings[END_REF] 37 t1 increments and 96 repetions for each increment were used with a repetition delay of 4 s.

Conclusion

We have demonstrated that the signal of the solvent used for preparing DNP samples by incipient wetness impregnation can be conveniently quenched by paramagnetic relaxation using highly concentrated radical-containing solutions for impregnating the sample. This approach is compatible for both aqueous and organic solvents, and preserves the resolution of the spectrum and the overall sensitivity enhancement. Contrary to the other solvent suppression methodologies proposed previously, the method presented here does not require long dephasing times to suppress the signal of the solvent, and hence does not lead to signal loss for the sample under study. As a result, this methodology is suitable for the study of samples at natural isotopic abundance where maximal signal-to-noise ratios are sought.

Acknowledgement

We thank K. M. D Harris, C. E Hughes and A. Willamson for providing the polymoprphic form III of 3-aminobenzoic acid. We also thank Bruker SAS for access to the solid-state NMR spectrometer used here and Dr F. Aussenac and Dr P. Dorffer for experimental assistance. This study has received funding from the Excellence Initiative of Aix-Marseille University -A*Midex, a French "Investissements d'Avenir" programme supported of the A*MIDEX project (no. ANR-

Figure 1 :

 1 Figure 1: DNP-enhanced NMR 1 H-13 C CP MAS spectra of 3-aminobenzoic acid as a powder and impregnated with a TCE solution of TEKPol at (a) 80 mM, and (b) 17 mM. Both spectra were recorded using a relaxation delay of 10 s, at a temperature of 108 K and with a MAS frequency of 10 kHz. The

  record each spectram was 60 s. The noise for each spectrum is shown in the inset. (The symbol * indicates a spinning side band).

Figure 2 :

 2 Figure 2: 1 H-13 C CP MAS spectra of 3-aminobenzoic acid recorded with microwaves irradiation on (a) the powder impregnated with a TCE solution of TEKPol at 80 mM, (b) the powder impregnated with TCE, and (c) the pure powder. All spectra were recorded at a temperature of 108 K and a MAS frequency of 10 kHz. The experimental time to record each spectrum was 80 s.

Figure 3 :

 3 Figure 3: 13 C-13 C DQ/SQ dipolar correlation 2D spectrum recorded for a sample of natural abundance 3-aminobenzoic acid. The spectrum was recorded with microwave irradiation and for the powder

Figure 4 :

 4 Figure 4: 1 H-13 C CP MAS spectra of L-aspartic acid at natural abundance recorded for the powder impregnated with a 45 mM solution of AMUPol in glycerol/D2O/H2O (60/30/10). The spectrum was recorded with microwave irradiation and at a temperature of 108 K. The experimental time to record the spectrum was 120 s. (The symbol * indicates a spinning side band)

Table 1 :

 1 Relaxation properties, DNP signal enhancements and sensitivity (expressed as the S/N of the[START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C CPMAS spectrum normalized to the square root of the experimental time) measured for 3aminobenzoic acid impregnated with a TCE solution of TEKPol. , a higher sensitivity (expressed as the S/N of the 13 C CPMAS spectrum normalized to the square root of the experimental time) is obtained for the sample impregnated with the 80 mM TEKPol/TCE solution with respect to the sample impregnated with the 17 mM TEKPol/TCE solution. In fact, as shown in Table1, both samples have comparable[START_REF] Sani | Probing Ionic Liquid Electrolyte Structure via the Glassy State by Dynamic Nuclear Polarization NMR Spectroscopy[END_REF] C coherence lifetimes (T2') but the 1 H longitudinal relaxation time (T1) and the DNP signal enhancement (eC,CP) are more favorable for the sample impregnated with the 80 mM TEKPol/TCE solution. As a result, the overall sensitivity enhancement is ~4 times higher for the sample impregnated with the 80 mM TEKPol/TCE solution. This result is in full agreement with the previous results reported byMollica et al. for theophylline, where the maximal overall sensitivity was obtained for the powder impregnated with a 66 mM solution of TEKPol in EtBr4.[START_REF] Mollica | Quantitative Structural Constraints for Organic Powders at Natural Isotopic Abundance Using Dynamic Nuclear Polarization Solid-State NMR Spectroscopy[END_REF]

	Concentration of	T1 a ( 1 H) and T1 b ( 1 H) a	A	B	T2'( 13 C) b	eCP	S/N x t -1/2
	TEKPol in TCE	(s)			(ms)
	(mM)
	17	2.4±0.1 and 105±7	0.40	0.60	38±3	26	12
	80	0.60±0.1 and 45±5	0.35	0.65	37±3	76	53
	a Data obtained with a biexponential fitting of the form S(t)= A exp[-t/T1 a ]+ B exp[-t/T1 b ]

b Measured for the fastest decaying signal, i.e. the C2 resonance.

Furthermore

C Chemical Shift (ppm)