To illustrate this discussion, figure 4.4, shows different DOSY displays obtained from the same experimental data set, using either intensity of integrals. The sample is made of quinine, geraniol and camphene, and the resulting spectrum is quite complex. For the purpose of this discussion, it can helpfully be split into two parts, from 4 to 9 ppm where there is barely any overlap, and from 0 to 3 ppm where there is a high degree of overlap. A situation often found in NMR with organic molecules, as aliphatic part often overlaps while more functionalized part does less so.

<figure><img src="image_58.png" /><figcaption>Figure 4.4, DOSY spectra of the mixture camphene, geraniol, quinine at 0.1M in MeOH. The attribution is done for the peaks that can be easily attributed. The method to analyze the mixture is either integration or intensity. The circled peak is for methanol.</figcaption></figure>

In the 4–9 ppm region, using integrals gives good results, with well aligned peaks for the three compounds. One major mistake can be seen, for the peak of the solvent, methanol, that would be expected to be close to the peak of TMS. Here integration is clearly at fault, because of the overlap between the solvent signal and a peak of geraniol. The result is a peak in-between methanol and quinine in the diffusion dimension. Using finer integration area could give better result but the peaks never really separate from each other.

In that same region, using intensities gives clear separation between methanol and the rest but the results are not as good as with integrals when overlapping is weak. One can see that multiplets give multiple signals on the DOSY display that are slightly misaligned. There are even stronger glitches, such as the peak at 7.4 ppm and its estimated diffusion coefficient of 4.4× 10⁻¹⁰ m²/s.
For the 0–3 ppm region, the differences between the two methods are subtler. The intensity method seems to give lots of peaks. The quinine is almost perfect, the geraniol is readable but the camphene gets complicated with some cross talk with the other molecules. It is also interesting that even if the entanglement is not that strong, the spread simply coming from the multiplets make the display hard to read. The separation gives two molecules clearly but as for the camphene, it is not clear if it displays multiple species, a few bad instances of geraniol or a single species.

The integration method gives much clearer results, but they are in fact not better, as more peaks are visibly overlapping and give a value for the diffusion coefficient that is in-between two species. The uncertainty is also large for some regions, especially those that correspond to overlapping peaks. If the user is aware of the limitation of the thresholding, it gives slightly better results, as three lines can be guessed while integration gives strong peaks in-between that one can doubt if they are true or artefact. The integration is very difficult in this case as the spectra is full of really small peak that are impossible to avoid when integrating bigger ones.

Overall, one method is not better than the other and it is about specific cases and time of analysis. We used the integration method during this work because our samples were not too hard to resolve nor too complex, and because user time was available. It also highlights that it is hard to anticipate what will work better. As there is no clear ruling that come out of this discussion rather a set of observations.

## 4.4. Quantitative comparison

In this part we are going to go deeper in the comparison of different DOSY spectra. We will now compare elements of the pulse sequence that may or may not modify the experimental result. We also are going to investigate some optimization methods and check at whether or not they are relevant.

### 4.4.1. Metrics

It is first helpful to define a few specific words. "Trueness" refers to how close a measurement's value is to the true value, when such true value is conceivable. Precision, on the other hand, is a measure of how repeatable a measurement is. It indicates to which extent the same value is obtained or not if the measurement is repeated multiple times, irrespective of the trueness of that value. Accuracy is the combination of precision and trueness

It is important to note that, for diffusion NMR experiments, it is very difficult to evaluate the trueness of a result on an absolute scale, as there are many sources of systematic errors, that are difficult to evaluate. This is in most cases not a limitation, as most applications of diffusion NMR rely on the relative values of several diffusion coefficients, be it to separate the spectra of different compounds, or to estimate molecular sizes and shapes. What is then important is the trueness of
relative values of diffusion coefficients: are all the peaks for a given compound well aligned? Is the difference in diffusion coefficients for two compounds consistent?

Instead of the trueness of the estimated diffusion coefficients, we chose to use a metric that reflects how well the peaks for a given compound are aligned, in a given DOSY experiments. This "alignment" was calculated, for a given molecule, as the standard deviation for the set of values obtained from the different peaks of the molecule. In the following, the alignment will be expressed either in m²·s⁻¹, or in percent (in which case it is divided by the average diffusion coefficient for the set). In order to summarize the results with a single number for a given experiments, the root mean square of the alignments for each of the molecules can also be calculated.

Concerning the precision of the estimated diffusion coefficients, in the case of DOSY experiments, it can in principle be estimated from the fit, using the residuals and the Jacobian that are output of the least-squares procedure. In practice, sources of systematic errors also contribute to the uncertainty as estimated in this way. For example, in the case of significant spatial non-uniformity of the gradient field, the Stejskal Tanner equation is not expected to describe the data perfectly.¹⁷⁰,¹⁷¹ This is illustrated in Table 1, where the uncertainty estimated from the fit is compared to the precision measured by repeating the same experiment three times, for selected peaks in three different pulse sequences. One can see that the uncertainty estimated from the fit is always larger than the actual repeatability. Here we chose to use this estimated uncertainty obtained from the fit, rather than the actual repeatability, as it corresponds to the number used for the peak width in the DOSY display. It is also a useful indicator, as we found out that experiment with large uncertainty and overall bad result, can still have good repeatability. It therefore makes more sense to use the uncertainty.

<table><caption>Table 4.1: value of repeatability/precision and uncertainty in percentage of coefficient diffusion. Values for precision are acquired by standard deviation of diffusion coefficient for three identical experiments. Values for uncertainty are acquired by summing with RMS each value of uncertainty for the three identical experiments. The STE/DSTE Bruker is the by default sequence while DSTE monitoring is the DSTE used for the monitoring in later part.</caption><thead><tr><th rowspan="2"></th><th colspan="3">STE bruker</th><th colspan="3">DSTE bruker</th><th colspan="3">DSTE monitoring</th></tr><tr><th>Pyr</th><th>Suc.</th><th>But.</th><th>Pyr</th><th>Suc.</th><th>But.</th><th>Pyr</th><th>Suc.</th><th>But.</th></tr></thead><tbody><tr><td>% precision</td><td>0.40</td><td>0.16</td><td>0.38</td><td>1.77</td><td>0.34</td><td>0.32</td><td>0.72</td><td>0.32</td><td>1.16</td></tr><tr><td>% uncertainty</td><td>1.56</td><td>1.32</td><td>2.14</td><td>1.16</td><td>1.06</td><td>1.30</td><td>1.61</td><td>1.05</td><td>1.70</td></tr></tbody></table>

In order to provide a compact representation that helps to compare sequences, a single uncertainty value was calculated for each molecule, by calculating the root mean square of the uncertainties obtained for the different peaks of that molecule. An even more compact information was obtained by calculating a single uncertainty value for a complete display, as the root mean square of the uncertainties for all the analyzed peaks.
In summary, two quantitative metrics were used to assess the quality of the DOSY results at the level of a given compound, or a mixture of compound. They are the alignment and the uncertainty. These quantities are also referred to as “separation quality” and “fit quality” in our publication.

We are going to illustrate these concepts with an example, shown in figure 4.5. To do so we have created artificial displays, meaning they do not represent real sample and are born from a graphics software rather than data analysis. Consider three fictive experiments giving three sets of two peaks, for which the true value of the diffusion coefficient is not known. The only way to assess if the measured values are satisfactory is through the alignment of the two measured values. This is the case c) in figure 4.5. It represents the real situation of mixture analysis using DOSY and the challenge one can encounter.

<figure><img src="image_59.png" /><figcaption>Figure 4.5, Three cases of DOSY spectra. The spectra here are fake and do not represent any mixture or chemical species. Case a) display a wrong coefficient value with no increase of uncertainty/error. The case b) show the right value with a large uncertainty. The case c) is a representation of the real case where a standard value is indeed improbable.</figcaption></figure>

If we simplify the experiment by having a single species with single peak and a standard value that we know is true. In practice the case b) "poor uncertainty good trueness" is often found and can result from multiple problems, the most common being a bad optimization of the gradient strength, resulting in too slow or too fast signal attenuation. It can give some hints but 2D-peaks of the DOSY may overlap with others not part of the same molecule. It can also come from problem related to the NMR apparatus. Terrible gradient spatial homogeneity, motion within the sample, lack of temperature regulation... It often come from severe causes and while making the experiment less worthy, it is usually easy to diagnose.

The case a) "good uncertainty poor trueness" is more concerning and rarer. It often results from a problem within the experiment such as unwanted J-modulation, disturbance from the baseline, shimming qualities or problem with the fitting model. Those problems are usually subtler than case b).
To give a few examples that happened directly to us, a bad calculation of the delay for the fit model
can give such kind of wrong value seemingly with good quality. It happened several times that we use
wrong gradient list that is proportional to the real one, this give wrong D value but keep the error and
alignment that would be the right ones. Distortion and glitches of the baseline can also cause this effect
of having little to no effect on the error but change the value of the diffusion recorded. We are not
sure why and this seems to be peak dependent so it will impair the alignment resulting in cases like the
green one in case c).

This is for the causes unrelated to the chemistry of the system. Indeed, such modification of the
measured values can come from non-user controllable causes. As said previously this change of the
measured value could come from overlapping. It is reported<sup>164,172</sup> that upon overlapping the diffusion
coefficient while be a midway between the overlapped species. Another case slightly related to
overlapping is exchange. A peak in chemical exchange will also give erroneous diffusion coefficient
value. In the same fashion as overlapping, the diffusion coefficient will be a midway between the
exchange species. It could be useful as this value will be proportional to the ratio of the in-exchange
species.<sup>173</sup> Overall this case a) is the most concerning because without a good *a priori* knowledge of
the involved species/system it can be very misleading. Fortunately, it will also be rare as different
effects that can create a wrong value of diffusion coefficient are likely to have an impact on the fit. The
fitted model will be wrong and thus it is expected that the error increases. Nonetheless we have seen
in this work case where the change of value is very large compared to a relatively benign increase of
the error. This is particularly true on flow and will be discussed in the part 6.

4.4.2. Graphical representation

In order to compare the relative merits of different DOSY experiments, we propose to use a graphical
2D representation, in which each experiment appears as a single marker. The x coordinate is given by
the uncertainty, and the y coordinate is given by the alignment, calculated over the whole set of
selected peaks. The best experiment will thus have a marker close to the origin of the plot and as
experiments worsen, they will move further away. This system is made for comparison of closely
related experiment using the exact same sample, and the same kind of analysis. Here the sample of
sucrose, butanol and pyridine was used with the integral's method for analysis. A selection of three
spectra and the associated graphic is shown in Figure 4.6. The very bad DOSY was acquired with no
coherence selection whatsoever, the bad spectra had an unsuited attenuation, the good was simply
acquired in good condition. Each colored dot corresponds to a DOSY shown above. One can see that
the "good" is close to origin as expected. The "bad" one is clearly unsatisfactory but could be exploited.
The "very bad" one is chaotic and no information could really be extracted from it. In general, we
consider that anything that has 0.2 or more in any of its coordinate is failed, note that as previously
said it depend on your system and goals.

<figure><img src="image_60.png" /><figcaption>Figure 4.6, each correlation circle is representative of a single DOSY spectra matching its color. Both axis unit is that of diffusion coefficient (× 10⁻¹⁰ m²/s). Label are indicative related to the text.</figcaption></figure>

4.5. Comparing DOSY pulse sequences

The quantitative metrics introduced above will be used in the rest of this manuscript to assess new developments. Before that, in this subsection we will illustrate their use to answer some of the questions that we had to address when searching for the “starting point” of our DOSY developments. Specifically, we will describe two examples, the choice to include an LED block or not, and the choice of the amplitude of the spoiler gradient.

Many pulse sequence elements have been introduced over the years for DOSY experiments, and not all of them are still relevant today. For example, the LED block³⁸,³⁹ (see part I), while being relevant in the 90’s, was said to be unnecessary thanks to the progress made in shielding of electronics and in general in FT-NMR.
To test this, we ran identical experiment with or without the LED. We tested it on both STE and DSTE pulse sequence as they use different numbers of gradient pulses. We also tested on different probes to see if our comparison made sense and converged toward same values.

The results of the w/wo LED comparison are displayed in figure 4.7. The difference observed is not very large but seems noteworthy. Eddy current are foretold as strong disruptor of the analysis<sup>20</sup> here nothing so obvious seems to happen. Indeed, in an unexpected turn of event the LED seems to worsen the analysis. Our main hypothesis is that introducing one more delay within the sequence worsen it; due to evolution or possible problem in the refocusing (imperfect spoiler, pulse calibration...). There is a difference between STE and DSTE; DSTE having better uncertainty but lower alignment and *vice versa*. This is something we have seen all throughout this work, it is possible that it is caused by the abundance of delay in DSTE that may cause unwanted evolution of the magnetization and J-modulation, sometimes visible in the spectra, lightly disturbing the integration process. On the other part convection is almost unavoidable<sup>41</sup> and even in water this could explain the better fit and improved uncertainty. This slight difference of alignment is deemed a small price to pay for the compensation of convection that will be a major issue in all organic solvent with low viscosity.

<figure><img src="image_61.png" /><figcaption>Figure 4.7. each correlation circle/cross is representative of a single DOSY spectra matching its color. Cross indicate STE ring DSTE, blue with led orange without. Both axis unit is that of diffusion coefficient (× 10<sup>-10</sup>m<sup>2</sup>/s). Label are indicative related to the text.</figcaption></figure>

Another aspect that we tested is the strength of the spoiler gradient. Without spoiler gradient the resulting spectra is barely readable and can be called non-working DOSY. Adding even a small value of spoiler seems to solve this but it is interesting to test it, as gradient and their behavior play a huge
role in DOSY. We tested the same sequence (without LED) with varying gradient power and the result is shown in figure 4.8. The test is also done on both DSTE and STE. The spoiler to be efficient should dephase the magnetization and never rephrases it, so they should have 2 different values for DSTE, in all cases it should never be the same as diffusion encoding or any other gradient value. The value in the figure 4.8 are just the order of magnitude of the gradient power (100% ≈ 60 G/cm). The result shows a lack of difference in gradient strength. It did not seem to play any meaningful role the difference observed are close to ~1%. We thus decided to use the value advised by the constructor around 10%(orange).

<figure><img src="image_62.png" /><figcaption>Figure 4.8. each correlation dot is representative of a single DOSY spectra matching its color. Cross indicate STE ring DSTE, gradient order of magnitude for spoiler are indicated in the legend. Both axis unit is that of diffusion coefficient (× 10⁻¹⁰ m²/s). Label are indicative related to the text.</figcaption></figure>
# 5. Experimental part

In this part we will detail the experimental procedures for the rest of the thesis.

All experiments were done on a Bruker Avance III spectrometer, operating at a ¹H Larmor frequency of 500 Mhz, using a broadband inverse-detection triple-axis gradient probe (BBI) with a deuterium channel for locking. The NMR experiments are carried on a 5mm diameter NMR sample tube standard or with capillaries inside (flowtube).

The gradients were calibrated using a standard tube of doped water made by the vendor. "Doped water" by Bruker composition is 1% H₂O in D₂O doped with 0.1 mg/mL of GdCl₃ and containing 0.1% ¹³CH₃OH. Calibration of the gradients gave a value of 63.0 G/cm for the Z axis, 47.0 G/cm for the X axis, 48.2 G/cm for the Y axis. The gradient power is expressed as a percentage of this value.

The temperature was set at a nominal value of 298 K with an airflow 535 L/h and calibrated thanks to a standard methanol sample. No sample rotation was ever used.

All gradients for diffusion encoding are of sine shape (shape named SINE.100).

## 5.1. -Section 6 methods

### 5.1.1. Oneshot methodology

For this section the sample used was the same for all the experiments and was a solution in D₂O with a concentration at 0.1 M of three compounds: sucrose from edible sources, pyridine, and 2-butanol. The tube was standard NMR tube of 5 mm outer diameter filled with 600 µL of solution. The tube was closed with parafilm, and stored at room temperature.

#### 5.1.1.1. Coherence selection using parallel gradients

The Oneshot parallel gradient sequence is described at ref XX and is named "Doneshot". It was used as it is, except for the version with no phase cycling. For this version the only modification is the removing of the phase cycling within the sequence program.

The diffusion time $\Delta$ was 160 ms for all the DOSY experiments and $\delta$ was 2.8 ms. Each experiment was started by 4 dummy scans and had a 3 s inter scan delay. The number of points is 16384 by spectra with a spectral width (SW) of 12 ppm leading to a total of 1.36 s of acquisition.

Each DOSY data set was acquired with 16 gradient increments, using a linear ramp of gradient power ranging from 10% to 70%, along the Z axis. For 1-scan experiments the total duration of the experiment was 90 s and for the 8 scans version 10 ~min. When not specified the $\alpha$ factor for coherence selection was 13%.

#### 5.1.1.2. Coherence selection using orthogonal gradients

The oneshot orthogonal coherence selection gradient sequence was custom-written and is available in the appendix under the name "AMstebposgcGrad_v2".
The diffusion time $\Delta$ was 160 ms for all the DOSY experiments and $\delta$ was 2.8 ms. Each experiment was started by 4 dummy scans and had a 3 s inter scan delay. The number of points was 16384 by spectra with a spectral width (SW) of 12 ppm leading to a total of 1.36 s of acquisition.

Each DOSY data set was acquired with 16 gradient increments, using a linear ramp of gradient power ranging from 10% to 80%, along the Z axis. For 1 scan experiment the total duration of the experiment was 90 s and for the 8 scans 10 min. Coherence selection gradients (crushers) were on the Y axis and spoilers were on the Z axis. There are two versions with and without phase cycling; phase cycling is noted “_phycy”.

#### 5.1.1.3. Comparison between the two

The condition for DSTE used in 5.1.1.2 and 5.1.1.1 were re-used. $\Delta$ was 160 ms, $\delta$ was 2.8 ms, the number of points is 16384 by spectra with a spectral width (SW) of 12 ppm leading to a total of 1.36 s of acquisition. The gradient ramp was 10% to 70% linear for all compared experiment.

### 5.2. Section 7 methods

For flow experiments the sample was made of a 25 mL solution in H₂O with three compounds concentrated at 0.1 M. The compounds were: sucrose from edible source, pyridine and 2-butanol. The reference data was acquired by using 600 µl of this solution in an NMR tube out-of-flow.

#### 5.2.1.1. Methods for Diffusion-encoding with longitudinal gradients

For all experiments compared here the NMR setup was with flow on. The lock was off during the experiments. The NMR experiments were regular DSTE with bipolar pulses and 16 scans per increment and 16 increments with full phase cycling. The sequence used was “AMdsteosgcGradXPhycy” with no crusher enabled and three spoiler gradients.

The diffusion time $\Delta$ was 160 ms for all the DOSY experiments and $\delta$ was 2.4 ms. Each experiment was started by 4 dummy scans and had a 3 s inter scan delay. The number of points was 16384 by spectra with a SW of 12 ppm leading to a total of 1.36 s of acquisition time. The gradient power for diffusion encoding is 10% to 80% linear ramp in the Z axis. The WET solvent suppression pulses powers were optimized each day as necessary and gradient pulse length was 2ms. The total duration for one experiment was 21 min.

#### 5.2.1.2. Methods for Diffusion-encoding using transverse gradient

For all experiments compared here the NMR setup was with flow on. The lock was off during the experiment. The NMR experiment were either regular DSTE with 16 scans per increment and 16 total increments with full phase cycling or STE with 8 scan per increment with full phase cycling. The DSTE sequence was named “AMdsteosgcGradXPhycy”. The STE sequence was “AMwetstebposgcGradXPhycy_v2”. Both were used with no crusher gradient.
Each experiment started with 4 dummy scans. The gradient power for diffusion encoding is 10% to 80% linear ramp in the X axis. The diffusion time $\Delta$ was 160 ms for all the DOSY experiments and $\delta$ was 2.4 ms. The number of points is 16384 by spectra with a SW of 12 ppm leading to a total of 1.36s of acquisition time. The WET solvent suppression pulses powers were optimized each day as necessary and gradient pulse length was 2ms. The total duration was 21min for DSTE experiments and 10 min for STE experiments.

## 5.3. Section 8 methods

### 5.3.1. Chemistry experiment setup

#### 5.3.1.1. Sample preparation

The reaction was carried out in a glass flask with a special cap that allow capillaries to pass. The volume of solvent, acetonitrile HPLC-grad, was 20mL + the amount within the flowtube whole apparatus (~5 mL). In this volume dissolved 256 mg of the di-amine was dissolved. One has to be careful it oxidises quickly and form a black/brown dye. The solution was then put under magnetic stirring until full dissolution.

The flowtube apparatus was purged of the storing solvent, usually 2-propanol, by the reaction solvent acetonitrile. Once ready the capillaries were put on the glass flask containing the solution and the flow was set to 3 mL/min until pressure is constant and no bubbles appear. 1D NMR experiments and test DOSY experiments are done at this point. They are there to check the medium as well as the efficiency of solvent suppression.

When the solution containing acetonitrile and the di-amine loops continuously and properly, the aldehyde can be added. Using a glass syringe, 0.42 mL of aldehyde was added through the cap. The NMR monitoring experiments were then launched as fast as possible following this addition.

Throughout the reaction the medium will change colour, starting clear with a hint of yellow to orange/brown at the end, the pressure might slightly increase during the reaction (+0.5 bar maximum).

#### 5.3.2. NMR experiment setup

DSTE experiments were interleaved with 1D experiments for the duration of the monitoring. 1D experiments were for safety/control and are not used here. The DSTE sequence name is "AMwetdsteosgcGradXPhycy".

DSTE were 1 scan, 16 increments and 4 dummy scans with a diffusion encoding gradient in X with a linear randomly permutated ramp$^{159}$ of 10% to 80%. The diffusion time $\Delta$ was 100 ms for all the DOSY experiments and $\delta$ was 1.6 ms. Coherence selection gradient (crusher) were on the Y axis and spoilers were on Z axis.
DSTE were 16384 points, SW was 22 ppm for an acquisition time of 0.74 s and a recycle time (D1) of 3s, leading to a full experiment time of 80 s. The solvent suppression WET was on shaped pulses were aimed at the solvent peak (acetonitrile). Shimming routines were done when necessary, every ~20 min.
## 6. Accelerating DOSY experiments

This chapter describes two independent developments of fast DOSY experiments. The main goal of this Ph.D. work was to develop DOSY experiments for online reaction monitoring by flow NMR. For this to happen we needed an NMR experiment that is relatively fast, to be able to fit a lot of different situations that reflect the diversity of reaction monitoring. We aimed for an experiment duration of around one minute, which is an order of magnitude faster than classic DOSY experiments. This was achieved using the “oneshot” approach, as described in section 6.1. Independently from our main work on reaction monitoring, we also contributed to the development and implementation of multivariate processing methods for ultrafast diffusion-ordered NMR spectroscopy. This work is described in section 6.2.

### 6.1. The Oneshot approach

We will here call “oneshot” a sequence that allow to have only one scan per point in the indirect dimension. It means that coherence-transfer pathway selection cannot be achieved with phase cycling, and has to rely on pulsed field gradients. This is a general notion that could apply to any 2D NMR experiment. In DOSY it consists of using 1 scan per gradient value, greatly increasing the speed, by a factor of 8 or 16, corresponding to the minimum number of scans required for CTP selection in the STE and DSTE sequence. This approach also aims at being usable with traditional knowledge of routine diffusion NMR.

#### 6.1.1. Coherence selection using parallel gradients

Probably the better documented method using such approach is the pulse sequence described by the Manchester NMR group in 2002.<sup>136</sup> Part 2 gives most of the detail needed to understand the sequence and its uses, we are here going to evaluate results obtained with this sequence, as well as modified sequences that we implemented.

Pelta et al.<sup>136</sup> aimed at finding a way to improve DOSY by reducing the number of scans for each gradient value. For that they use unbalanced bipolar gradient pulses around the 180° pulses, the goal is to dephase the magnetization that is not refocused by the 180° pulse. Their pulse sequence is a concatenation of works<sup>164,174-176</sup> on diffusion NMR. The work includes adding a spoiler, the bipolar pulses technic as well as the use of unbalanced gradients and a new phase cycling, overall having an updated diffusion NMR sequence. It is a “modern” sequence (from the late 90’s), most of the improvement are the foundation of improved DOSY sequence, including this work.
The sequence is shown in Figure 3.1. The only new parameter one can really tune is the unbalancing factor $\alpha$ which makes the two gradient pulses of each pair in the ratio $1 + \alpha : 1 + \alpha$. The ideal value of $\alpha$ will depend on many factors such as the pulse calibration or any $B_1$ inhomogeneities. It is thus hard to tell which precise value will be the best, Pelta et al. advice a value between 10-20%. The gradient imbalance is achieved as a modification of the usual diffusion encoding gradient. We usually use a ramp of 10-80% of the maximum gradient amplitude, in order to avoid using the amplifier in a regime where it may be nonlinear. Using this we decided to run a test for each value between 10% and 20% imbalance by steps of 1 percentage point. The results are shown in Figure 5.1. It seems that all the results are very close to each other. One can see that there is already a noticeable error for the measured value of the diffusion coefficient, especially in the absence of phase cycling. For the rest of this work, we choose to use an experiment with 13% of alpha.

It is important to note that this approach requires a fit with a modified version of Stejskal-Tanner equation.¹²⁴ The equation used for the fit is therefore¹²⁴

$$S = S_0 \exp \left\{ -\gamma^2 g^2 \delta^2 \left[ \Delta + \frac{\delta(\alpha^2 - 2)}{6} + \frac{\tau(\alpha^2 - 1)}{2} \right] \right\} \qquad 56-1$$
<figure><img src="image_63.png" /></figure>

<figure><img src="image_64.png" /></figure>

<figure><img src="image_65.png" /><figcaption>Figure 6.1 Optimization of the parameter α in the oneshot pulse sequence. The measured diffusion coefficients are shown for a series of values of α. The dotted line are references experiment from a STE in 8scans with full phase cycling.</figcaption></figure>
### 6.1.2. Coherence selection using orthogonal gradients

Another method to achieve coherence selection with gradient pulses is to apply them on an axis that is orthogonal to the diffusion-encoding axis, rather than parallel. This method was for example described by Ferrage and co-workers in STE-DOSY NMR experiments.<sup>138,177</sup> Despite this early development it does not seem to have caught the attention of the community as we did not find much use of this method reported in the literature. The main probable reason being the need to implement it on a triple-axis gradient probe, which is not a common material in NMR lab and facilities. Having this kind of hardware in our lab (for the implementation or pulse sequences derived from MRI methods), we decided to use it.

To implement this method, we were inspired by the sequence X-STE of F. Ferrage et al.<sup>177</sup> Our implementation is shown in figure 6.2. It is important that the diffusion encoding and CTP selection happen at the same time. The diffusion channel is the axis on which the incremented diffusion-encoding pulse are. In this chapter it was the Z axis, to allow meaningful comparison with the other sequences. Each 180° pulse is flanked by a pair of identical gradient pulses. For this strategy to work as well as possible, the total area of the gradient pulses should be different for each pair, and for the spoiler gradient. If the total area is the same then we are at risk of defocusing the unwanted signal, then refocusing it with a latter pair (see part 1).

<figure><img src="image_66.png" /><figcaption>Figure 6.2. The STE orthogonal oneshot sequence. The rainbow-colored pattern pulses represent the incremented gradient for diffusion encoding. The black rectangle are 90° pulse and white ones are 180° on the Rf channel. The gradient pulse is either a sinusoid-like shape for CTP selection or a rounded-square for spoiler. The spoiler and CTP selection pulses are compensated to keep the lock working during the experiment.</figcaption></figure>

As with the parallel encoding we did some tests to see what would be the best value for coherence selection gradient power. The results are displayed in figure 6.3. The method gave result that were satisfactory out-of-flow and allow for easy and rapid customization in the axes that can be used for the experiment. On figure 6.3 one can see that the relation between gradient power and
quality is not straightforward. There is no real trend one could confidently retrieve from this number
of experiments. It seems that value between 10 % to 70 % are safe.

<figure><img src="image_67.png" /><figcaption>figure 6.3 The effect of multiple gradient power for CTP. Note that 0% is not included for DSTE and STE without phase cycling the result can be called non-working DOSY. The ramp was 10-80%.</figcaption></figure>

6.1.3. Comparison between the two

Having characterized the two methods of oneshot we now want to compare them. We recorded DOSY experiments in a conventional fashion and with both parallel or orthogonal-coherence selection gradients, with and without the phase cycling in 8 scans for STE experiments and 16 for DSTE experiments. The experiments with multiple scans but not phase cycling were carried out to focus on the effect of CTP selection, rather than sensitivity. Experiments were also carried out with 1 scan to assess the results obtained with a fast implementation. For this part we choose a power for the CTP gradient to be around 10% for the orthogonal gradient and 13% for alpha in parallel gradient selection.

The results are displayed in figure 6.4, in the style of comparison that was described in part 4.4.2.
As a reminder the closer a point is to the origin, the better the corresponding DOSY results are. For the
STE case, we tried the two methods of gradient-based coherence selection (parallel and orthogonal)
and we can see that, without the phase cycling, using parallel CTP selection gradients does not yield
good results. In contrast, using orthogonal CTP selection gradients gives results that are in the middle.
It is not easy to explain why the phase cycling does not yield better results in that case. Yet in the
absence of phase cycling the orthogonal selection clearly works giving good result even in 1 scan and
it seems to be the only coherence selection that allow 1 scan.
<figure><img src="image_68.png" /><figcaption>Figure 6.4, results of different comparison for coherence selection. STE are in 8 scan and 16 for DSTE as it is the minimum number of scans for a full phase cycling. Result seems to indicate that only orthogonal gradient and phase cycling give some coherence selection. DOSY spectra are available in annexe (figure 6-4s)</figcaption></figure>

With those results in mind, we tried the DSTE sequence. Because there is no parallel oneshot reported in the literature, and because our result with STE sequences shows no improvement, we decided to skip this approach. We developed our own DSTE sequence using the same kind of instruction for orthogonal gradient. From our knowledge it is the first time a DSTE experiments in one scan per increment is reported. The sequence is showed in figure 6.5. The CTP gradients in the DSTE appear 3 times, one for each 180°and are identical within the same pair but slightly different between pairs (vide supra).
<figure><img src="image_69.png" /><figcaption>Figure 6.5. The DSTE orthogonal oneshot pulse sequence. The incremented diffusion encoding pulse are represented with the rainbow-colored pattern pulse. The black rectangle are 90° pulse and white ones are 180° on the Rf channel. The gradient pulse is either a sinusoid-like shape for CTP selection or a rounded-rectangle for spoiler. The spoiler and CTP selection pulses are compensated to keep the lock working during the experiment.</figcaption></figure>

In the DSTE case, coherence-selection is even more important, because achieving it through phase cycling requires more scans than in the STE case. If no coherence selection is done, the errors accumulate and lead to much worse results. This is seen in Figure 6.6, where the error for the DSTE case without CTP selection is larger than for the STE case without CTP selection. The use of orthogonal gradients this time seems to give better result with or without phase cycling and in 1 scan.

## 6.2. Multivariate processing of ultrafast DOSY experiments

This section is largely independent from the rest of the thesis. It describes the design and implementation of multivariate processing methods for ultrafast DOSY experiments, to which I contributed in the early part of my Ph.D. work. The multivariate processing algorithms that we have used will first be briefly described in section 6.2.1., and their application of UF DOSY data will be described in section 6.2.2.

### 6.2.1. Multivariate processing methods for diffusion NMR

Up to this point, we have focused on the DOSY display as a way to analyse diffusion NMR data. This method used to construct a DOSY display is called univariate, because it accounts for one variable at a time, either an intensity or an integral. This approach while interesting has two major limitations: it is user dependent and it is very limited when it comes to overlapping peaks. Another approach, called multivariate processing, consists of processing the data set as a whole. These methods aim at solving the equation:
where X Is the data set, C is a matrix of component decays, P is a matrix of component spectra, and E
is the experimental error (noise). In practice, multivariate methods do not require setting a threshold
and/or integration region. The parameters to apply are usually simple, and include the definition of
the number of components within the mixture, and the possibility to disregard a region for the
processing. This result in a set of 1D spectra with only the pure component, ideally one spectrum per
molecule.

Multivariate methods rely on algorithms that are more user independent, and they can achieve better results for overlapping peaks, to the point of solving overlaps. They can also quicken the analysis as there is no need for manual integration, yet this can be balanced by the duration of the calculation. Also, the number of compounds that can be efficiently separated is somewhat limited. In the case of UF DOSY we tested two algorithms, Direct Exponential Curve Resolution Algorithm (DECRA), and Speedy Component Resolution (SCORE).

DECRA was introduced as a way to analyse diffusion NMR data by Antalek in 1996. It is one of
the most straightforward methods of multivariate that exists, and also the fastest one.¹⁶⁶,¹⁷⁸,¹⁷⁹ It is
available in the processing software Topspin, as well as in the GNAT package for MATLAB.¹⁵⁰ DECRA
relies on the possibility to find the exact solution of Eq. 6-2 in the case of purely exponential decay of
the data as a function of the row index. In the case of diffusion NMR data, this requires that the
gradient ramp be quadratic, meaning that the values of $G_i^2$ are equally spaced, where $G_i$ is the $i^{th}$
gradient amplitude in the list. The benefits of using non-linear gradient ramps for univariate analysis
have been debated in the literature,¹⁸⁰⁻¹⁸² but in the case of DECRA quadratic spacing is a requirement.

The SCORE algorithm was developed by Nilsson and Morris,¹⁸³ and is an evolution of the CORE algorithm originally developed by Stilbs.¹⁸⁴⁻¹⁸⁶ In contrast to DECRA, which relies on an exact solution, (S)CORE relies on an iterative fitting process, using a model for the data. The model is derived from the Stejskal-Tanner equation and can be defined for arbitrary gradient ramps. Initial guesses are made for the diffusion coefficients, and the component spectra and diffusion coefficients are then optimized in turns, until the convergence conditions are met. The improvement of SCORE over CORE arises from the realization that finding the best possible component spectra for a given set of diffusion coefficients is a linear rather than nonlinear optimization problem. In practice, in the implementation provided in GNAT,¹⁵⁰ the user simply has to provide the number of fitted components, and choose how the initial guesses of diffusion coefficients are made. Options are also available, such as the use of a non-negativity constraint within the algorithm.¹⁸³
### 6.2.2. Methods¹⁷⁸

#### 6.2.2.1. Sample preparation

A sample composed of sucrose and propan-1-ol in D₂O was used to perform spatially encoded diffusion NMR experiments. 103 mg of sucrose is dissolved in a solution of 45 µL propanol and 555 µL D₂O to prepare approximate 500 mM concentration of both of the components in D₂O. The mixture was transferred to a standard 5 mm NMR tube for SPEN DNMR experiments.

#### 6.2.2.2. NMR spectroscopy

All the experiments were carried out on a Bruker spectrometer operating at a Larmor frequency of 500.13 MHz, equipped with a triple-axis gradient, broadband inverse-detection probe. The temperature was set at a nominal value of 298 K and the diffusion time was 100 ms for all the DOSY experiments, except when specified.

<figure><img src="image_70.png" /><figcaption>Figure 6.6, the pulse sequence for UF DOSY upper channel show the rf pulse. Medium channel shows the gradient pulses for encoding/acquisition. The bottom channel display coherence selection gradients. Reproduced from Mishra et al.¹⁶⁸ under a creative commons license (CC BY 3.0)</figcaption></figure>

SPEN DOSY experiments were carried out using the pulse sequence shown in figure 6.6. The diffusion encoding parameters were chosen to sweep a 10.2 mm region of the sample with the frequency swept pulses (for both linear and quadratic sweep). The pulses had a bandwidth of 110 kHz, a duration of 1.5 ms, and were applied together with a rectangular gradient, with a short ramp of 25 µs placed symmetrically around the gradient, of 0.2538 T/m. The acquisition consisted of a train of bipolar gradient pulses, with an amplitude of ±0.29 T/m and a duration of 332.8 µs for each gradient pulse, resulting in a spectral width of 3 ppm. 256 loops were acquired, resulting in an acquisition time of 170.4 ms. Spatial encoding was performed along the Z axis. Coherence selection gradients were applied along the Y axis, with amplitude 0.041 T/m for a and 0.077 T/m for b, and a duration of 1000 µs. The spoiler gradient f was applied with 0.052 T/m on the X-axis and 0.061 T/m on the Y-axis, and a duration of 1000 µs. The compensation gradient was g1 = -f. The WET pulse sequence block was used for solvent suppression.
### 6.2.3. DECRA analysis of SPEN DOSY data

Before applying DECRA to SPEN DOSY data, we wanted to test it on conventional DOSY data. In order to do this, we worked with a simple mixture of sucrose and propan-1-ol. This mixture was prepared at a concentration of 0.1 M in D₂O. The 1D ¹H spectrum is shown in Fig 6.7 and it can be seen that peaks overlap in the 3.4–3.6 ppm region. Several conventional DOSY data set were acquired, using quadratic ramps, and different values of the diffusion delay (Δ), and of the duration of the diffusion-encoding gradient pulses (δ). We observed that the results are highly dependent on the choice of parameters. Also, unmixing was never perfect, with artefact visible in the new yielded spectra. On the other hand, field homogeneity was found to have little influence on the quality of the separation. Overall, the process was less straightforward than we thought. By using reduced fitting area in UF, shortening the spatial area that is the diffusion axis, we noticed, as reported¹⁶⁶, that linearity of gradients was very important.

<figure><img src="image_71.png" /><figcaption>Figure 6.7, the UF spectra disentangled using DECRA procedure and the QS (Quadratic Sampling) pulse for spatial encoding. Reproduced from Mishra et al.¹⁷⁸ under a creative commons license (CC BY 3.0)</figcaption></figure>

DECRA was initially not applicable to SPEN DOSY data. This is because the experiments are usually carried out with a linearly swept pulse, also called chirp pulses, that yields data with a linear ramp of effective gradient values:

$$K_n = n \times K_1, \text{ for } n = 2, 3, \dots \qquad 56-3$$
where $K_n$ is the effective gradient area for slice $n$. This is in conflict with the requirement for DECRA, which is:

$$ (K_n)^2 = n \times K_1^2, \text{ for } n = 2, 3, \dots \qquad 56-4 $$

A solution was found by R. Mishra and J.-N. Dumez having a swept pulse of the form¹⁷⁸

$$ v_{rf} = BW \left( \frac{(T_e - t)^2}{T_e^2} - \frac{1}{2} \right) \qquad 56-5 $$

Which give a gradient spacing that match the DECRA quadratic sampling condition.¹⁷⁸ Figure 6.8 shows the shape of the new pulse.

<figure><img src="image_72.png" /><figcaption>Figure 6.8, (a) Amplitude and (b) phase wrapped to 360° of the quadratic sampling (QS) chirp pulse of length 1:5 ms. Reproduced from Mishra et al.¹⁷⁸ under a creative commons license (CC BY 3.0)</figcaption></figure>

This new swept pulse allowed to have unmixing of propanol and sucrose, after trial-and-error optimization of the spatial region used for the analysis. The algorithm, which is not based on a fitting process, required less than 1 second to run. At this stage, the analysis was found to fail for more than 2 spectra, for example when the residual HDO peak is kept in the analysis. For this reason, that signal was suppressed using the WET solvent-suppression block. The failure to separate 3 spectra might be because of insufficient sensitivity (multivariate methods will demand for more and more SNR as the number of compounds increase), and also because gradient non-uniformity is no accounted for in the analysis. Note that when carrying the experiment on conventional DOSY (non-uf) separation of more than 3 compounds was also very difficult.

### 6.2.4. SCORE UF

We also applied the SCORE algorithm to SPEN DOSY data. In this case the experiment does not need any adjustment, as SCORE does not have special requirements on the sampling scheme. However, the algorithm needs to be adapted to match the specific form of the Stejskal-found in SPEN DOSY. A general form of this equation with linear ramping can be written:

$$ y = a_1 e^{-a_2 \Delta' K^2} \qquad 56-6 $$
With $a_1$ and $a_2$ adjustable parameters, those that the SCORE procedure will optimize. In the UF DOSY $\Delta'$ and K are slightly different. Spatial encoding and parallelization that make the process possible is linked to a spatial axis z rather than incrementation in time. This gives:

$$y = a_1 e^{-a_2 \Delta' K(z)^2} \qquad 66-6$$

With $\Delta' = \Delta - Te$ and $K(z) = 2\gamma G[2Te - 2t_{flip}(z)]$. Note that $t_{flip}(z)$ is defined as the moment spins in z position undergo their flip due to the swept pulse. It is calculated in the classic CHIRP as:¹⁴¹

$$t_{flip}(z) = \frac{\Omega_i - \gamma G z - \omega_{rf}^i}{R} \qquad 56-7$$

With R the swept-rate, $\omega_{rf}^i$ the initial frequency-offset. The inner and outer loop can then do the SCORE routine and yield figure 5.8 upon successful separation of the compounds.

<figure><img src="image_73.png" /><figcaption>Figure 6.9 SCORE processing of SPEN DNMR data for a mixture of propan-1-ol and sucrose in D2O, compared to the spectra of the compounds acquired separately. The residual HDO peak is suppressed by a WET pulse sequence block. (a) 1D spectrum of a mixture of propan-1-ol and sucrose in D2O. (c, e) Components obtained by SCORE processing of SPEN DNMR data. (b, d) 1D spectra of (b) propan-1-ol and (d) sucrose alone in D2O. The 1D spectra are obtained as slices of EPSI data. Reproduced from Mishra et al. ¹⁷⁸ under a creative commons license (CC BY 3.0)</figcaption></figure>

SCORE and DECRA gave similar output and quality of separation, this is also true for conventional (non-UF) methods. The main difference between them is applicability and ease-of-use. SCORE as some parameters than can be changed and tunes which also implies that it will work on a trial-and-error fashion that can take time. The condition such as non-negative constrain will lengthen the analysis but can allow it to work. The main advantage versus DECRA is that it does not need special pulses or
modification of the experiments. In theory one could use or try SCORE on any DOSY data which is a huge advantage against DECRA that need a specific set of conditions.

### 6.2.5. UF DOSY for flow NMR monitoring

Since the main goal of this Ph.D. project was to achieve online reaction monitoring with fast DOSY methods that are flow compatible, one may wonder why spatially-encoded DOSY was not used further. The main interest of SPEN methods is the experiment duration, that is often under one second. However, this ultimate speed comes at the cost of several challenges, and it may also not be needed for the kind of applications targeted in this work. Here we will discuss these two points.

Several limitations of the SPEN DOSY method arise from the use of echo-planar spectroscopic imaging (EPSI) for detection. EPSI is very demanding for the gradient coils. The rapid change of polarity from the gradients put a physical constrain on the coils, making them heat, vibrate or even twisting them. This limits the minimum echo spacing that can be achieved, and as a result the spectral width, according to:

$$SW = \frac{1}{2Ta'}, \qquad 56-8$$

Where $2Ta'$ is the echo spacing. The SW is often limited to ~4 ppm, aliasing possibly allowing to have all the peaks but in a crowded version with possible wrong chemical shifts.¹⁸⁷ This could probably be addressed with the use of “interleaved” acquisitions, as for ultrafast 2D spectroscopy experiments, but such strategy has not been implemented so far.

Another limitation results from the maximum duration during which the EPSI train can be sustained by the gradient coil. On high-resolution probes, the acquisition time is limited to about 150 ms, which limits the resolution that can be achieved in the spectral dimension. This could be addressed with the use of more robust gradients, for example those available on a micro-imaging probe, but it will require dedicated developments. These two limitations, together with the fact that setting up SPEN DOSY experiments is not straightforward, mean that specific motivations are needed to use spatial parallelization as a way to accelerate diffusion experiments.

Reaction monitoring by flow NMR, using devices such as the InsightMR flow tube, typically do not aim at monitoring reactions that occurs in time as short as a minute or less, since this is shorter than the transfer time from the reaction flask to the spectrometer. The ultimate speed that can be achieved with SPEN DOSY (acquiring all the data in one scan), is thus not mandatory. Since preliminary experiments suggested that mitigating flow effects in SPEN DOSY would be even more complicated than for conventional DOSY, we decided to leave that approach aside in the context of this Ph.D. work.

## 6.3. Conclusion

We have seen strategies to increase the speed of the DOSY experiment by doing coherence selection using gradients. This was done in two ways, with either parallel or orthogonal gradients. We found that
using orthogonal gradients was a functioning way to bypass the phase cycling. This new way of CTP selection enables diffusion experiment in 1 scan. We can now reach an experiment time of less than 2 min instead of 10 to 20 min. The methods are relatively easy to implement and has drawback associated with its 1 scan nature (sensitivity mostly) but seems a convenient way to accelerate the process. This will be useful for our aim of monitoring reaction as it will allow to study a larger array of reactions.

We have seen the so-called way of multivariate processing, instead of univariate processing. This way of processing the data make the analysis much more straightforward and less user-dependant. It even allows to go further and solve the problem of overlapping. We have seen two of those technics applied in Ultrafast-NMR SCORE and DECRA. Those technics while having peculiar need such as the choice of constrain for SCORE or the use of quadratic gradient sampling or ramp for DECRA have shown unmixing capabilities.

The implementation of SPEN UF (or UF DOSY) is not straightforward and give data that are not easy to process and could be dangerous for the spectrometer to acquire. Despite astonishing time scale reached, with experiment taking less than a second, we believe it would not be easy to use for non-NMR specialist. This coupled with the relatively good time scale (<2 min) reached by 1 scan diffusion NMR make us choose the latter option. It remains a very interesting technic and we believe that further development could make it closer to routine technics. Once this point reached it would make a very powerful tool for monitoring but the accessibility to non-NMR specialist is a bit too far at the moment.
# 7. DOSY NMR in continuous flow.

## 7.1. Presentation of the apparatus

To carry out flow NMR experiments we used a commercial device, called InsightMR, that we briefly described in part 2.2.1. We will here describe our experimental setup in details and its evolution throughout this work.

The InsightMR product contains several elements. The flow tube itself is a glass NMR-tube-like insert located within the probe like a regular NMR tube. Capillaries carry the solution to the magnet and back. They are surrounded by a jacket. In order to carry out flow NMR experiments, additional elements are needed, and in particular a pump that generates the flow. A "chiller" is also used to flow the thermoregulation liquid, we are not going to detail it as it does not play any major role. In this subsection we will describe our experience with the operation of these devices.

### 7.1.1. The tubbing

The exterior jacket is one large tube made of rigid polymer. It is not supposed to be in contact with any chemical/liquid. It consists of a black rigid and resistant polymer wrapped around the main line. It protects against light, and against possible aggression from the exterior like pinching or physical damage.

Inside the jacket is found the large tube. Made of a polymer, it carries the thermoregulation fluid. In this tube are located the two capillaries that carry the flowing medium. The temperature regulation happens within the large tube, with a flow of liquid that is circulated by the chiller. Temperature regulation is possible with fluids other than water, such as ethylene glycol or some specialized oil that have a greater range of temperature than water, but this was not tested. Most of our experiments were carried out at room temperature. For this reason, temperature regulation was not used, as testing showed no difference if the room is correctly air-conditioned. Furthermore, the probe temperature regulation was calibrated carefully by using a standard sample of methanol. Experiment onflow and out of flow showed repeatability of diffusion coefficient in this fashion, showing no need for more temperature regulation. The temperature regulation of the tubbing, is a source of both vibrations and possible leaks so after testing showed no difference it was mostly discarded despite the importance of temperature in diffusion measuring.

Finally, within the large tube are the two capillaries that carries the medium of interest. During this work we tested two main kind of capillaries PEEK and PTFE both with the same diameter of 0.5 mm. Those materials are known to resist to a wide range of solvents, and conditions, thus chemo-resistance was not a problem for either of them for this work. Interestingly the elasticity and behavior of those capillaries are not the same and PEEK is much more rigid and hard to use than the PTFE. We
settled for PTFE in the connection to the pump and back for simplicity of handling. The rest of the capillaries that are the main line are in PEEK.

The small inner diameter of the capillaries, 0.5 mm, raised some challenges, especially for concentrated solutions. Reaction monitoring applications in organic chemical synthesis can involve highly concentrated solutions. As such we aimed for a concentration of 100 mM in most of our mixtures, whether they are reacting or not. In those conditions, solubility can start to be a problem and what is not apparent in a large flask (50 to 100 mL) can become a problem in an 0.5 mm capillary. Our tests with sucrose showed that, despite excellent solubility of sucrose in water, if it happens that a co-solvent is found in the flowtube (as impurities most of the time), the solubility of sucrose is reduced and a solid can start to precipitate, blocking the capillaries. Fortunately, the problem is easy to solve as a cleared batch of solvent (water) will quickly solubilize the solid (sucrose). Such blockage can have two main consequences, the increase of the pressure in the tubbing and loss of homogeneity within the flowtube as a solid perturbs the analysis. Another problem is that the very small size of the capillaries makes it so that it changes temperature extremely fast. The capillaries themselves are unprotected for ~15cm when they go in and out of the reactor which allows the medium inside to change its temperature at the outside temperature.¹¹⁷ Here we worked at room temperature so this was not an issue for most of the work. Figure 7.1 illustrates a schematic of the insightMR/flowtube, it re-use the lexicon we have used in the above paragraph (Jacket, large tube...).

<figure><img src="image_74.png" /></figure>

Figure 7.1 an illustration of the flowtube. The grey jacket represents the protective semi-rigid outside layer. The green "large tube" is the canal for thermoregulation fluid. The red capillary is the intake one that takes out the medium and pour it into the NMR-sample flow tube. The blue one retakes it and pour it back to the solution medium. The thermo control unit is both a pump for the thermoregulation fluid and a thermo-regulator. The unit "pump and capillaries handling" include a small plastic interface where the tubes are connected.

The increase of pressure is the main problem one could face and it should be acknowledged as fast as possible to avoid any catastrophic outcomes. The main sources of pressure increase are the nature of the flowing sample dictated mostly by the kind of solvent and its viscosity. Other sources include disruption of the line by the inside or outside. Inside disruptions are blockages, partial or total,
linked to solid formation. Fortunately, the ever-flowing nature of the medium made total long-lived
blockage unlikely. The main problem will be blockage from insoluble (salt, dust...) compounds that will
often be partial. The increase of pressure will thus be treacherous as it will be shallow and hard to
decipher from the normal state. It is to be noted that the problem could be much more concerning if
solid with low solubility find their way within the reaction, solid formation should be avoided if
possible.¹⁷⁶ We decided to use a sinter filter (so-called "last-drop filter") to avoid absorption of solid
from the medium as much as possible. All of our reagents were tested for solubility and our solvents
were of HPLC grade to avoid any solid particle. Storage of the reactor and glassware are also made in
a fashion that should avoid dust. It is to be noted that despite those efforts we have seen multiple
unwanted and terrifying increase of pressure that were not always well explained.

Another possible source of pressure increase is outside disruption, the twisting and possible
knotting of the capillaries or both capillaries and large tube. As the capillaries are not accessible from
the outside, it is hard to know in which state they are. Pinching, knotting or twisting are known to
increase the pressure and are not easy to see, just like precipitation of a solid in the middle of the line.
The answer to those problems is a set of good practice recommendations, such as storing the flowtube
in the same position. Avoiding twisting either on its axis (as it is cylindrical) or otherwise and any kind
bending, seems to give good result.

The cleaning and storage of the line is an important point as we have seen in this work several
instances of contaminations. The lack of access to the main-line as well as the lack of visibility makes
the operation somewhat difficult. Our first protocol included rinsing with the solvent of the
sample/reaction for 15 min then replacing it with water for 10 min. The pipeline was left in this state
with water in it for storage. The water used was osmosed water to avoid any traces of minerals or salt
that could precipitate. We did encounter some problems because of the water, mostly we feared the
apparition of algae that could damage the apparatus. We then settled for another solvent much more
common in storing and maintaining electronics, isopropanol. The protocol was also further modified
with a cleaning and rinsing of 30 min. The isopropanol although good for storing is our most viscous
solvent which restrict us from going above 1 mL/min for the filing/rinsing.

As a side note and despite this work containing mostly work in water, we discourage water use
in the flowtube especially if one wants to also use organic solvent. Using water is a challenging
condition both for its viscosity, solubilization properties and great abilities to support life, extra care
should be used with it.
### 7.1.2. The pumps

#### 7.1.2.1. Dual piston HPLC Azura

The HPLC Azura pump is the by-default pump sold by the manufacturer of the insighMR flowtube. It is a dual piston pump *a priori* designed for HPLC. This kind of pump is capable of handling strong pressure (up to 400 bar) and high temperature variation (-40 to 200°C) in the pumped liquid, as well as a fine regulation of the flow rate (0.001 to 10 mL/min).¹¹⁷ It works with a set of two pistons that make inverse motion with one another so that it can provide a steady continuous flow. The pistons and fine moving parts are in direct contact with the flowing medium. One of the most critical parts of the machinery is the check-valves that control the motion of the fluid so that the piston action does not cancel out. The flowing medium pass through those check-valves.

In order to work, this kind of pump needs to be manually primed, meaning that the user should fill the pump head with the solvent of the medium, thus getting rid of all the air inside. This procedure can be done easily, but needs to be done very carefully and can be long depending on the solvent. If not done well, the pump can be impacted and give wrong flow rate or simply stop pumping while seemingly working. The piston being inside of a bubble of gas continue the pumping motion without being efficient at generating the flow.

Even if priming is done well some bubbles may appear in the system. The phenomena of cavitation can be attributed to degassing of the medium or by vaporization of the solvent due to suction pressure.¹⁰⁵ This can be damaging for the pump as solute may precipitate and hinder the valves and pistons. It can also slow the flow-rate, increase the pressure or send the bubble inside the NMR flow tube, which is disastrous for the field homogeneity and the overall quality of the analysis. For this reason, a careful degassing of the solvent or the medium was performed using 15 min of ultrasounds prior to priming. Even with that the vaporization of the solvent is still possible and will lead to bubbles.

The pump being well specialized in pumping exclusively fluids, it was impossible to use it to empty the flowtube. Such emptying was done using a simple syringe filled with air that needed to be pushed slowly and constantly to get rid of the liquid (manually). This operation was not very efficient and could be long depending on the fluid viscosity. It called for the use of another pump that could pump gas to empty the flowtube. It is also interesting to note that any increase of pressure while pumping gas would have been a clear sign of a blockage, which is useful for troubleshooting.

Because of those variables, and due to the different natures of the solvents that were tested, calibration of the pump was important as the indicated flow rate could be different from the actual flow rate. Calibration was done in a straightforward way using chronometer and measuring-glassware while monitoring the pressure indicated by the pump. If not successful the rate had to be adapted and if the difference was deemed too important, we would empty the pump and re-prime it.
The direct contact of check-valves and piston with the solvent was a problem as it limits the compatibility of the pump with the medium. The parts being ceramic and metal they are not at risk when in contact with most organic solvent. Yet, the parts are very sensitive to gas and solids, it often happened that the valves were blocked by something, to clean them was half a day long in a mixture of methanol and water under ultrasound. Those valves were considered as consumable needing change quite often. It is thought, from reading literature, that such kind of pump are not meant to work at those low-pressure with high-flowrate (relative to HPLC).

In conclusion despite seemingly good capability the Azura pump was not deemed a good starting pump for our use. It might come handy in more advanced situation if harsher condition or medium are necessary. As it stands the rest of the apparatus is not able to withstand high temperature or pressure the NMR flowtube being limited to 10 bars. The lack of compatibility with a variety of medium as well as its long needs for priming, cleaning and high cost of maintenance due to the valves, made it difficult to use.

#### 7.1.2.2. Vapourtec Peristatic pump

The peristatic pump by Vapourtec is a pump that was brought to our attention by the work of Hall<sup>188</sup>, and which seemed more convenient to monitor reactions. It is a pump aimed at pumping reaction medium. The peristaltic motion consists of the pinching of a flexible tube called peristatic tube. This pinching is slid along the tube by the rotors in a controlled way thus creating the flow. It is convenient as it allows to pump a large variety of medium from liquid to slurry or gas. Peristaltic pumps are the default pumping option for many NMR setup especially for benchwork/low-field NMR. Despite the large array of possible medium it can handle, it is less efficient in providing very accurate flow and handling strong pressure or temperature.

We have peristatic pumps in the lab used for NMR at low field. From the past experience of our coworker, we validated that it is good at handling different media, including challenging ones.<sup>189</sup> Yet the pump we had at hand or traditionally available does not always have good or well-documented chemical compatibility. The handling of pressure was also a problem, and the pump we tested were not able to pump the liquid in the flow tube. The pressure was too intense due to capillaries being long and thin. In contrast, the Vapourtec peristatic pump is designed for continuous flow reaction<sup>190,191</sup> and is well suited for our purpose.

The part of the pump handling the reaction medium is made of a polymer that comes with a comprehensive list of what reagents it can handle. This peristatic tube is the most critical point in the setup. It has to resist any chemical aggression, either by dissolution from the solvent or of other kind such as acidic/basic. Chemical compatibility is crucial as the tube is always under stress by the rotor and its plastic abilities and resilience should not be impaired otherwise the peristatic flow will not work
well. A major problem was that we believe it may be able to absorb some solvent without looking
damaged and release it within the pumped medium. The peristatic tube has a limited life duration and
should be changed regularly.

The peristatic motion does not need for priming as it can handle fluid and gas and create little
to no bubble on its own. Despite that to test both the state of the peristatic tube and the accuracy of
the flow, the pump includes a calibration routine much like the Azura that allow to compensate for
flow-rate inaccuracy. The time needed for this is approximately 10 min.

The critical factor being the peristatic tube it needs to be inspected and checked regularly as
well as stored in the right conditions. There is a variety of tubes to handle different pressures and
solvents. It is interesting to notice that constructor advise the use of a back pressure to ensure better
result, we did not use it. This could be the cause of reduced lifetime for the tubes although unlikely, as
back-pressure are more often advised to avoid cavitation and improve flow regularity. Despite this, it
seems that the cost of maintenance was less than with the Azura pump as the valves are too costly
and fragile.

The Vapourtec pump is less prone to handle complex situations in regard of flow physics (high or low pressure, temperature etc.) but more efficient at handling a variety of media. This seems appropriate for reaction monitoring. Even if reactions are tested before flow NMR experiments, unexpected problems may happen and the possibility to pump even slurry is welcomed. It also allows to empty and clean the flowtube using the same pump instead of manually which is more reliable.

It asks the question, have we switched the critical point from the pump to the capillaries. The capillaries themselves are not made to handle slurry or solid and due to their thin nature may block quickly. A blockage of the capillaries could be much worse than a blockage of the pump and cause damage to system or even to the probe. For this reason, we decided to take more precaution with the last droplet filter on the receiving end of the capillaries to allow for filtration of solids. We deemed the peristatic motion and pump more useful for our use with its polyvalence. It is still not the perfect systems as flow quality using such pump is sub-optimal.¹¹⁷ We are working on installing a new kind of pump that use quaternary pistons which should have the combined advantage of the two pumps. It is a less user-friendly so we take more time to make it work as the two pumps discussed here were very user-friendly.

## 7.2. Flow compensation and effect

We are here going to discuss the effect of flow in diffusion NMR experiments. It is interesting to note
that we use an unshielded magnet and a flow rate of 3 mL/min max to spare the flowtube of pressure-
related problems. In those conditions we did not see much difference between on-flow and out-of-
flow 1D ¹H spectra, except possible shimming/locking effects more related to the use of non-deuterated solvent.

### 7.2.1. Methods

The sample is made of pyridine, sucrose and butanol at 0.1 M in 30 mL of H₂O. The medium was flowed using the Vapourtec pump (peristatic motion) at 3 mL/min. All the experiments were carried out on a Bruker spectrometer operating at a Larmor frequency of 500.13 MHz, equipped with a triple-axis gradient, broadband inverse-detection probe. The temperature was set at a nominal value of 298 K and the diffusion time Δ was 160 ms for all the DOSY experiments, and δ was 1.4 ms. Spoiler gradients were on the Z axis. All the experiments used their full phase cycling, STE experiments thus had 8 scans are were ~12 min long, while DSTE experiments had 16 scans and were ~25min long.

### 7.2.2. flow effect on diffusion

The effect of flow on diffusion experiments has been characterized already. Consider the magnetization helix from by diffusion-encoding gradient pulses, as described in section 1.1.2. In the absence of diffusion or other motion, refocusing of the helix would be perfect. In the presence of diffusion, refocusing is unperfect due to the individual displacement of spins, and this results in an attenuation of the signal. In the presence of flow, assuming uniform motion of the spins, the helix is displaced and this result, after the refocusing gradient, into an additional phase, which is independent of position.⁵ This phenomenon is illustrated in figure 7.2 taken from the work of Sinnaeve²¹
<figure><img src="image_75.png" /></figure>

Figure 7.2, Simulation of the effects of diffusion and unidirectional translation on the magnetization during a PFG spin echo. The transverse magnetization, with initial position along the y-axis, is assumed here to be on resonance. Here every line represents a single randomly selected spin at each position along the z-axis, as opposed to Fig. where each line represents the magnetization, i.e., an ensemble of spins. The spins are dephased by a first, defocusing constant gradient pulse with 1.25 G/cm strength and 500 ms duration. The 180° y pulse inverts the phase of the spins, so a second, refocusing gradient pulse with the same gradient strength, polarity, and duration will realign the spins to the y-axis, concluding the spin echo. The diffusion delay of the spin echo in this simulation was 10 s, chosen so long for the purpose of illustration. If during the spin-echo diffusion takes place (with a diffusion coefficient D of 4×10⁹ m²/s), the molecules displace randomly across the z-axis, causing the helix pattern to disintegrate. When each line would have represented the magnetization instead of a single selected spin, the visual effect would be that the helix diameter attenuates due to diffusion, as is illustrated and explained in the Figure 80 on page 80 in the book by Price¹⁹². The refocusing of the second gradient pulse is now no longer ideal, leaving a stochastic, irreversible dephasing of the spins along the z-axis. The red line in the top view represents the vector sum of all spins. When additionally, unidirectional translation takes place (here simulated with a velocity v_z of 8.7 cm/s, chosen so high for the purpose of illustration), the molecules and thus the helix pattern will experience a constant shift in position along the z-axis, causing a constant phase shift for all spins after the refocusing gradient. Note that since the magnetic field gradient is constant, the helix pitch is constant along the z-axis and therefore diffusion and unidirectional translation induce a z-position independent loss of coherence or phase shift, respectively. Reproduced from Sinnaeve²¹ with permission Copyright © 2012 Wiley Periodicals, Inc.

### 7.2.2.1. Diffusion-encoding with longitudinal gradients

We evaluate different diffusion NMR pulses sequences in continuous flow, starting with the use of the Z axis for diffusion encoding. Since the mail direction of the flow is also along Z, flow effects are expected to be stronger in this configuration. However, NMR probes equipped with a Z-only gradient are more common.
Since we chose to work with a model mixture in water, our flowrate was limited to 3 mL/min. Indeed, water is a relatively viscous solvent compared to organic solvent (Acetonitrile, chloroform, DCM...). As the flowrate increase so does the pressure inside the flowtube, and this is linked to viscosity.¹⁰⁵ The guidelines for the maximum pressure for the flowtube state that it is resistant “up to 10 bars”. As we relied on the pressure sensor of the pump only, which may not be accurate for the pressure at various critical point of the apparatus, we used conservative values of the flow rates.¹¹⁷ A flow rate of 3 mL/min is both relevant for monitoring application and sufficient to observe significant flow effects. Note that the choice of water as a solvent date from early investigation, when the fumehood was not available and toxic solvents had to be avoided.

Unsurprisingly, STE experiments in flow using longitudinal gradients gave nonsensical results. They are not represented here as DOSY displays, which would have little meaning but the size of the error along with the recorded diffusion coefficient were very large. The results obtained with DSTE experiments are more relevant and are displayed in figure 7.3. It is interesting to note that the relation between the measured diffusion coefficient and the flow rate seems almost linear. Indeed, the difference between coefficient stay the same. The flow seems to also increase the uncertainty.

Independently from our work, Hintermair and coworkers¹⁹³ also investigated diffusion NMR in continuous flow.¹⁸² They worked with an InsightMR flow tube and a single-axis gradient probe. They found that with the same peristatic apparatus and suitable parameter choices for the DSTE sequence they indeed get a linear relation between flow-rates and measured diffusion coefficients. In order to have a linear dependence, they notably used a small diffusion delay, $\Delta$, that is counterbalanced by longer durations of the diffusion-encoding gradient pulses, $\delta$. The dependence as a function of the flow rate was otherwise more complex. They also observed that the uncertainty also increases at significant flow-rate, making separation more and more difficult. Interestingly, they also observed that, using a rotary tetra-piston pump that delivers a steady flow rate,¹¹⁷,¹³⁴ the estimated diffusion coefficients were approximately constant as a function of the flow rate. It would be interesting to compare the accuracy of this approach, and of the method described in the following.
<figure><img src="image_76.png" /><figcaption>Figure 7.3, The DSTE parallel to the flow diffusion encoding gradient. The dotted line represents the reference experiments in the same setup with no flow. Error bar come from the uncertainty calculated from the fitting. The diffusion coefficient come from the average of three sites belonging to same molecule see part 4.</figcaption></figure>

7.2.2.2. *Using transverse gradient*

With our lack of success to deliver accurate diffusion coefficient using diffusion-encoding gradients that are parallel to the main direction of the flow, we decided to try to use transverse gradients for diffusion-encoding. Since the flow is expected to be mainly laminar and longitudinal, it should have much less influence on diffusion experiments in that case. The experimental results are displayed in figure 7.4 In the case of STE experiments, a clear effect of the flow is observed. Both the estimated diffusion coefficients and the corresponding uncertainties increase when the flow rate increases, in a non-linear fashion. Spectral separation is thus very poor at 3 mL/min, especially between butanol and pyridine.

In contrast, in the case of DSTE experiments, flow effects appear to be very small. The flow-rate and the measured diffusion coefficients do not seem to be correlated, and the results are very close whatever the flowrate. Even the uncertainty does not appear to move significantly. There is a slight deviation from the reference value, which is difficult to explain, but which is also much smaller than any of the errors observed with the other methods. It is good to remind that because of the lack of locking on flow a wide array of causes could explain this relatively small deviation. On average the deviation from the reference is 1.9% with a maximum at 4.2%.

It is interesting to note that double-diffusion encoding is still needed when using transverse encoding. This implies that their flow has a transverse component, which is consistent with the fact that the flow tube does not have perfect cylindrical symmetry. In the case of transverse encoding, double diffusion encoding works very well while it fails for longitudinal encoding. This may simply be
due to the relative magnitude of flow effects in the two cases, which make flow fluctuations negligible
in the transverse encoding case.

<figure><img src="image_77.png" /></figure>

<figure><img src="image_78.png" /><figcaption>Figure 7.4, diffusion coefficient at different flowrate. The dotted lines are conventional experiment out-of-flow. The error bars represent the uncertainty from the DOSY plot.</figcaption></figure>

Figure 7.5 summarizes the results obtained in this chapter, using the representation presented in part 4.4.2. This gives a good idea of the experiment's usability and of the impact of the flow. One can see that DSTE experiments using longitudinal encoding at 1 mL/min is only slightly degraded. This worsens when the flow rate increases, although with a simple relationship between estimated diffusion coefficient and flow rate. This is relevant for experiments carried out with a single-axis gradient probe. Along the same line, we can see that the STE experiment with transverse encoding seems good at 1 mL/min but fails at higher flow rates. In this case the dependence of the estimated diffusion coefficients on the flow rate is more complex. Then, DSTE experiments using transverse encoding gave results that are very close to those of the conventional experiment, no matter the flowrate. This makes it a very interesting option for reaction monitoring applications.
<figure><img src="image_79.png" /><figcaption>Figure 7.5 quality of the experiment at 1,2,3 mL/min flowrate. The grey experiments are references out-of-flow. green experiments use orthogonal gradients, blue use parallel no other modification were made to those experiments.</figcaption></figure>

Finally, as a proof of concept for the reaction monitoring, that was our goal and that will be discussed in the next part, we decide to use the orthogonal gradient coherence selection method (see part 5) with the orthogonal diffusion encoding to record a 1 scan spectra on flow at 3 mL/min. The DOSY display is show in figure 7.6. It is very satisfactory knowing we go from ~25 min for the conventional method to 90 s.

<figure><img src="image_80.png" /><figcaption>Figure 7.6 The DOSY display for a 1 scan DSTE DOSY on flow (3 mL/min) total experiment duration was 90s.</figcaption></figure>
# 8. Monitoring a flowing reaction with DOSY

## 8.1. Prerequisite for Reaction monitoring

### 8.1.1. Solvent suppression

Throughout this work we have used non-deuterated solvent. There are several reasons for this, one of which is the cost of deuterated solvents in large quantities. Indeed, to fill the reaction vessel and the flowtube, and have realistic conditions the sample volume has to be increased from the usual ~500 µL to ~30 mL. Another problem is that deuterated solvents are known to have different viscosities. This change of viscosity can impact diffusion heavily, as we have seen. Also, it can modify the kinetic properties of the reaction, especially if proton/deuteron motion is involved¹⁹⁴,¹⁹⁵ but possibly also in other cases. To have realistic conditions we have to use non-deuterated solvent, as such we need a way to suppress its signal and work without the spectrometer lock. Solvent suppression techniques are widespread in NMR. There is no universally best method, and they have to be compared in an application-specific way. Here we compared three methods that were already used with flow NMR in the past: pre-saturation¹⁹⁶, excitation sculpting¹⁹⁷, and finally WET¹⁹⁸.

### 8.1.2. Presaturation

Presaturation is one of the oldest¹⁹⁶ and easiest methods for solvent suppression. The principle is simple and relies on using a long, low-power pulse at the solvent frequency to saturate it. The longer the pulse the more frequency selective is its saturation profile. The method consists of putting this long low-power pulse just before the excitation pulse in order to suppress the solvent signal.

This method, while straightforward, does not always give best performance. From a practical point-of-view it seems to be very sample dependent. Furthermore, in our experience we found it hard to predict which combination of power/duration would be the right one to efficiently suppress the signal. This resulted in rather long time for pulse calibration/optimization. It also requires some care, very long pulse with excessive power can damage the transmitter or at least reduce the lifetime of the hardware.

As for flow effects and monitoring, presaturation have the issue of needing rather long dead-time during the long pulse, up to seconds. This could make the experiment notably longer, in our case we use the recovery delay so it is less impactful. Related to this duration, a strong apparent relaxation is reported due to the influx of solvent, lessening the effectiveness of the suppression.¹⁹⁹ Indeed, the long pulse duration allows medium to flow in and out, lessening the effect of the saturation.

It is also known that presaturation influences the signals for protons that are in exchange with solvent protons, which can be a problem. It is not a direct problem for us but in the case of a chemical reaction, interactions with the solvent are common and it could be an issue. Finally, methods exist to
improve the presaturation,²⁰⁰,²⁰¹ especially to provide answers for suppressing multiple frequencies,
but we chose to use instead gradient-based methods.²⁰⁰,²⁰²

One of our attempts at presaturation is displayed in figure 8.1, it shows a sample of sucrose and DMSO in water on-flow (3 mL/min). The presaturation was 3 s long to match the relaxation recovery delay that we typically use. The spectra have the same level of receiver gain and are displayed such that the whole peak is in the frame. The decrease of the water peak intensity is of ~10 folds with presaturation. Note that it is not a very good result if we compare to other results in the literature. This is attributed to a sub-optimal optimization of the pulse and delay. This could come from flow effect but our experiments out-of-flow showed a rather weak solvent suppression by 10 to 60 folds.

<figure><img src="image_81.png" /><figcaption>Figure 8.1, sample of 0.1 M of DMSO and sucrose in H2O at 3 mL/min. a) no solvent suppression is used. b) spectrum presaturation is used on water for a duration of 3 s. The zoom level is chosen in both cases so that all the peaks appear whole within the frame. The approximate reduction of intensity for water is 10-fold.</figcaption></figure>

8.1.2.1. *Excitation sculpting*

Excitation sculpting is a solvent suppression technic first named and published in 1995, nicknamed "Water Suppression That Work".¹⁹⁷ The technic is relatively simple and relies on a block consisting of a selective 180° pulse at the solvent's signal and a non-selective 180° pulse, flanked by two identical gradients pulses. The net result is that the excited solvent magnetisation will be dephased by the gradient pulses and the corresponding signal removed, while the rest will be untouched. This block is doubled for better result and placed at the end of the sequence of interest. The sequence and typical results are shown in figure 8.2.
<figure><img src="image_82.png" /><figcaption>Figure 8.2 a) typical block for excitation sculpting, the bottom axe represent gradient. b) typical DOSY transient spectrum given with excitation sculpting. The arrows underline the baseline distortion (J-modulation).</figcaption></figure>

This technique has been the topic of a number of developments, and it is often found associated with DOSY.<sup>20,203</sup> The solvent suppression is impressively efficient and quick to obtain in our experience and as reported. Yet it is known to cause J-modulation effects, that result in the distortion of multiplets.<sup>195</sup> In our attempts, the J-modulation effects that appeared from the use of excitation sculpting were often visible, and distorted the baseline, thus making integration difficult or hazardous. This compounds with the fact that multiplets can already be difficult to integrate accurately because of their lower intensity. While improved versions of excitation sculpting exist, which compensate to some extent for J-modulation effects, we decided to set aside this approach for a more robust and easily accessible one.

Figure 8.3 shows an example of spectrum obtained with excitation sculpting under flow (3 ml/min). The sample is made of a mixture of alcohols (iPrOH, n-PrOH and EtOH) in water. One can clearly see that the solvent peak almost disappears entirely.
<figure><img src="image_83.png" /><figcaption>Figure 8.3. 1D ¹H spectra of an 0.1 M alcohol mixture. a) no solvent suppression. b) solvent suppression with excitation sculpting, solvent suppression on water for a duration of 3 s. The zoom level is chosen in both cases so that all the peaks appear whole within the frame. The approximate reduction of intensity for water is more than 600 folds.</figcaption></figure>

#### 8.1.2.2. WET - Water suppression Enhanced through T1 effects

WET is a solvent suppression technic that was first published in 1993¹⁹⁸ and that was designed for MRI applications. The principles of WET are to use selective shaped pulses to selectively excite the magnetization of the solvent protons and then to dephase their signal using gradient pulses. This is repeated 4 times to suppress the magnetization, and then the sequence of interest proceeds. The pulses angles are calculated using simulations to be optimal in difficult situations such as field inhomogeneities, and different T₁ values for the solvent spins, both issues being common in MRI.¹⁹⁸ This makes the sequence rather useful as it will be effective in a large array of situations. The duration of the whole block is usually around 100 ms, much faster than presaturation. The WET solvent suppression bloc is illustrated in figure 7.4. It is nowadays the default method used on flow NMR especially with LC-NMR.¹⁹⁹,²⁰⁴⁻²⁰⁶

<figure><img src="image_84.png" /><figcaption>Figure 8.4. The WET sequence. The pulses are on the top axis. They are selective pulses on the signal of the solvent. The bottom axis represents gradient pulses.</figcaption></figure>

WET relies on RF pulses that are about 20 ms. The overall rapid nature of the WET suppression-block makes it more suitable for flow than presaturation. It will be better to handle the refreshment of the solvent spins due to the flow.²⁰⁴ The selective pulses may be problematic as their selective excitation profile is limited and it may also suppress part of the signal in close vicinity of the solvent.
This can be a problem as it will prevent any successful fitting of peaks too close, for diffusion analysis purposes.

WET is being advised for flow and compatible with a large array of conditions. It is also automatically implemented in Bruker's spectrometers. This implementation includes a routine to calculate of the angles and that can also modify the pulses to have multiple signals suppression, for solvents with multiple peaks. The optimization is made on the last angle and the others are readjusted to follow the ratio of power to respect the sequence. It is therefore the most straightforward and simple sequence to use, for us. The only drawback is that one has to be careful and may sacrifice the peaks close to the suppressed peak, its efficiency while being sufficient is often less than excitation sculpting.

Figure 8.5, display the result of WET solvent suppression in the same sample as before (alcohol mixture), at the same flow rate (3 mL/min). The result shows that the peak of the solvent while still there, has decrease by approximately 140 folds.

WET

<figure><img src="image_85.png" /><figcaption>Figure 8.5, sample of 0.1M alcohol mixture. A) show no solvent suppression. b) WET, solvent suppression on water. The zoom level is chosen in both cases so that all the peaks appear whole within the frame. The approximate reduction of intensity for water is 140 folds.</figcaption></figure>

8.1.3. Spectral and diffusion resolution

To assess the possibility to monitor reactions with our methods, we have to choose a relevant and appropriate test reaction. While we have already described the key parameters of the experiment, we will summarize them here for the case of reaction monitoring.

Spectral resolution plays an important role, as the peaks should be well separated and if possible, not subject to effects such as widening from exchange, or to T₁-T₂ effect (possibly caused by metals in the medium).¹⁷ This in itself can be considered as a challenge, since some reactions use paramagnetic metals for catalysis, sometimes in transient states. Changes of viscosity in the medium, possibly due to polymerization for example or complexation, could also disturb the diffusion analysis.
It also happens that reactants and products might be close in terms of chemical structure and therefore also in chemical shift, thus causing some form of overlapping. This proximity may hinder the processing of the spectra. This is an important and hard-to-avoid fact. It underlines the needs for means to separate the overlapping peaks. Such mean includes multivariate processing as seen in part 6. Especially a multivariate method known as PARAFAC<sup>207</sup> which allow to retrieve concentration profiles, diffusion curve and pure component spectra untangled.

The differences between reactants and products will be a key point for reaction monitoring with DOSY. Reaction such as reduction will often have a small impact on the molecular weight, and if we have seen that this is not the relevant parameters (the molecular radius is) the two remain linked. Therefore adding 1 or 2 hydrogens to a possibly bigger molecule may not be sufficient to change its diffusion coefficient enough to virtual separate them. Overall, many reactions that add few atoms in a bigger molecule will not be well suited for DOSY monitoring. This still left a large array of reaction, as coupling reaction are common and those changes will be impactful on smaller molecule. If we take the example of drug design adding different block to a molecule to add different effects is a common practice that will often make the molecule bigger.<sup>208</sup>

## 8.2. The model reaction

In order to evaluate the methods that we have developed, we chose to monitor the double imination reaction described in Figure 8.6. This reaction was already monitored by flow NMR in the past,<sup>150</sup> and by diffusion NMR on a static (non-flowing) sample.

There are several interesting aspects of this reaction, especially to provide a test for our monitoring methods. The reaction occurs in two steps, each increasing the molecular weight noticeably, with an additional isobutyraldehyde moieties. This suggests that it should be possible to separate the compounds with DOSY. There are five molecules of different molecular weights to follow, the reactants, the intermediate, and the final product. This is displayed in Figure 8.6.
<figure><img src="image_86.png" /><figcaption>Figure 8.6 reaction scheme for the reaction of interest. Note that the isobutyraldehyde is present in twice the equivalents of the amine. The reaction creates water and the imine are actually in equilibrium with their dissociated forms.</figcaption></figure>

This reaction, while it was not primarily chosen for its relevance in synthetic chemistry, yields a Schiff base that can be of interest in biological and coordination chemistry.<sup>209-211</sup> It was chosen for its simplicity as the known mechanism and relative ease of manipulation make it safe for our apparatus. Indeed, the reaction involve little to no precipitation or risk of solid formation which is a very important point for the safety. It also does not involve ions or species that could become salt, even less so metallic species.

Interesting information on this reaction is available in the literature<sup>150,212</sup>. The kinetics depend strongly on the ratio of aldehyde/amine and can therefore happen quickly ~20 min<sup>150</sup> or very slowly ~47 h.<sup>212</sup> Due to its equilibrium nature, it is not straightforward when the reaction reach completion, yet it can be estimated that after ~4h a plateau is reached in our conditions. Working on a well-known reaction also helped to be able to confirm our observation by comparing with the literature.

The reaction also displays some challenges that make it a relevant example for online monitoring. Neither the reactant, the aldehyde, nor the product, an imine, are very stable. This means that once in solution they can be hard to extract, separate or characterize. The reaction is indeed in equilibrium with water and water can be the main reason why the reaction would not work. The fact that it reacts accordingly to our plan with a slowly rising water peak indicate that the apparatus is protected enough against ambient water. Those imines would be challenging to separate and isolate, indeed they are sensible to oxidation by air and water. Imine are often used as intermediaries, mean to react in-situ rather than be separated/isolated.<sup>213</sup>

Interestingly, the handling of the amine reactant was not easy. Indeed, it is a very potent pigment that get more coloured with time and oxidation by air.<sup>214</sup> This property while forcing us to
handle with extreme care, especially due to the toxicity of the compound, also showed us where leaks
or exchange with the exterior might happen. It challenged deeply our belief, and our methods for
cleaning and storing of the whole flowtube apparatus.

8.3. Methods

8.3.1. The pulse sequence

For reaction monitoring to happen we had to merge all the different strategies introduced from part 4 to part 7. The resulting pulse sequence is shown in figure 8.7. Note that the Δ/2 delay starts precisely with the first encoding gradient pulse and end precisely right before the second pair,⁵ while δ/2 is the length of the diffusion encoding pulse.

<figure><img src="image_87.png" /></figure>

figure 8.7 the reaction monitoring sequence. Ellipse-shaped pulse represent selective pulse for the WET that select the peak of acetonitrile. Black rectangles pulses are 90° while white ones are 180°.Rainbow ellipse are the diffusion encoding gradient pulses. On the CTP channel white rectangle represent spoiler pulses. note that they can be put on any axis. The wider ellipse inverted pulse on CTP channel are compensation pulse. Δ/2 length start exactly after or before the diffusion encoding pulse as illustrated.

The compatibility with flow is ensured by using the DSTE approach with diffusion encoding gradients along the X axis, an axis orthogonal to the flow direction. We have seen in part 6 that it allows to measure accurate diffusion coefficients while on flow up to 3 mL/min, which will be the flow rate here. The incremented diffusion encoding pulse are shown as rainbow-colored ellipses on the so-called diffusion channel (X-axis).

The improvement of the time resolution is achieved by the strategy described in part 6, which consists of using gradient pulses around the 180° RF pulses for coherence pathway selection. This allows to have accurate experiment in 1 scan, effectively replacing the phase cycling. This is true if the
concentration allows to have enough sensitivity in 1 scan. Those pulses are simultaneous to the
diffusion encoding pulse, and on another axis, here Y orthogonal to the flow.

In part 4 we have seen that to calibrate DOSY experiments one should do the experiment with 2 transients with maximum and minimum gradient. This is done to evaluate that the signal of all compound attenuates to ~5% of its original strength. It was not straightforward for the reaction. The first time we did the reaction (figure 8.6) it was done on the benchwork with monitoring done on thin layer chromatography. Once every product visible we sampled the medium put it into an NMR tube, added a bit of water and ACN to quench the reaction. The tube was submitted to the DOSY process and it was found that 0.1 s of Δ with 1.6ms of δ was sufficient for all species. Note that adding water was not the cleverest idea as it quickly degraded the species in the sample and it was not useable after 1 hour ahead of sampling.

8.3.2. Experimental

For all the reactions studied here, the flowtube apparatus was used as described in part 4.
Thermoregulation was off, as using it did not yield any difference at room temperature. In a flask closed
by a holed cap 25 mL of acetonitrile HPLC grade was added, 256 mg of solid p-phenylenediamine (white
slightly pink) was added with magnetic stirring. The solution was left until complete dissolution. The
flowtube apparatus was washed of isopropanol (the storage solvent) with acetonitrile and filled with
it. The capillaries linked to the apparatus were then inserted into the flask and the flow was settled at
3 mL/min for 10 min. Solvent suppression and the NMR sequence were checked and corrected if
necessary due to field drifting. An extensive shimming was made during 10 to 15 min using both the
vendor's routine and manual options. On flow while continuously stirring the isobutyraldehyde was
added (0.42 mL), with a Hamilton glass syringe. The cascade of NMR experiments was launched as the
user promptly went from the fumehood to the computer (~5 m apart). The reaction medium turns
slightly yellow up to red-brownish at the end of the reaction.

The NMR experiment consisted of a DSTE with 16 increments and 1 scan per increment. The duration of it was ~80 s. It was interleaved with 1D ¹H experiment using WET for diagnostic and to check the ongoing process that were ~20 s long. The repetition delay between experiment was 3 s. For 1D experiment the acquisition time was 0.43 s for DSTE it was 0.74 s.

In order to face and overcome the trouble of changing concentration (and linked intensity) the p-DOSY¹⁵⁹ strategy was used. It consists of taking the conventional linear ramp of gradient and permutate the gradients order in a random way. The same randomized list was used for all experiments.
## 8.4. Results and discussion

### 8.4.1. Monitoring's data and processing

The monitoring data for the reaction takes the form of ~140 DOSY data sets and the same number of 1D spectra. An example of DOSY display that corresponds to 120min of monitoring is shown in figure 7.8. In Figure 7.8a), all the peaks were integrated. In figure 8.8b), only the peaks that can be reliably integrated are shown. These peaks were considered in the following.

<figure><img src="image_88.png" /></figure>

Figure 8.8 the DOSY map for the monitoring. This represent a DOSY experiment done at the middle of the monitoring (~140min). A) The full map with every relevant peak integrated including water, the large peaks of mono-imine and amine N-H proton was not integrated (barely visible). The area 3.6-2.3 ppm has multiplet that have very low SNR and overlap. They were discarded for the final display b).

The excluded peaks include multiplets in the 3.6-2.3 ppm region. These give erroneous and inconsistent values of the diffusion coefficients. This can be explained by their high multiplicity and low SNR. The multiplicity also causes the peaks to spread facilitating the overlap making the processing of integration complex. Indeed, those give non-conclusive answer their diffusion coefficient seems to fluctuate, sometimes depending on the shim, sometimes in more mysterious way.

To process each experiment, a list of the integration regions is needed, with one integration region for each peak of interest. The problem is that the spectrum moves slightly throughout the reaction. The exact cause is not well known, it could be field drifting, temperature, chemical environment changes such as pH... This change is relatively slow, and this makes it possible to create integration regions that can be reused on average for 20 spectra (~40 min) before requiring recalibration. This reduces the time needed for processing and reduce the impact of the experimenter. The recalibration is very light and is in the order of 0.1 ppm or less. It is to be noted that usually it also depends on the shimming as better shimming give more intensity and SNR, and peaks with higher SNR are easier to integrate.

The DOSY display is useful as a virtual separation tool but is a bit less convenient when one wants to do monitoring. Indeed, overlapping a great number of DOSY map to show all the points is not
especially information rich. We decided to do another way of processing the data that use the DOSY
display but in another form. GNAT¹⁶¹ provide detailed non-graphic report of each DOSY map. In it are
found the value of each diffusion coefficient (at the chemical shift value) along with its associated
uncertainty. This can be extracted to get the core information and use it for monitoring.

Along this process that is specific to DOSY, we were interested to retrieve information that is
commonly retrieved in reaction monitoring. The least attenuated transient of a DOSY is very akin to a
normal 1D ¹H spectra. We decided to use those transients to integrate the peaks and have a monitoring
of the peaks area that give us information on the relative concentration. This was done by very
conventional manner by selecting the areas to integrate and processing all the least attenuated
transient. In this case the same integral regions were used for the entire set of spectra. This is because
no visual difference was observed when using integral regions that change from spectra to spectra, as
was done for DOSY. If the data were analysed quantitatively, then such recalibration may be needed.

The result of all those processing steps is displayed in figure 8.9, which can be considered the
final product of our effort to make online reaction monitoring by diffusion NMR
<figure><img src="image_89.png" /><figcaption>Figure 8.9 Monitoring of a di-imination reaction by flow DOSY NMR. a) Reaction scheme. b) Representative 1D spectrum from a DOSY transient, chosen here at ~250 min of the reaction's monitoring to display all species. c) Integration of the least attenuated spectra in the time series of DOSY data sets. d) Diffusion coefficient measured by DOSY throughout the reaction. The width of the curve represents the uncertainty for each site. The black line represents the reference values measured in a NMR sample tube in pure CAN, for reactant separately.</figcaption></figure>
8.4.2. Estimated diffusion coefficients

One can see at first glance in Figure 8.9d that the estimated diffusion coefficients are fluctuating over time, while no changes in size or shape are expected for the different molecules during the reaction. This can be explained by considering the concentration of the compounds as a function of time. Figure 8.9d) shows the fitting results at every time point in the reaction, but looking at Figure 8.9c) one can see for example that the di-imine is barely present before 50 min of reaction. The diffusion fitting curve thus yields non-sensical diffusion coefficients as there is in reality no peak to integrate and fit. The same is true for the mono-imine (red) that seems to give bad result in the first points and improves quickly after that. The amine shows the opposite behaviour. This is correlated to its concentration that drops fast as seen in figure 8.9c).

These phenomena are finally the consequence of something already known and observed in this work. Relatively high SNR is needed to get a good fit and reliable measurements of diffusion coefficients. This allows us to make some estimate about the SNR threshold needed to retrieve a good fit. We estimated that SNR should be above ~200 for the fit to work reliably. It is to be noted that the amine site gets quickly depleted therefore only the first dozens of points are usually useable with this condition. We decided to keep the amine site nonetheless as it seemed an interesting information, but note that it almost never validates the SNR condition. This SNR threshold also explains the exclusion of the multiplets that indeed never reach the condition, and also overlap.

To assess the reliability of the estimated diffusion coefficients, we compared them, for the reactants, to values measured on the compounds alone and at equilibrium. The products are not very stable and thus it is difficult to use conventionally accepted method to record their diffusion coefficients. The reactants, on the other hand, are reasonably stable, which allows to record diffusion NMR experiments in a straightforward manner, in an NMR tube using non-deuterated acetonitrile, using the DSTE pulse sequence. This yields the reference values shown by the black line in figure 8.9d). There is a difference of about 6 % for the amine and 2 % for aldehyde, on average the maximum reached difference is 12 %, if we only consider points with enough SNR (vide-supra) it falls at ~8 %.

The estimated diffusion coefficient stays relatively constant as expected throughout the whole experiment. The Mono-imine case is particularly interesting as one can see a slight increase of the error from 5 to 40 min, it can be explained by the great increase of concentration at the same time. Using non-permutated random gradient ramp, this increase of concentration would change the measured diffusion coefficient. Indeed, the increase of concentration creates an increase of intensity that perturbs the fitted attenuation. Using p-DOSY allows to have those variation in a random way rather than a systematic one, making it akin to noise-related error.¹⁵⁹ The mono-imine can be seen rising extremely fast up to 44% in between two point in its fastest segment, reaching a maximum
concentration around 92 min. The increase of concentration of the amine is in average ~30% during the first 40 min of monitoring. Despite this impressive rate, the error only slightly increases during the fastest growth segment. This is rather impressive as the error increase is visible but stay in very acceptable margin which show that the use of fast DOSY and shuffled gradient work well for monitoring.

### 8.4.3. Some challenges of reaction monitoring

Flow NMR and the use of non-deuterated solvent raises some challenges concerning the homogeneity and stability of the magnetic field. They were described in previous sections already, but their consequences are more of a concern in the case of reaction monitoring. When the reaction proceeds it cannot be stopped to improve field homogeneity.

Figure 8.9 presents one of the best results obtained for reaction monitoring. Figure 8.10 shows the result of a monitoring session during which several issues were met. It corresponds to the data published in Ref²¹⁵. One can see that the main difference is the lack of several points in the beginning. There is a point at 0 then 15 min, and the points in between are lost because of a severe loss of field homogeneity. This was found to be quite common across different attempts. Lineshapes become terrible a few minutes after the introduction of the aldehyde. We are not sure why this happens, and it could be a change of nature of the medium, viscosity or otherwise, maybe the presence of very transient solid formation. It is even hard to tell if it comes from the studied system or if it is something that is bound to happen from time to time because of the hardware or environment. As figure 7.9 demonstrate, such issues are not systematic.

One can also see especially when comparing figure 8.9c) and 8.10a) that the gaps between the points are not always at the same position or of the same length. Those gaps correspond to shimming events during which the flow is stopped and a shimming routine is performed. This routine is usually very fast (~30s) and barely visible. It is an automatic feature and it can be longer in the case when it did not reach its linewidth goal and should be prolonged, or if it completely fails and the operator has to shim manually. Those gaps are very visible albeit small in the figure 8.10 and barely visible in figure 8.9 except for a bigger one in the middle. This bigger gap is a shimming routine failure that demanded operator intervention and was longer. It just shows that despite the care taken in the NMR room and the prevention of exterior disruption, some differences are bound to happen.
<figure><img src="image_90.png" /><figcaption>Figure 8.10 another earlier occurrence of the monitoring. Everything experimentally wise was done the same way as figure 8.9 as well as analyze processing etc.</figcaption></figure>

The loss of those few points at the beginning is a real trouble. Indeed, one could argue that the whole point of the flowing apparatus is to have those first, very important points that can be keypoint in the monitoring. This effect is problematic but can be overcome by knowing the system well and doing multiple tries, it is also worth noting that even in the figure 8.10 the time resolution of the 3 firsts points is above the capability of conventional DSTE (0-15-17 min vs 20 min for a conventional DSTE experiment).

Another problem is the field drift. While it is usually negligible during one, it can be significant across a series of experiments. This can require recalibration of the solvent-suppression block. It is also the case that the solvent suppression become less effective from the beginning to the end of the experiment because the solvent signal has moved. This did not have enough effect on the baseline to corrupt the DOSY data mostly due to the double effect of solvent suppression and diffusion attenuation on the solvent, that make the solvent peak rather small anyway. It could be a problem for the integration of peak close to the solvent peak. In practice the need to adjust the frequency was only present beforehand and not during the monitoring.
### 8.4.4. Repeatability

The reaction monitoring experiment was repeated multiple times. After two initial runs to set up the parameters, five independent runs were performed. Out of these two were unsuccessful, giving three repetitions that can be analysed. The two failed experiment come from a problem of field homogeneity and a problem with the solvent suppression. The shimming was deemed ok for the experiment but was in fact bad and did not allow a good integration and fitting. A problem of solvent suppression as the WET strategy failed in the middle of the monitoring also made the processing difficult and not comparable to the already analysed points. The reason of this failing is unclear.

In order to assess the repeatability of the experiments, the average estimated diffusion coefficient was calculated for each compound, keeping only points that verify the SNR > ~200 conditions. The results are shown in Table 1 for the three reaction runs. One can see that the diffusion coefficient values are comparable even between different runs. The associated uncertainties are estimated in two different ways: (a) as the root mean square, over a reaction run, (b) as the standard deviation of the diffusion coefficient values over a reaction run of the uncertainties estimated from the fits.

The aldehyde has a surprisingly high uncertainty, probably coming from its hard to integrate nature and the fact that it stays for a rather long-time near the threshold level. One can see that the estimated uncertainty is, for three compounds, larger with method (b) then with method (a), which is consistent with the fact that the latter also accounts for variations during the reaction. The results for the amine are an exception, but the corresponding data set is in fact smaller, the SNR condition is verified only for about ~20 min for that compound. In those condition the apparatus instability, field shift and other inconveniences have no time to develop and thus give very repeatable experiment.
Table 8.1 Average estimated diffusion coefficient over different reaction runs, for the compounds involved in the di-imation reaction. The average is calculated, for each reaction run, over all of the data points for which SNR >~200. The associated uncertainty is calculated in two ways: (a) as the root mean square, over a reaction run, of the uncertainties estimated from the fits, (b) as the standard deviation of the diffusion coefficient values over a reaction run. The standard deviation for the average diffusion coefficients over the three reaction runs is also reported.

<table><thead><tr><th rowspan="3">Exp no°</th><th colspan="3">Amine (1 site)</th><th colspan="3">Aldehyde (2 site)</th><th colspan="3">Imine (4 sites)</th><th colspan="3">Dimine (3 sites)</th></tr><tr><th>D</th><th colspan="2">Uncertainty</th><th>D</th><th colspan="2">Uncertainty</th><th>D</th><th colspan="2">Uncertainty</th><th>D</th><th colspan="2">Uncertainty</th></tr><tr><th></th><th>(a)</th><th>(b)</th><th></th><th>(a)</th><th>(b)</th><th></th><th>(a)</th><th>(b)</th><th></th><th>(a)</th><th>(b)</th></tr></thead><tbody><tr><td>1</td><td>27.36</td><td>0.31</td><td>0.23</td><td>35.86</td><td>0.37</td><td>0.61</td><td>21.63</td><td>0.21</td><td>0.25</td><td>18.30</td><td>0.28</td><td>0.76</td></tr><tr><td>2</td><td>26.72</td><td>0.30</td><td>0.29</td><td>36.03</td><td>0.62</td><td>0.74</td><td>21.35</td><td>0.32</td><td>0.34</td><td>18.16</td><td>0.31</td><td>0.53</td></tr><tr><td>3</td><td>26.67</td><td>0.30</td><td>0.30</td><td>36.25</td><td>0.50</td><td>0.73</td><td>21.48</td><td>0.20</td><td>0.28</td><td>18.49</td><td>0.21</td><td>0.47</td></tr><tr><td>Standard deviation</td><td>0.38</td><td></td><td></td><td>0.19</td><td></td><td></td><td>0.15</td><td></td><td></td><td>0.16</td><td></td><td></td></tr></tbody></table>
Doing the experiment 3 times in a row allow us to appreciate the differences with the reference compound for the reagents. Interestingly despite different behaviour the maximum deviation is ~10 % and the average is around 4 % for all experiment, on reasonable condition (SNR > 200.). It shows that, the change of behaviour one can sees comparing figure 8.9 and 8.10 are little and the impact of the shimming and other possible bad influences average over the time of the experiment. Those influences may worsen in the case of a much shorter monitoring but do not make it impossible just challenging. It is very interesting to see that the deviations in between experiment are very close to the deviations seen inside the same experiment.

## 8.5. Conclusion and perspective

We have shown that DOSY NMR in continuous flow can be used for the monitoring of an organic chemical reaction. Besides some preliminary experiments to select the parameters of the diffusion NMR pulses sequences, the approach is not more complicated or time consuming than a typical NMR monitoring, provided that a triple-axis gradient probe is available.

The experimental errors that we have reported are consistent across all of this work. Indeed, the error is of ~4 % in average and rarely goes above ~8 %. These results are obtained when sensitivity is sufficient and there is little to no overlapping, which is not always easy to obtain. Nonetheless, a chemical reaction is often a good medium as concentration are often quite high.

We believe that this method can be extended to a broad range of application, and in particular more complex reactions, or reactions or which the diffusion information itself if of interest. The most obvious for us would be polymerization or reactions where mass/size should be monitored such as coupling reactions. Reaction like this can be prone to mishaps, the coupling happening in multiple site or multiple times thus creating mixture that could use virtual separation. The monitoring of such kind of reactions in an online way with diffusion NMR could be very useful, as is could allow to have a finer understanding but also to have a finer control of the medium and the outcome. Reaction of coupling and polymerization indeed often yield multiple species of various length. NMR could be the insight to finely tune the system by using retro-loops and automated optimization.<sup>129,216</sup>

It is interesting to note that other reactions such as complexation, supramolecular assemblies also could use this technic to separate the component of the mixture and link which part are indeed bounded together. The interest being the change of size and well as the possibility to identify the possible outcome using its size. This is an interesting thing in the field of designing/understanding catalysts, that are often metallic complexes of varying nature. The catalyst being by definition present in low concentration in actual catalytic reactions, and often invisible for NMR, reactions in which it may be monitored by NMR most often concern specific steps, sometimes modified for analytical purpose, in the catalytic reaction.<sup>81,106,217</sup>
Different complementary paths may be taken to further improve the method. The improvement
and change made to the flowing apparatus and flow quality could solve much problems.¹¹⁷,¹⁹³ One
other way could be to improve or change the analysis methods. In this work we focused much of our
attention on univariate methods but multivariate methods presented in part 5 could be interesting.
Especially we found that PARAFAC²⁰⁷,²¹⁸ is a very good tool for the chemist as it gives as output
concentration profiles, diffusion curves and the spectral component of each molecule. PARAFAC can
disentangle spectra and get rid of the overlap which would allow to study more system with less a
priori knowledge. It is also less user dependant and if one has enough calculation power it is faster. We
tried our hand on this but the results are not right yet. Such methods being very sensitive to
inhomogeneities and other problems they probably suffer from the harsh NMR conditions. Those
methods also seem to require rather high SNR, and the required SNR increases with the number of
compounds to solve. Nonetheless having this to work would mean a very simple method both for NMR
experiments and to fully analyse the data. This would open it to more systems that overlap and to user
that would need less NMR knowledge, ultimately the method could spread to a much wider audience
of chemists.
Conclusion

In the bibliographical sections of the manuscript, we have highlighted the principles and features of diffusion NMR, as well as its usefulness for mixture analysis. We have also illustrated the relevance of reaction monitoring by NMR, and the usefulness of flow NMR for that purpose. Since reaction monitoring can require speed, we also described the acceleration strategies that are available for diffusion NMR. All of these considerations pointed out to the relevance of having diffusion NMR methods available for reaction monitoring by online flow NMR.

A first requirement was to have a way to evaluate the “virtual separation” provided by DOSY NMR methods. To do so we studied the processing and analysis of diffusion NMR, and proposed quantitative metrics to compare different methods. We then used methods to accelerate the NMR experiment while avoiding any drawback from this acceleration. The use of another axis for pulsed gradients around the 180° radiofrequency pulses turned out to be an excellent way of accelerating the experiments, even for DSTE pulse sequences, where, it allowed to accelerate the experiment by a factor 16 with not significant drawback.

We then evaluated methods to compensate for flow effect in diffusion NMR, and thus obtained accurate experiments for a continuously flowing sample. The best results were obtained with the combination of DSTE and the use of an axis orthogonal to the flow direction. Flow effects are very well suppressed with this approach, even for pulsatile flow.

Using what we learned and merging the methods of flow-compensation and acceleration we obtained a powerful sequence for monitoring. We illustrated its capability with the monitoring of a model organic chemical reaction. We expect this methodology to be useful to a broad community of chemists, and hope that it will be used by non-NMR specialist to get relevant information on interesting and complex chemical reactions.
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# Appendix

## A-1 DOSY plot

The figure is labelled function of where its spectra are used. Such as S7-5 is linked to figure 7-5.

<figure><img src="image_91.png" /><figcaption>Figure S4.7 Corresponding DOSY spectra.</figcaption></figure>
<figure><img src="image_92.png" /></figure>

STE 1 scan orthogonal gradient

STE 8 scans phase cycling

<figure><img src="image_93.png" /></figure>

STE 1 scan no phase cycling

STE 1 scan parallel gradient

<figure><img src="image_94.png" /></figure>

STE 8 scan parallel gradient
+ phase cycling

<figure><img src="image_95.png" /></figure>

STE 8 scan parallel gradient

<figure><img src="image_96.png" /></figure>

STE 8 scans no phase cycling

<figure><img src="image_97.png" /></figure>

<figure><img src="image_98.png" /></figure>
<figure><img src="image_99.png" /><figcaption>Figure S6-4 DOSY spectra with their matching symbols in the figure.</figcaption></figure>
<figure><img src="image_100.png" /></figure>

<figure><img src="image_101.png" /></figure>

<figure><img src="image_102.png" /></figure>

<figure><img src="image_103.png" /></figure>

<figure><img src="image_104.png" /></figure>

2 mL/min

<figure><img src="image_105.png" /></figure>

<figure><img src="image_106.png" /></figure>
<figure><img src="image_107.png" /><figcaption>Figure S7-5, DOSY plot that are used to make figure 7.5.</figcaption></figure>

## Sequence

“AMstebposgcGradPhycy_v2”

;by achille Marchand CEISAM nantes 29/04/21 under direction by J.-N. Dumez

;STE diffusion with CTP gradient in non-diffusion axis (simultaneous)
;reasonable doubt for l2 is it working

#include <Avance.incl>

#include <Grad.incl>

#include <Delay.incl>

define list<gradient> diff=<Difframp>

"p2=p1*2"

"d24=0*abs(cnst1)*abs(cnst11)"

"GPX8=abs(cnst1*0.01)"

"d25=0u*abs(cnst2)*abs(cnst13)"

"GPY9=abs(cnst2*0.01)"

"d26=1u*abs(cnst3)"

"d27=1u*abs(l2)"

"d31=d30*2"

"DELTA1=d20-d30-10u-d16-p2-d30-10u-d16-p1-p19-d31-10u-d16-p1"

"p21=2*p20"

1 ze

2 d1
50u UNBLKGRAD

p19:gp7*-1

d16

d31 grad{ sin(cnst1*-1,l2) | sin(cnst2*-1,l2) | sin(0,l2) }

10u groff

d16

; excitation

p1 ph1

; diffusion encoding with xy ctp gradients

d30 grad{ sin(cnst1,l2) | sin(cnst2,l2) | sin(cnst3,l2)*diff }

10u groff

d16

p2 ph2

d30 grad{ sin(cnst1,l2) | sin(cnst2,l2) | sin(cnst3*-1,l2)*diff }

10u groff

d16

; storage
p1 ph3

p19:gp7

DELTA1

d31 grad{ sin(cnst11*-1,l2) | sin(cnst13*-1,l2) | sin(0,l2) }

10u groff

d16

; excitation

p1 ph4

; diffusion decoding with xy ctp gradients

d30 grad{ sin(cnst11,l2) | sin(cnst13,l2) | sin(cnst3,l2)*diff }

10u groff

d16

p2 ph2

d30 grad{sin(cnst11,l2)| sin(cnst13,l2) | sin(cnst3*-1,l2)*diff}

10u groff

d16

; acquisition

4u BLKGRAD
go=2 ph31

d1 mc #0 to 2 F1QF(calgrad (diff))

d24*0

d25*0

d26*0

d27*0

exit

ph1= 0

ph2= 0000 2222

ph3= 0000 0000 2222 2222

ph4= 0123

ph31=0321 0321 2103 2103

;p1 : f1 channel - 90 degree high power pulse

;p2 : f1 channel - 180 degree high power pulse

;p19: gradient pulse 2 (spoil gradient)

;d31: gradient pulse (little DELTA * 0.5)

;d1 : relaxation delay; 1-5 * T1

;d16: delay for gradient recovery 200μs
;d20: diffusion time (big DELTA)

;cnst1 value of x gradient constant

;cnst11 value X grad 2nd

;cnst13 value y grad 2nd

;cnst2 value of ygradient constant

;cnst3 value of z gradient incremented by diff 100

;cnst11 for 2nd 180 value of x gradient constant

;l2 number of point for sin grad

;NS : 8 * n

;DS : 4 * m

;td1: number of experiments

;FnMODE: QF

; use xf2 and DOSY processing

;use gradient ratio: gp 6* : gp 7

; 100 : -17.13

;*not included anymore

;for z-only gradients:
;gpz7: -17.13% (spoil)

;use gradient files:

;gpnam6: SINE.100

;gpnam7: SINE.100

;use AU-program dosy to calculate gradient-file Difframp

"AMdsteosgcGradZ_phycy"

;avance-version (08/01/16)

;2D sequence for diffusion measurement using double stimulated

; echo for convection compensation and LED

;using bipolar gradient pulses for diffusion

;using 3 spoil gradients

;A. Jerschow & N. Mueller, J. Magn. Reson. A 123, 222-225 (1996)

;A. Jerschow & N. Mueller, J. Magn. Reson. A 125, 372-375 (1997)

;$CLASS=HighRes

;$DIM=2D

;$TYPE=
;$SUBTYPE=

;$COMMENT=

#include <Avance.incl>

#include <Grad.incl>

#include <Delay.incl>

define list<gradient> diff=<Difframp>

"d24=0u*abs cnst1)*abs cnst11)*abs cnst12)*abs l2)*abs cnst13)*abs cnst14)"

"d12=20u"

"d13=4u"

"d17=2m"

"d31=2*d30"

"p2=p1*2"

"d3=p2"

"p7=2*p6"

"DELTA1=d20*0.5-d30-10u-d16-p2-d30-10u-d16-p1-p19-d16-d31-10u-d16-p19-d16-p1"

"DELTA3=d21-p19-d16-4u" ;led
;"WETWAIT=10m-d16"

"acqt0=-p1*2/3.1416"

1 ze

2 30m

d1

50u UNBLKGRAD

d12 pl0:f1

d13

(p11:sp7 ph15):f1

4u

p16:gp21

d17 pl0:f1

(p11:sp8 ph16):f1

4u

p16:gp22
d17 pl0:f1

(p11:sp9 ph16):f1

4u

p16:gp23

d17 pl0:f1

(p11:sp10 ph16):f1

4u

p16:gp24

d16

; compensation grad

p19:gp7*-1

d16

d31 grad{ sin(1*-1,l2) | sin(cnst2*-1,l2) | sin(0,l2) }

10u groff

d16 pl1:f1

; excitation

p1 ph1
d30 grad{ sin(cnst1,12) | sin(cnst2,12) | sin(cnst3,12)*diff}

10u groff

d16

p2 ph2

d30 grad{ sin(cnst1,12) | sin(cnst2,12) | sin(cnst3*-1,12)*diff}

10u groff

d16

;storage

p1 ph3

p19:gp7

d16

DELTA1

d31 grad{ sin(cnst16*-1,12) | sin(cnst13*-1,12) | sin(0,12)}

10u groff

d16

p19:gp8*-1

d16

;transplannation

p1 ph4

d30 grad{ sin(0,12) | sin(0,12) | sin(cnst3,12)*diff}
10u groff

d16

d30 grad{ sin(cnst16,l2) | sin(cnst13,l2) | sin(cnst3,l2)*diff}

10u groff

d16

p2 ph5

d30 grad{ sin(cnst16,l2) | sin(cnst13,l2) | sin(cnst3*-1,l2)*diff}

10u groff

d16

d30 grad{ sin(0,l2) | sin(0,l2) | sin(cnst3*-1,l2)*diff}

10u groff

d16

d3 ;symmetrization

p1 ph6

p19:gp8

d16

DELTA1

p19:gp9*0

d16
d31 grad{ sin(17*-1,l2) | sin(cnst14*-1,l2) | sin(0,l2)}

10u groff

d16

p1 ph3

d30 grad{ sin(cnst17,l2) | sin(cnst14,l2) | sin(cnst3,l2)*diff}

10u groff

d16

p2 ph8

d30 grad{ sin(cnst17,l2) | sin(cnst14,l2) | sin(cnst3*-1,l2)*diff}

10u groff

d16

4u BLKGRAD

go=2 ph31

30m mc #0 to 2 F1QF(calgrad(diff))

d24*0

exit
ph1= 0 0 1 1 2 2 3 3

ph2= 1 2 2 3 3 0 0 1

ph3= 0

ph4= 2 2 3 3

ph5= 3 3 0 0 3 3 0 0 0 0 1 1 0 0 1 1

ph6= 2

ph8= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

ph31=0 2 0 2 2 0 2 0 2 0 2 0 0 2 0 2

ph15= 0

ph16= 1

;pl1 : f1 channel - power level for pulse (default)

;cnst1 ctp grad x

;cnst2 ctp grad y

;cnst3 diff grad 100

;cnst11 ctp grad x 2ng 180

;cnst12 ctp grad x 3th 180
;cnst13 ctp grad y 2th 180

;cnst14 ctp grad y 3th 180

;l2 number point ctp and diff grad

;p1 : f1 channel - high power pulse

;p2 : f1 channel - 180 degree high power pulse

;p19: gradient pulse 2 (spoil gradient)

;d30: gradient pulse (little DELTA * 0.5)

;d1 : relaxation delay; 1-5 * T1

;d16: delay for gradient recovery

;d20: diffusion time (big DELTA)

;d21: eddy current delay (Te) [5 ms]

;NS: 16 * n

;DS: 4 * m

;td1: number of experiments

;FnMODE: QF

; use xf2 and DOSY processing

;use gradient ratio: gp 6 : gp 7 : gp 8 : gp 9

; 100 : -13.17 : -17.13 : -15.37
;for z-only gradients:

;gpz6: 100%

;gpz7: -13.17% (spoil)

;gpz8: -17.13% (spoil)

;gpz9: -15.37% (spoil)

;use gradient files:

;gpnam6: SINE.100

;gpnam7: SINE.100

;gpnam8: SINE.100

;gpnam9: SINE.100

;use AU-program dosy to calculate gradient ramp-file Difframp

;the gradients serve the following purpose:

; d30

; d30 first STE dephase bipolar pulse pair

; p19 spoiler

; d30
; d30 first STE rephase bipolar pulse pair

; d30 and second STE dephase bipolar pulse pair

; d30

; p19 spoiler

; d30

; d30 second STE rephase bipolar pulse pair

; p19 LED with spoiler

"AMwetstebposgcGradX_pycy_v2"

;by achille Marchand CEISAM nantes 29/04/21 under direction by J.-N. Dumez

;STE diffusion with CTP gradient in non-diffusion axis (simultaneous)

;reasonable doubt for l2 is it working

#include <Avance.incl>

#include <Grad.incl>

#include <Delay.incl>

define list<gradient> diff=<Difframp>

"d12=20u"

"d13=4u"

"d17=2m"

"p2=p1*2"

"d24=0*abs(cnst1)*abs(cnst11)"

"d25=0u*abs(cnst2)*abs(cnst13)"

"GPY9=abs(cnst2*0.01)"

"d26=1u*abs(cnst3)"
"d27=1u*abs(l2)"

"d31=d30*2"

"DELTA1=d20-d30-10u-d16-p2-d30-10u-d16-p1-p19-d31-10u-d16-p1"

"p21=2*p20"

1 ze

2 30m

d1

50u UNBLKGRAD

d12 pl0:f1

d13

(p11:sp7 ph15):f1

4u

p16:gp21

d17 pl0:f1

(p11:sp8 ph16):f1

4u

p16:gp22

d17 pl0:f1

(p11:sp9 ph16):f1

4u

p16:gp23

d17 pl0:f1

(p11:sp10 ph16):f1

4u

p16:gp24

d16 pl1:f1
p19:gp7*-1
d16
d31 grad{ sin(cnst1*-1,l2) | sin(cnst2*-1,l2) | sin(0,l2)}
10u groff
d16

; excitation
p1 ph1

diffusion encoding with xy ctp gradients
d30 grad{ sin(cnst1,l2) | sin(cnst2,l2) | sin(cnst3,l2)*diff}
10u groff
d16

p2 ph2
d30 grad{ sin(cnst1,l2) | sin(cnst2,l2) | sin(cnst3*-1,l2)*diff}
10u groff
d16

; storage
p1 ph3
p19:gp7
DELTA1
d31 grad{ sin(cnst11*-1,l2) | sin(cnst13*-1,l2) | sin(0,l2)}
10u groff
d16

; excitation
p1 ph4

diffusion decoding with xy ctp gradients
d30 grad{ sin(cnst11,l2) | sin(cnst13,l2) | sin(cnst3,l2)*diff}
10u groff
d16

p2 ph2

d30 grad{sin(cnst11,l2)| sin(cnst13,l2) | sin(cnst3*-1,l2)*diff }

10u groff

d16

; acquisition

4u BLKGRAD

go=2 ph31

30m mc #0 to 2 F1QF(calgrad (diff))

d24*0

d25*0

d26*0

d27*0

exit

ph1= 0

ph2= 0000 2222

ph3= 0000 0000 2222 2222

ph4= 0123

ph15= 0

ph16= 1

ph31=0321 0321 2103 2103

;p1 : f1 channel - 90 degree high power pulse

;p2 : f1 channel - 180 degree high power pulse

;p19: gradient pulse 2 (spoil gradient)

;d31: gradient pulse (little DELTA * 0.5)

;d1 : relaxation delay; 1-5 * T1

;d16: delay for gradient recovery 200u

;d20: diffusion time (big DELTA)
;cnst1 value of x gradient constant

;cnst11 value X grad 2nd

;cnst13 value y grad 2nd

;cnst2 value of ygradient constant

;cnst3 value of z gradient incremented by diff 100

;cnst11 for 2nd 180 value of x gradient constant

;l2 number of point for sin grad

;NS : 8 * n

;DS : 4 * m

;td1: number of experiments

;FnMODE: QF

; use xf2 and DOSY processing

;use gradient ratio: gp 6* : gp 7

; 100 : -17.13

;*not included anymore

;for z-only gradients:

;gpz7: -17.13% (spoil)

;use gradient files:

;gpnam6: SINE.100

;gpnam7: SINE.100

;use AU-program dosy to calculate gradient-file Difframp

"AMwetdsteosgcGradXPhycy"

;advance-version (08/01/16)

;2D sequence for diffusion measurement using double stimulated
; echo for convection compensation and LED

;using bipolar gradient pulses for diffusion

;using 3 spoil gradients

;A. Jerschow & N. Mueller, J. Magn. Reson. A 123, 222-225 (1996)

;A. Jerschow & N. Mueller, J. Magn. Reson. A 125, 372-375 (1997)

;

;$CLASS=HighRes

;$DIM=2D

;$TYPE=

;$SUBTYPE=

;$COMMENT=

#include <Avance.incl>

#include <Grad.incl>

#include <Delay.incl>

define list<gradient> diff=<Difframp>

"d16=20u"
"d24=0u*abs(cnst1)*abs(cnst11)*abs(cnst12)*abs(l2)*abs(cnst13)*abs(cnst14)*abs(cnst2)*abs(cnst3
)*abs(cnst15)*abs(cnst16)"

"d12=20u"

"d13=4u"

"d17=2m"

"d31=2*d30"

"p2=p1*2"

"d3=p2"

"p7=2*p6"

"DELTA1=d20*0.5-d30-10u-d16-p2-d30-10u-d16-p1-p19-d16-d31-10u-d16-p19-d16-p1"

"DELTA3=d21-p19-d16-4u";led

;"WETWAIT=10m-d16"

"acqt0=-p1*2/3.1416"

1 ze

2 30m

d1

50u UNBLKGRAD
d12 pl0:f1

d13

(p11:sp7 ph15):f1

4u

p16:gp21

d17 pl0:f1

(p11:sp8 ph16):f1

4u

p16:gp22

d17 pl0:f1

(p11:sp9 ph16):f1

4u

p16:gp23

d17 pl0:f1

(p11:sp10 ph16):f1

4u
p16:gp24

d16

; compensation grad

p19:gp7*-1

d16

d31 grad{ sin(0,12) | sin(cnst2*-1,12) | sin(cnst3*-1,12) }

10u groff

d16 pl1:f1

; excitation

p1 ph1

d30 grad{ sin(cnst1,12)*diff | sin(cnst2,12) | sin(cnst3,12) }

10u groff

d16

p2 ph2

d30 grad{ sin(cnst1*-1,12)*diff | sin(cnst2,12) | sin(cnst3,12) }

10u groff

d16

;storage
p1 ph3

p19:gp7

d16

DELTA1

d31 grad{ sin(0,12) | sin(cnst13*-1,12) | sin(cnst15*-1,12) }

10u groff

d16

p19:gp8*-1

d16

;transplannation

p1 ph4

d30 grad{ sin(cnst1,12)*diff | sin(0,12) | sin(0,12) }

10u groff

d16

d30 grad{ sin(cnst1,12)*diff | sin(cnst13,12) | sin(cnst15,12) }

10u groff

d16

p2 ph5

d30 grad{ sin(cnst1*-1,12)*diff | sin(cnst13,12) | sin(cnst15,12) }

10u groff
d16

d30 grad{ sin cnst1*-1,l2)*diff | sin(0,l2) | sin(0,l2) }

10u groff

d16

d3 ;symmetrization

p1 ph6

p19:gp8

d16

DELTA1

p19:gp9*0

d16

d31 grad{ sin(0*-1,l2) | sin cnst14*-1,l2) | sin cnst16*-1,l2) }

10u groff

d16

p1 ph3

d30 grad{ sin cnst1,l2)*diff | sin cnst14,l2) | sin cnst16,l2) }

10u groff

d16
p2 ph8

d30 grad{ sin(cnst1*-1,l2)*diff | sin(cnst14,l2) | sin(cnst16,l2)}

10u groff

d16

4u BLKGRAD

go=2 ph31

30m mc #0 to 2 F1QF(calgrad(diff))

d24*0

exit

ph1= 0 0 1 1 2 2 3 3

ph2= 1 2 2 3 3 0 0 1

ph3= 0

ph4= 2 2 3 3

ph5= 3 3 0 0 3 3 0 0 0 0 1 1 0 0 1 1

ph6= 2

ph8= 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2

ph31=0 2 0 2 2 0 2 0 2 0 2 0 0 2 0 2
ph15= 0

ph16= 1

;pl1 : f1 channel - power level for pulse (default)

;cnst1 diffusion x 100

;cnst2 ctp grad y

;cnst3 ctp grad z

;cnst11 ctp grad x 2ng 180

;cnst12 ctp grad x 3th 180

;cnst13 ctp grad y 2th 180

;cnst14 ctp grad y 3th 180

;cnst15 ctp grad z 2th

;cnst16 ctp grad z 3rd

;l2 number point ctp and diff grad

;p1 : f1 channel - high power pulse

;p2 : f1 channel - 180 degree high power pulse

;p19: gradient pulse 2 (spoil gradient)

;d30: gradient pulse (little DELTA * 0.5)

;d1 : relaxation delay; 1-5 * T1
;d16: delay for gradient recovery

;d20: diffusion time (big DELTA)

;d21: eddy current delay (Te) [5 ms]

;NS: 16 * n

;DS: 4 * m

;td1: number of experiments

;FnMODE: QF

; use xf2 and DOSY processing

;use gradient ratio: gp 6 : gp 7 : gp 8 : gp 9

; 100 : -13.17 : -17.13 : -15.37

;for z-only gradients:

;gpz6: 100%

;gpz7: -13.17% (spoil)

;gpz8: -17.13% (spoil)

;gpz9: -15.37% (spoil)

;use gradient files:

;gpnam6: SINE.100

;gpnam7: SINE.100
;gpnam8: SINE.100

;gpnam9: SINE.100

;use AU-program dosy to calculate gradient ramp-file Difframp

;the gradients serve the following purpose:

; d30

; d30 first STE dephase bipolar pulse pair

; p19 spoiler

; d30

; d30 first STE rephase bipolar pulse pair

; d30 and second STE dephase bipolar pulse pair

; d30

; p19 spoiler

; d30

; d30 second STE rephase bipolar pulse pair

; p19 LED with spoiler
**Titre :** développement de méthodes RMN diffusionnel rapide et compatible avec le flux pour le suivi de réaction en ligne

**Mots clés :** RMN, suivi de réaction, flux, réaction en ligne, DOSY

**Résumé :** La RMN est un outil d'analyse puissant qui a plusieurs usages. Parmi ces usages nous pouvons citer l'analyse de composé organique, leur structure et diverse propriété associé et le suivi de réaction. Nous nous intéressons particulièrement à une méthode particulière de la RMN, la RMN DOSY (Diffusion Ordered SpectroscopY). Cette méthode s'intéresse particulièrement à la diffusion des molécules et utilise ces propriétés de diffusion afin de crée un spectre qui permet la séparation virtuelle des composant. Cette séparation est très utile afin d'identifié les composés dans un mélange potentiellement complexe et donne quelques informations possiblement utiles lié à la taille des composés. Nous aimerions coupler cette expérience RMN avec la possibilité de faire du suive de réaction en flux. Le suivis de réaction par NMR existe sous diverse forme mais souvent l'échantillonnage du milieu de réaction pose problème.

Il semblerait qu'utilisé le flux afin de permettre cet échantillonnage serait une manière appropriée et peu dérangeante pour le système étudié. Nous nous proposons ici d'étudier l'impact du flux sur la RMN diffusionnel. De voir comment en compensé ces effets en utilisant un axe d'encodage de la diffusion orthogonal à la direction du flux et une séquence appelée DSTE. Puis nous nous intéressons à des méthodes pour accélérer l'expérience RMN afin de pouvoir analyser un grand nombre de réactions possiblement courte. Pour ce faire nous utilisons des pulse de gradients qui permettent une sélection de cohérence alternative en un seul scan, accélérant ainsi le processus par un facteur 16. Finalement en fusionnant ces deux approches nous démontrons la possibilité de faire du monitoring de réaction en utilisant la RMN DOSY en flux.

**Title :** Development of fast and flow-compatible NMR diffusion methods for online reaction monitoring.

**Keywords :** NMR, Reaction monitoring, flow, Online reaction, DOSY

**Abstract :** NMR is a powerful analysis tool with multiple usages. Among those uses, one can cite the analysis of organic compound and molecules, especially their structure and other properties, there is also the reaction monitoring. We are interested in a special kind of NMR method called DOSY (Diffusion Ordered SpectroscopY). This method is focused on molecular diffusion and use those properties to create display. Those display are often referred as a virtual separation method. This separation is then useful to identify compounds within a complex mixture and give some hint linked to their molecular size. We would like to use this with the possibility to do reaction monitoring by NMR. We would like to use this with the possibility to do reaction monitoring by NMR.

NMR reaction monitoring exist on various form, we here ask the question of the medium sampling. It appears to us that using flow as a sampling method for the reacting medium would be a rightful and non-disruptive way to do so. We herein study the impact of flow on diffusion NMR as well as compensating the flow effects thanks to a sequence called DSTE. We are interested in methods to accelerate the diffusion NMR experiment. In order to have the possibilities of monitoring a large array of reaction, some potentially fast. To do so we use pulsed gradient for coherence selection which allow to do the NMR experiment in one scan instead of 16. Finally, by merging the two approach we show the possibility to do reaction monitoring using such DOSY NMR for online monitoring.