An unexploited dimension: new software for mixture analysis by 3D

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An unexploited dimension: new software for mixture analysis by 3D diffusion-ordered NMR spectroscopy Guilherme Dal Poggetto, Laura Castañar, Mohammadali Foroozandeh, Peter Kiraly, Ralph W. Adams, Gareth A. Morris, and Mathias Nilsson Anal. Chem., Just Accepted Manuscript • DOI: 10.1021/acs.analchem.8b04093 • Publication Date (Web): 29 Oct 2018 Downloaded from http://pubs.acs.org on October 29, 2018

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Analytical Chemistry

An unexploited dimension: new software for mixture analysis by 3D diffusion-ordered NMR spectroscopy Guilherme Dal Poggetto*, Laura Castañar, Mohammadali Foroozandeh, Peter Kiraly, Ralph W. Adams, Gareth A. Morris and Mathias Nilsson* School of Chemistry, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom ABSTRACT: 3D DOSY experiments have the potential to provide unique and valuable information but are underused, in part due to the lack of efficient processing software. Here we illustrate the power of 3D DOSY and present MAGNATE (Multidimensional Analysis for the GNAT Environment), an open-source and free software package for the analysis of pulsed field gradient (PFG) 3D NMR diffusion data, distributed under the GNU General Public Licence. The new software makes it possible for the first time to efficiently analyse and visualise 3D diffusion (e.g. 3D HSQC-DOSY) data using both univariate (e.g. DOSY) and multivariate methods (e.g. OUTSCORE) in a user-friendly graphical interface. The software can be used either independently, or as a module in the GNAT programme. Diffusion NMR is a powerful technique for the study of intact mixtures, providing valuable information about both relative and absolute sizes of, and interactions between, species.1-9 (The general term “Diffusion NMR” is used here in preference to common terms such as PGSE and DOSY, which refer only to specific subsets of NMR methods for the study of diffusion). A typical experiment consists of a series of 1D (e.g. 1H) spectra recorded using a pulsed field gradient (PFG) spin or stimulated echo.10 The diffusion coefficient (𝐷) is measured by fitting the net signal attenuation (𝑆(𝑔)) as a function of gradient strength (𝑔) to the Stejskal-Tanner equation:11 𝑆(𝑔) = 𝑆0 𝑒 ―(𝐷 𝛾

2

𝛿2 𝑔2 ∆′)

(1)

where 𝑆0 is the signal amplitude (or signal integral) at zero gradient, 𝛾 is the magnetogyric ratio of the analysed nucleus, 𝛿 is the gradient pulse duration, and ∆′ is the time between the midpoints of the defocusing and refocusing periods less a small correction for diffusion during the gradient pulses. The most useful presentation of such data is arguably a pseudo-2D spectrum, in which the indirect dimension is the diffusion coefficient (Figure 1). This processing method is referred to as Diffusion-Ordered SpectroscopY (DOSY).12-13 In liquid state NMR, at high fields, the HighResolution DOSY (HR-DOSY)2 approach is commonly used to determine the number, and physical properties, of molecular species in a mixture. In HR-DOSY, every signal in the 1D spectrum is fitted to an appropriate form of the mono-exponential Stejskal-Tanner equation (Equation 1), implicitly assuming that any individual signal derives only from one species.14-15 The DOSY spectrum is then constructed with a Gaussian shaped

signal in the diffusion dimension, centred on the diffusion coefficient and with a width reflecting the fitting uncertainty. DOSY is commonly used to identify signals from distinct components in a mixture. Provided that the species in solution show different diffusion coefficients, this works very well when there is no signal overlap in the spectrum. When signals do overlap, however, as is common (especially for 1H NMR), the HR-DOSY assumption breaks down. The fitted diffusion coefficient is now a compromise between those of the species that contribute to the signal fitted, making useful interpretation of the DOSY spectrum difficult, or even impossible. An example of a 2D DOSY spectrum for a system showing significant overlap in the proton spectrum is given in Figure 1, presenting data obtained from a mixture of five different vitamin B species.

Figure 1. 2D DOSY plot for a mixture of five B vitamins (biotin B7, pyridoxine hydrochloride B6, pantothenic acid calcium complex B5, niacin B3, and thiamine hydrochloride B1), in DMSO-d6, obtained using the Oneshot pulse sequence.16 Here it is impossible, due to spectral overlap,

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to determine unambiguously how many species are present in the mixture.

Advanced processing methods can restore interpretability, but they typically require both a difference in diffusion coefficient of 30% or more, and very high quality experimental data.17-18 A more desirable solution, by far, is to eliminate spectral overlap so that the HR-DOSY assumption holds. An increase in field strength is an obvious (if expensive) solution to the signal overlap problem, but even with bottomless coffers this cannot provide better than a factor of 2 improvement over a standard 500 MHz spectrometer. Fortunately, we are not limited to simple 1H NMR as the base experiment; diffusion encoding can be added to virtually any NMR experiment, of arbitrary dimensionality. The use of nuclei with sparse spectra (such as 19F,19-21 31P,22-23 or 13C,24-25) in DOSY experiments can be very effective when such isotopes can be used, facilitating the study of complex mixtures. However, when using 1H (the most abundant and most widely used nucleus in liquid state NMR) the need to solve the problem of overlap of signals has been the focus of research into new acquisition and

Figure 2. a) Pseudo-3D DOSY plot of the Oneshot-HSQC spectrum of the same sample as in Figure 1, b) 2D projection along F2, and c) 2D projection along F1. F1 and F2 are the 13C and 1H dimensions respectively. Dashed lines are used to aid visualization and indicate biotin B7 (red), hydrochloride B6 (purple), pantothenic acid B5 (blue), pyridoxine and niacin B3 (orange) and thiamine hydrochloride B1 (green) peaks respectively. The spectra from the five components are easily distinguished, with diffusion coefficients of 1.05, 1.12, 1.18, 1.36 and 1.64 ×10-

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m2 s-1 for vitamins B1, B7, B5, B6 and B3, respectively. Data acquisition took 45 h, but a much shorter acquisition would have sufficed.

processing methods since the early days of NMR. The advent of pure shift NMR spectroscopy,26-29 in which the effects of homonuclear couplings are suppressed and a single peak is observed for each chemically distinct site, has allowed an impressive improvement in resolution and reduction in signal overlap. The usefulness of pure shift 1H DOSY methods to solve overlapping problems has been proved, albeit at a cost in signal-to-noise ratio that reduces resolution in the diffusion domain.30-33 Another efficient way to minimise signal overlap is to add a diffusion dimension to a 2D (or higher dimensionality) NMR experiment, generating 3D (or higher) DOSY spectrum (Figure 2a).34-47 Thus Figure 2a shows that the mixture yielding the unresolved 2D DOSY spectrum of Figure 1 is perfectly resolved using 3D HSQC-DOSY. The use of HSQC as the base experiment provides a major reduction in the probability of overlap by increasing the sparsity of the spectral dimensions, spreading correlation signals out over the full 2D plane instead of along a single frequency axis. Each peak in the diffusion dimension (i.e. the third dimension) is, as in 2D DOSY, generated by fitting cross-peak volumes as a function of gradient amplitude to the appropriate form of the Stejskal-Tanner equation and constructing a Gaussian peak centred at the fitted diffusion coefficient and with a width determined by the statistics of the fit.2 Thus, 3D DOSY spectra are made up of F1-F2 planes (or layers) containing signals of species diffusing with the same diffusion coefficient D. The potential of 3D diffusion experiments has been explored in the literature in a significant number of publications. Perhaps the most obvious way to construct a 3D diffusion experiment is by concatenating a conventional 2D pulse sequence with a diffusionencoding sequence, as in COSY-DOSY,34 DOSYTOCSY,35 DOSY-NOESY,36 2DJ-DOSY,39 DOSYTROSY,46 DOSY-HSQC44 and DOSY-HMQC,38 among others. The nomenclature used here reflects the order in which pulse sequence blocks are concatenated: thus the COSY-DOSY sequence consists of a COSY sequence followed by a diffusion-encoding step. An alternative strategy for designing 3D diffusion experiments is to incorporate diffusion encoding into the conventional 2D experiment (an approach known as iDOSY), where the parent pulse sequence can accommodate a diffusion delay of several tens of ms.37 This approach can be simpler, and usually has less sensitivity loss due to relaxation. A number of 3D iDOSY experiments can be found in the literature, including TOCSY-iDOSY,37 COSY-iDOSY,41 2DJ-iDOSY,37, 48 HSQC-iDOSY43, 45, 47 and HMQC-iDOSY40. One potential drawback of 3D experiments is that they can be time-consuming, but great reductions in experiment time are possible with modern non-uniform sampling (NUS) schemes.49-55 3D DOSY experiments have, however, failed to find much real application, in part due to the lack of a suitable processing platform. Naturally, the extra dimension, although offering a great advantage, adds a layer of complexity to the processing, and current processing


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Analytical Chemistry software leaves much to be desired in capabilities and user-friendliness. In this work we aim to harness the power of 3D diffusion experiments through a new free and open source software package, MAGNATE (Multidimensional Analysis for the General NMR Analysis Toolbox Environment). The new software is written in MATLAB® and comes with a user-friendly graphical user interface (GUI), illustrated in Figure 3. MAGNATE complements the popular GNAT (General NMR Analysis Toolbox,56 former DOSYToolbox57). In its present incarnation, MAGNATE is a free standing programme but will also in the future be incorporated into the GNAT. It runs under Matlab (we recommend version 2017a or newer) on any platform, and free-standing compiled versions will be made available for compatible versions of Windows, Mac and Linux. Detailed instructions on practical processing are given in the supporting information, and the Matlab code and test data are available for download at https://nmr.chemistry.manchester.ac.uk/. To demonstrate the new software we present a new 3D DOSY experiment, Oneshot-HSQC (see supporting information). This concatenates the Oneshot,16, 58 and echo/anti-echo HSQC sequences, using adiabatic chirp pulses for 13C inversion and refocusing. It has a short minimum phase cycle, and allows flexible setting of diffusion parameters. As with all HSQC experiments which use broadband heteronuclear decoupling, there will be some sample heating due to the extra radio frequency power during acquisition. This is reflected in the slightly higher diffusion coefficients, due both to the decrease in sample viscosity caused by the temperature

increase and to slight convection,59-60 seen in Figures 2-4 than in the 2D DOSY spectrum of Figure 1. If the experiment is carried out without decoupling, the diffusion coefficients are in good agreement with those of the 2D experiment (see ESI). Experimental The sample of a mixture of B vitamins contained 32 mg of thiamine hydrochloride, 43 mg of pantothenic acid calcium hemicomplex, 15 mg of nicotinic acid, 23 mg of pyridoxine hydrochloride, and 25 mg of biotin in approximately 500 µL of DMSO-d6 and 50 µL of D2O. The flavonoid sample contained 20 mg of quercetin and 30 mg of rutin in 600 µL DMSO-d6. Both samples were prepared with around 8 mM concentration of tetramethylsilane (TMS) as reference signal. Data were acquired on a Bruker Avance Neo 500 spectrometer using a 5 mm diameter direct detection probe (BBO) with a nominal z-gradient of 49.9 G cm-1, at 25 ºC, without spinning. For the flavonoid sample, in the Oneshot-HSQC experiment the diffusion encoding time (Δ) used was 0.12 s, the total diffusion-encoding pulse width (δ) 4 ms, the stabilization delay for gradient pulses 1 ms, and the nominal gradient strength ranged from 3.1 to 25.4 G cm-1 in equal steps of gradient squared, with an imbalance factor α of 0.2. For each of the 8 gradient amplitudes, 32 scans with 2048 complex data points in the direct and 128 points in the indirect dimension were acquired. For the vitamin B sample, the diffusion encoding time (Δ) used was



Figure 3. Main window of the graphical user interface of MAGNATE: The spectrum shows a small region of the least attenuated 2D spectrum of 3D Oneshot-HSQC of a sample of a mixture of B vitamins in DMSO-d6, which demonstrates one of the available display modes, where each peak is labelled (in light blue) with its diffusion coefficients and corresponding statistical error (in black). Signals 8 and 9 are from pyridoxine hydrochloride B6; 10, 11, and 16 are from biotin B7; 12, 14, and 16 are from pantothenic acid B5; and 13 is from thiamine hydrochloride B1.

0.115 s, the total diffusion-encoding pulse width (δ) 4.4 ms, the stabilization delay for the gradient pulses 600 µs, and the nominal gradient strength ranged from 3.18 to 22.24 G cm-1 in equal steps of gradient squared, with an imbalance factor α of 0.2. For each of the 8 gradient amplitudes, 32 scans with 4096 complex data points in the direct and 256 points in the indirect dimension were acquired. Constant-adiabaticity WURST-40 decoupling was applied during FID acquisition in all HSQC experiments. More detailed experimental and processing parameters for the Oneshot-HSQC experiment are given in the supporting information. All the experimental data, and MAGNATE itself, are available for free download from DOI: 10.17632/67t9s63nby.1. Analysing diffusion data The current version of MAGNATE imports 2D frequency domain data that have previously been processed by double Fourier transformation using TopSpin®, the proprietary software used in the majority of current high resolution NMR spectrometers. This lets the user take full advantage of the standard multidimensional processing capabilities of that package, and allows the full range of potential 2D base experiments to be easily

accessed. Support for data from other packages should be straightforward to implement in future versions. The imported data can be efficiently analysed in MAGNATE using both univariate (where each signal is processed individually) and multivariate methods (where whole spectra, or chosen sections thereof, are processed simultaneously). An extended description of the processing capabilities of MAGNATE is given in the ESI. Univariate analysis The most straightforward way of analysing diffusion NMR data is by univariate analysis. This treats each signal independently (e.g. by assuming that it decays according to a single exponential, or a sum of exponentials17) and results in data that can be represented as a 3D DOSY plot (Figure 2a). The analysis is performed by defining peak regions in the 2D plot, integrating to find the peak volumes, and fitting these to an appropriate form of the Stejskal-Tanner equation.11, 15 MAGNATE then constructs a 3D spectrum using the fitted parameters and displays the result in an interactive GUI that facilitates user investigation. Different 2D projections from the 3D plot can be extracted (Figures 2b-c), as well as the 2D spectrum


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Analytical Chemistry obtained by integrating the 3D spectrum between given diffusion coefficients. The latter functionality allows the user to perform a virtual separation of the components of a mixture, based on their diffusion coefficients, and to obtain the 2D sub-spectrum for each component (Figure 4). An alternative display mode is provided, in the main MAGNATE window, in which each peak in the 2D spectrum is labelled with its fitted diffusion coefficient (Figure 2). In cases where cross-peaks overlap, monoexponential fitting procedure will generate an apparent diffusion coefficient intermediate between those of the overlapped signals, just as in 2D DOSY. Where data with very high signal-to-noise ratio are available, multiexponential fitting may be performed, but in most cases the best recourse is multivariate analysis.

Figure 4. 2D slices extracted from the 3D spectrum (Figure 2) of the results of a Oneshot-HSQC experiment of Figure 2 on a mixture of vitamins B, in DMSO-d6. a) 2D spectrum obtained by integrating the region between 1.13 and 1.20 × 10-10 m2 s-1 in the diffusion dimension, where only pantothenic acid B5 correlations are observed. b) Corresponding 2D spectrum for the region between 1.07 and 1.11 × 10-10 m2 s-1 of the diffusion dimension, where only biotin B7 correlations are observed.

Multivariate analysis Multivariate methods use the whole spectrum (or chosen parts thereof) simultaneously, extracting whole (or partial) spectra for individual mixture components. Many different multivariate methods have been applied to 2D diffusion data, including SCORE,61 OUTSCORE,18 DECRA,62-63 and MCR,64 and a variety of others,65-69 but they have not hitherto been applied to 3D diffusion data. The MAGNATE software package extends both the SCORE61 and OUTSCORE18 methods to the analysis of 3D data. In Figure 5 the OUTSCORE method is demonstrated on DOSY-HSQC data for a mixture of flavonoids. To the best of our knowledge, this is the first time any multivariate method has been used to generate 2D sub-spectra of individual mixture components from a 3D diffusion data set. The only user input necessary is the number of components to fit; a successful analysis provides the 2D (here HSQC) spectra for each of the

mixture components, together with their estimated diffusion coefficients. OUTSCORE and SCORE use closely-related fitting algorithms, differing only in their minimisation criterion. Their use is largely complementary. OUTSCORE allows the separation of spectra where the components have as little as 5 % difference in diffusion coefficient, provided that the signal overlap is only moderate. SCORE, on the other hand, can handle more extensive spectral overlap but requires that components differ in diffusion coefficient by 20 % or more. We expect this new tool to be of great help in mixture analysis, speeding up and simplifying the interpretation of multidimensional diffusion datasets.

Figure 5. 2D HSQC spectra, obtained by OUTSCORE analysis of Oneshot-HSQC data, for the components of the mixture of flavonoids in DMSO-d6: (a) quercetin and (b) rutin. The estimated diffusion coefficients are 1.63 and 1.25 ×10-10 m2 s-1 for quercetin and rutin, respectively.

Miscellaneous features In addition to the methods described, MAGNATE contains a variety of additional tools for the analysis of diffusion and related data; it is under continuous development and we expect to keep adding useful features. It is well known that DOSY results are strongly influenced by i) the quality of the input diffusion data and ii) the quality of the processing and analysis methods used. There are many factors that can degrade the quality of experimental data, for example accidental refocussing of unwanted coherences or spectrometer instabilities, and convection, which is an ever-present problem in diffusion experiments.59 There are various ways of reducing the impact of convection during experiments, such as convection compensation,41 using transverse pulse field gradients,70 using very viscous solvents, or even modifying the active volume of the NMR tube.60, 71 A different problem is that if the fitting is not done using the appropriate form of the StejskalTanner equation,14, 17, 63, 67 and therefore the quality of the results can leave much to be desired. Any problem with the experimental data, or with its fitting, will typically be reflected in systematic residuals and increased estimated uncertainties in diffusion


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coefficient (leading to broad signals in the diffusion dimension). It is therefore very useful to have tools that allow users to diagnose such problems. In MAGNATE, questionable data can be conveniently investigated by simultaneously plotting diffusion data (signal decay as function of the gradient strength squared), fitted function and residuals (see supporting information, Figure S9). MAGNATE also offers the option to prune (exclude from analysis) identified outliers from the data to be fitted, allowing more reliable results to be produced. MAGNATE is intended to have applications beyond the processing and analysis of diffusion data, and as open source software it has the potential to be a versatile tool for implementation of new (and existing) methods by any user. In the current version we have incorporated a powerful tool for resolution enhancement: covariance NMR.72-75 Covariance processing uses statistical treatment of NMR data, and sets to transfer the high resolution of one spectral dimension to another dimension of lower resolution, improving the interpretability of the data. An impressive application of this methodology is its use with 2D pure shift NMR experiments to produce high-resolution spectra in which multiplet structure is suppressed in both dimensions.76-80 Some other NMR software packages include covariance tools, but the processing times can be painfully long; in MAGNATE we have found that our implementation is capable of treating large datasets (typically 2k by 2k points) very efficiently in about a few seconds on a standard desktop computer (see supporting information, Figure S12). Covariance processing can be applied to any 2D spectra (including nD arrayed datasets). Another helpful tool is the possibility to measure (and plot) the signal-to-noise ratio of 2D (and 3D) data, again using the user-friendly interface. Specifically for diffusion measurements, we have implemented an independent module for the estimation of the hydrodynamic radius or molecular weight of a solute from its diffusion coefficient, and vice versa, using either the classic Stokes-Einstein equation or the recently-proposed Stokes-Einstein-Gierer-Wirtz estimation (SEGWE) method.81-82 Conclusion The availability of the user-friendly MAGNATE software can, as illustrated here, help end users to exploit the power of 3D diffusion experiments in many applications. There are already many published pulse sequences to choose from, and some already available as part of manufacturers’ pulse sequence packages. We hope that this work will spur the development of even more. MAGNATE is, and will continue to be, free and open source GNU (General Public License) software, and is under continued development. We will make further versions available via our website (https://nmr.chemistry.manchester.ac.uk/).

ASSOCIATED CONTENT Supporting Information The Supporting Information is available free of charge on the ACS Publications website.

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Additional figures as noted in the text, full NMR pulse sequence, experimental parameters, recommended processing protocol, and pulse program code for Bruker spectrometers. (PDF)

AUTHOR INFORMATION Corresponding Author * Emails: [email protected] and [email protected]

Author Contributions All authors have given approval to the final version of the manuscript.

ACKNOWLEDGMENT This work was supported by the Engineering and Physical Sciences Research Council (grant numbers EP/M013820/1, EP/N033949/1 and EP/R018790/1) and by a studentship to GDP from Science Without Borders – Brazil (CNPq reference number 233163/2014-0).

REFERENCES 1. Stilbs, P., "Molecular Self-Diffusion Coefficients in Fourier Transform Nuclear Magnetic Resonance Spectrometric Analysis of Complex Mixtures". Anal. Chem. 1981, 53 (13), 2135-2137. 2. Barjat, H.; Morris, G. A.; Smart, S.; Swanson, A. G.; Williams, S. C. R., "High-Resolution Diffusion-Ordered 2D Spectroscopy (HR-DOSY) - A New Tool for the Analysis of Complex Mixtures". J. Magn. Reson. Series B 1995, 108 (2), 170-172. 3. Luo, R.; Liu, M.; Mao, X., "NMR Diffusion and Relaxation Study on Ibuprofen-HSA Interaction". Appl. Spectrosc. 1999, 53 (7), 776-779. 4. Cohen, Y.; Avram, L.; Frish, L., "Diffusion NMR spectroscopy in supramolecular and combinatorial chemistry: an old parameter--new insights". Angew. Chem. Int. Ed. 2005, 44 (4), 520-554. 5. Trefi, S.; Gilard, V.; Malet-Martino, M.; Martino, R., "Generic ciprofloxacin tablets contain the stated amount of drug and different impurity profiles: A 19F, 1H and DOSY NMR analysis". J. Pharm. Biomed. Anal. 2007, 44 (3), 743-754. 6. Trefi, S.; Gilard, V.; Balayssac, S.; Malet-Martino, M.; Martino, R., "Quality assessment of fluoxetine and fluvoxamine pharmaceutical formulations purchased in different countries or via the Internet by 19F and 2D DOSY 1H NMR". J. Pharm. Biomed. Anal. 2008, 46 (4), 707-722. 7. Balayssac, S.; Trefi, S.; Gilard, V.; Malet-Martino, M.; Martino, R.; Delsuc, M. A., "2D and 3D DOSY 1H NMR, a useful tool for analysis of complex mixtures: application to herbal drugs or dietary supplements for erectile dysfunction". J. Pharm. Biomed. Anal. 2009, 50 (4), 602-612. 8. Howe, P. W., "Selective diffusion spectroscopy using excitation sculpting". Magn. Reson. Chem. 2017, 55 (5), 433437. 9. Pregosin, P. S., "Applications of NMR diffusion methods with emphasis on ion pairing in inorganic chemistry: a mini-review". Magn. Reson. Chem. 2017, 55 (5), 405-413. 10. Pelta, M. D.; Barjat, H.; Morris, G. A.; Davis, A. L.; Hammond, S. J., "Pulse sequences for high-resolution diffusion-ordered spectroscopy (HR-DOSY)". Magn. Reson. Chem. 1998, 36 (10), 706-714. 11. Stejskal, E. O.; Tanner, J. E., "Spin Diffusion Measurements: Spin Echoes in the Presence of a Time-


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Page 7 of 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60

Analytical Chemistry Dependent Field Gradient". J. Chem. Phys. 1965, 42 (1), 288292. 12. Morris, K. F.; Johnson Jr, C. S., "Resolution of Discrete and Continuous Molecular Size Distributions by Means of Diffusion-Ordered 2D NMR Spectroscopy". J. Am. Chem. Soc. 1993, 115 (10), 4291-4299. 13. Morris, G. A., "Diffusion-Ordered Spectroscopy". In eMagRes, 2009. 14. Connell, M. A.; Bowyer, P. J.; Adam Bone, P.; Davis, A. L.; Swanson, A. G.; Nilsson, M.; Morris, G. A., "Improving the accuracy of pulsed field gradient NMR diffusion experiments: Correction for gradient non-uniformity". J. Magn. Reson. 2009, 198 (1), 121-131. 15. Sinnaeve, D., "The Stejskal-Tanner equation generalized for any gradient shape-an overview of most pulse sequences measuring free diffusion". Concepts Magn. Reson. Part A 2012, 40A (2), 39-65. 16. Pelta, M. D.; Morris, G. A.; Stchedroff, M. J.; Hammond, S. J., "A one-shot sequence for high-resolution diffusion-ordered spectroscopy". Magn. Reson. Chem. 2002, 40 (13), 147-152. 17. Nilsson, M.; Connell, M. A.; Davis, A. L.; Morris, G. A., "Biexponential Fitting of Diffusion-Ordered NMR Data: Practicalities and Limitations". Anal. Chem. 2006, 78 (9), 30403045. 18. Colbourne, A. A.; Meier, S.; Morris, G. A.; Nilsson, M., "Unmixing the NMR spectra of similar species - vive la difference". Chem. Commun. 2013, 49 (89), 10510-10512. 19. Dal Poggetto, G.; Favaro, D. C.; Nilsson, M.; Morris, G. A.; Tormena, C. F., "19F DOSY NMR analysis for spin systems with nJFF couplings". Magn. Reson. Chem. 2014, 52 (4), 172-177. 20. Power, J. E.; Foroozandeh, M.; Moutzouri, P.; Adams, R. W.; Nilsson, M.; Coombes, S. R.; Phillips, A. R.; Morris, G. A., "Very broadband diffusion-ordered NMR spectroscopy: 19F DOSY". Chem. Commun. 2016, 52 (42), 6892-6894. 21. Xu, C.; Wan, Y.; Chen, D.; Gao, C.; Yin, H.; Fetherston, D.; Kupce, E.; Lopez, G.; Ameduri, B.; Twum, E. B.; Wyzgoski, F. J.; Li, X.; McCord, E. F.; Rinaldi, P. L., "19F DOSY diffusion-NMR spectroscopy of fluoropolymers". Magn. Reson. Chem. 2017, 55 (5), 472-484. 22. Kagan, G.; Li, W.; Hopson, R.; Williard, P. G., "Internally Referenced Diffusion Coefficient-Formula Weight (DFW) Analysis of 31P Diffusion-Ordered NMR Spectroscopy (DOSY)". Org. Lett. 2009, 11 (21), 4818-4821. 23. Shestakova, P.; Absillis, G.; Martin-Martinez, F. J.; De Proft, F.; Willem, R.; Parac-Vogt, T. N., "Integrating 31P DOSY NMR spectroscopy and molecular mechanics as a powerful tool for unraveling the chemical structures of polyoxomolybdate-based amphiphilic nanohybrids in aqueous solution". Chem. Eur. J. 2014, 20 (18), 5258-5270. 24. Wu, D.; Chen, A.; Johnson Jr, C. S., "HeteronuclearDetected Diffusion-Ordered NMR Spectroscopy through Coherence Transfer". J. Magn. Reson. Series A 1996, 123 (2), 215-218. 25. Botana, A.; Howe, P. W.; Caer, V.; Morris, G. A.; Nilsson, M., "High resolution 13C DOSY: the DEPTSE experiment". J. Magn. Reson. 2011, 211 (1), 25-29. 26. Adams, R. W., "Pure Shift NMR Spectroscopy". eMagRes 2014, 295-310. 1H 27. Castañar, L.; Parella, T., "Broadband homodecoupled NMR experiments: recent developments, methods and applications". Magn. Reson. Chem. 2015, 53 (6), 399-426. 28. Zangger, K., "Pure shift NMR". Prog. Nucl. Magn. Res. Sp. 2015, 86-87, 1-20. 29. Castañar, L., "Pure shift 1H NMR: what is next?". Magn. Reson. Chem. 2017, 55 (1), 47-53. 30. Nilsson, M.; Morris, G. A., "Pure shift proton DOSY: diffusion-ordered 1H spectra without multiplet structure". Chem. Commun. 2007, (9), 933-935.

31. Aguilar, J. A.; Faulkner, S.; Nilsson, M.; Morris, G. A., "Pure shift 1H NMR: a resolution of the resolution problem?". Angew. Chem. Int. Ed. 2010, 49 (23), 3901-3903. 32. Glanzer, S.; Zangger, K., "Directly decoupled diffusion-ordered NMR spectroscopy for the analysis of compound mixtures". Chem. Eur. J. 2014, 20 (35), 1117111175. 33. Foroozandeh, M.; Castañar, L.; Martins, L. G.; Sinnaeve, D.; Dal Poggetto, G.; Tormena, C. F.; Adams, R. W.; Morris, G. A.; Nilsson, M., "Ultrahigh-Resolution DiffusionOrdered Spectroscopy". Angew. Chem. Int. Ed. 2016, 55 (50), 15579-15582. 34. Wu, D.; Chen, A.; Johnson Jr, C. S., "ThreeDimensional Diffusion-Ordered NMR Spectroscopy: The Homonuclear COSY–DOSY Experiment". J. Magn. Reson. Series A 1996, 121 (1), 88-91. 35. Jerschow, A.; Muller, N., "3D Diffusion-Ordered TOCSY for Slowly Diffusing Molecules". J. Magn. Reson. Series A 1996, 123 (2), 222-225. 36. Gozansky, E. K.; Gorenstein, D. G., "DOSY-NOESY: Diffusion-Ordered NOESY". J. Magn. Reson. Series B 1996, 111 (1), 94-96. 37. Birlirakis, N.; Guittet, E., "A New Approach in the Use of Gradients for Size-Resolved 2D-NMR Experiments". J. Am. Chem. Soc. 1996, 118 (51), 13083-13084. 38. Barjat, H.; Morris, G. A.; Swansom, A. G., "A ThreeDimensional DOSY–HMQC Experiment for the High-Resolution Analysis of Complex Mixtures". J. Magn. Reson. 1998, 131 (1), 131-138. 39. Lucas, L. H.; Otto, W. H.; Larive, C. K., "The 2D-JDOSY Experiment: Resolving Diffusion Coefficients in Mixtures". J. Magn. Reson. 2002, 156 (1), 138-145. 40. Stchedroff, M. J.; Kenwright, A. M.; Morris, G. A.; Nilsson, M.; Harris, R. K., "2D and 3D DOSY methods for studying mixtures of oligomeric dimethylsiloxanes". Phys. Chem. Chem. Phys. 2004, 6 (13), 3221-3227. 41. Nilsson, M.; Morris, G. A., "Improving pulse sequences for 3D DOSY: convection compensation". J. Magn. Reson. 2005, 177 (2), 203-211. 42. Nilsson, M.; Gil, A. M.; Delgadillo, I.; Morris, G. A., "Improving pulse sequences for 3D DOSY: COSY-IDOSY". Chem. Commun. 2005, (13), 1737-1739. 43. Vitorge, B.; Jeanneat, D., "NMR Diffusion Measurements in Complex Mixtures Using Constant-TimeHSQC-IDOSY and Computer-Optimized Spectral Aliasing for High Resolution in the Carbon Dimension". Anal. Chem. 2006, 78 (15), 5601-5606. 44. Brand, T.; Cabrita, E. J.; Morris, G. A.; Gunther, R.; Hofmann, H. J.; Berger, S., "Residue-specific NH exchange rates studied by NMR diffusion experiments". J. Magn. Reson. 2007, 187 (1), 97-104. 45. McLachlan, A. S.; Richards, J. J.; Bilia, A. R.; Morris, G. A., "Constant time gradient HSQC-iDOSY: practical aspects". Magn. Reson. Chem. 2009, 47 (12), 1081-1085. 46. Didenko, T.; Boelens, R.; Rudiger, S. G., "3D DOSYTROSY to determine the translational diffusion coefficient of large protein complexes". Protein Eng. Des. Sel. 2011, 24 (12), 99-103. 47. Augustyniak, R.; Ferrage, F.; Paquin, R.; Lequin, O.; Bodenhausen, G., "Methods to determine slow diffusion coefficients of biomolecules. Applications to Engrailed 2, a partially disordered protein". J. Biomol NMR 2011, 50 (3), 209218. 48. Nilsson, M.; Gil, A. M.; Delgadillo, I.; Morris, G. A., "Improving Pulse Sequences for 3D Diffusion-Ordered NMR Spectroscopy: 2DJ-IDOSY". Anal. Chem. 2004, 76 (18), 54185422. 49. Jaravine, V.; Ibraghimov, I.; Orekhov, V. Y., "Removal of a time barrier for high-resolution multidimensional NMR spectroscopy". Nat. Meth. 2006, 3 (8), 605-607. 50. Urbanczyk, M.; Kozminski, W.; Kazimierczuk, K., "Accelerating diffusion-ordered NMR spectroscopy by joint


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sparse sampling of diffusion and time dimensions". Angew. Chem. Int. Ed. 2014, 53 (25), 6464-6467. 51. Hassanieh, H.; Mayzel, M.; Shi, L.; Katabi, D.; Orekhov, V. Y., "Fast multi-dimensional NMR acquisition and processing using the sparse FFT". J . Biomol. NMR 2015, 63 (1), 9-19. 52. Qu, X.; Mayzel, M.; Cai, J. F.; Chen, Z.; Orekhov, V., "Accelerated NMR spectroscopy with low-rank reconstruction". Angew. Chem. Int. Ed. 2015, 54 (3), 852-854. 53. Kazimierczuk, K.; Orekhov, V., "Non-uniform sampling: post-Fourier era of NMR data collection and processing". Magn. Reson. Chem. 2015, 53 (11), 921-926. 54. Hansen, A. L.; Bruschweiler, R., "Absolute Minimal Sampling in High-Dimensional NMR Spectroscopy". Angew. Chem. Int. Ed. 2016, 55 (45), 14169-14172. 55. Aguilar, J. A.; Belda, R.; Botana, A.; Kenwright, A. M., "HD-2D: routine high-dispersion two-dimensional NMR spectra at no extra cost". RSC Adv. 2016, 6 (86), 83380-83385. 56. Castañar, L.; Dal Poggetto, G.; Colbourne, A. A.; Morris, G. A.; Nilsson, M., "The GNAT: A new tool for processing NMR data". Magn. Reson. Chem. 2018, 56 (6), 546–558. 57. Nilsson, M., "The DOSY Toolbox: a new tool for processing PFG NMR diffusion data". J. Magn. Reson. 2009, 200 (2), 296-302. 58. Botana, A.; Aguilar, J. A.; Nilsson, M.; Morris, G. A., "J-modulation effects in DOSY experiments and their suppression: the Oneshot45 experiment". J. Magn. Reson. 2011, 208 (2), 270-278. 59. Swan, I.; Reid, M.; Howe, P. W.; Connell, M. A.; Nilsson, M.; Moore, M. A.; Morris, G. A., "Sample convection in liquid-state NMR: why it is always with us, and what we can do about it". J. Magn. Reson. 2015, 252, 120-129. 60. Barbosa, T. M.; Rittner, R.; Tormena, C. F.; Morris, G. A.; Nilsson, M., "Convection in liquid-state NMR: expect the unexpected". RSC Adv. 2016, 6 (97), 95173-95176. 61. Nilsson, M.; Morris, G. A., "Speedy Component Resolution An Improved Tool". Anal. Chem. 2008, 80 (10), 3777-3782. 62. Windig, W.; Antalek, B., "Direct exponential curve resolution algorithm (DECRA) - A novel application of the generalized rank annihilation method for a single spectral mixture data set with exponentially decaying contribution profiles". Chem. Int. Lab. Sys. 1997, 37 (2), 241-254. 63. Nilsson, M.; Morris, G. A., "Improved DECRA processing of DOSY data: correcting for non-uniform field gradients". Magn. Reson. Chem. 2007, 45 (8), 656-660. 64. Karakach, T. K.; Knight, R.; Lenz, E. M.; Viant, M. R.; Walter, J. A., "Analysis of time course 1H NMR metabolomics data by multivariate curve resolution". Magn. Reson. Chem. 2009, 47 (2), 105-117. 65. Toumi, I.; Torresani, B.; Caldarelli, S., "Effective processing of pulse field gradient NMR of mixtures by blind source separation". Anal. Chem. 2013, 85 (23), 11344-11351. 66. Zhong, J.; DiDonato, N.; Hatcher, P. G., "Independent component analysis applied to diffusion-ordered spectroscopy: separating nuclear magnetic resonance spectra of analytes in mixtures". J. Chemom. 2012, 26 (5), 150-157.

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67. Nilsson, M.; Morris, G. A., "Correction of systematic errors in CORE processing of DOSY data". Magn. Reson. Chem. 2006, 44 (7), 655-660. 68. Van Gorkom, L. C. M.; Hancewicz, T. M., "Analysis of DOSY and GPC-NMR Experiments on Polymers by Multivariate Curve Resolution". J. Magn. Reson. 1998, 130 (1), 125-130. 69. Nuzillard, D.; Bourg, S.; Nuzillard, J. M., "Model-Free Analysis of Mixtures by NMR Using Blind Source Separation". J. Magn. Reson. 1998, 133 (2), 358-363. 70. Kiraly, P.; Swan, I.; Nilsson, M.; Morris, G. A., "Improving accuracy in DOSY and diffusion measurements using triaxial field gradients". J. Magn. Reson. 2016, 270, 2430. 71. Iwashita, T.; Konuma, T.; Harada, E.; Mori, S.; Sugase, K., "Use of glass capillaries to suppress thermal convection in NMR tubes in diffusion measurements". Magn. Reson. Chem. 2016, 54 (9), 729–733. 72. Zhang, F.; Bruschweiler, R., "Indirect Covariance NMR Spectroscopy". J. Am. Chem. Soc. 2004, 126 (41), 13180-13181. 73. Bruschweiler, R.; Zhang, F., "Covariance nuclear magnetic resonance spectroscopy". J. Chem. Phys. 2004, 120 (11), 5253-5260. 74. Bruschweiler, R., "Theory of covariance nuclear magnetic resonance spectroscopy". J. Chem. Phys. 2004, 121 (1), 409-14. 75. Snyder, D. A.; Bruschweiler, R., "Multidimensional covariance spectroscopy by Covariance NMR". eMagRes 2009. 76. Morris, G. A.; Aguilar, J. A.; Evans, R.; Haiber, S.; Nilsson, M., "True Chemical Shift Correlation Maps: A TOCSY Experiment with Pure Shifts in Both Dimensions". J. Am. Chem. Soc. 2010, 132 (37), 12770-12772. 77. Aguilar, J. A.; Colbourne, A. A.; Cassani, J.; Nilsson, M.; Morris, G. A., "Decoupling two-dimensional NMR spectroscopy in both dimensions: pure shift NOESY and COSY". Angew. Chem. Int. Ed. 2012, 51 (26), 6460-6463. 78. Foroozandeh, M.; Adams, R. W.; Nilsson, M.; Morris, G. A., "Ultrahigh-Resolution Total Correlation NMR spectroscopy". J. Am. Chem. Soc. 2014, 136 (34), 1186711869. 79. Fredi, A.; Nolis, P.; Cobas, C.; Martin, G. E.; Parella, T., "Exploring the use of Generalized Indirect Covariance to reconstruct pure shift NMR spectra: Current Pros and Cons". J. Magn. Reson. 2016, 266, 16-22. 80. Fredi, A.; Nolis, P.; Cobas, C.; Parella, T., "Access to experimentally infeasible spectra by pure-shift NMR covariance". J. Magn. Reson. 2016, 270, 161-168. 81. Evans, R.; Deng, Z.; Rogerson, A. K.; McLachlan, A. S.; Richards, J. J.; Nilsson, M.; Morris, G. A., "Quantitative interpretation of diffusion-ordered NMR spectra: can we rationalize small molecule diffusion coefficients?". Angew. Chem. Int. Ed. 2013, 52 (11), 3199-3202. 82. Evans, R.; Dal Poggetto, G.; Nilsson, M.; Morris, G. A., "Improving the Interpretation of Small Molecule Diffusion Coefficients". Anal. Chem. 2018, 90 (6), 3987-3994.

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Analytical Chemistry


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An unexploited dimension: new software for mixture analysis by 3D

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