Category Archives: knowledge bits

Why don’t some protons have signals on 2D spectra?

This small molecule has good signals on 1H-15N HMBC:

However, upon incorporation into a polymer, the region expected to show correlation peaks is completely noise:


If you look at the 1D proton signals, plotted on top of the 2D spectra, the aromatic protons in the small molecule have sharp peaks, while those in the polymer have much broader peaks. The broadening is due to short T2. A simple though not the most rigorous way to estimate T2 is T2 = 1/(pi*Delta), while Delta is proton peak width in Hz.

Some 2D pulse sequences keep magnetizations on the xy plane for a long time (a few ms to > 100 ms). For protons with short T2, unfortunately, their signals cannot survive these pulse sequences. HMBC has the harshest requirements for T2.

While the no-show on 2D spectra is a bit disappointing, a short T2 does tell you important information about the dynamics of your molecules – it often indicates that they interact poorly with the solvent and are aggregating.

Correct Baseline for Pseudo-2D Data

Baseline correction is essential for T1, T2 and diffusion experiments as our target peaks are often broad and short, so any baseline imperfection can cause large errors.

Baseline correction is done by computer fitting of the baseline by a fifth order polynomial: y = A + Bx + Cx2 + Dx3 +Ex4 + Fx5. Since the baseline shapes cannot always be perfectly fit by such a polynomial and sometimes some peaks are so broad that the computer is having a hard time decide whether it is a peak or part of the baseline, we need to be aware of the limitations of the fitting algorithm and tweak strategy if the correction result is not good.

To check if your slices have good baselines, go to the Multiple Display mode. Make the peaks very tall so you can see the baseline well. When you do Scan Rows, zero intensity of each spectrum is at the middle line of the window. This means that a good baseline would not shift downward or upward if you adjust peak heights (remember: ten times zero is zero!). If, when you left click to make the peaks taller, the baseline sinks or rises, the baseline is not good and needs correction. Note that the vertical scale is erroneous – zero intensity of a slice is at the middle line of the window, while the 0 as indicated by the scale is at the bottom of the window.

While in the Multiple Display mode, you need to decide two factors for baseline correction: (1) a chemical shift range of spectrum in which you want baseline correction. Since it is often difficult to perfectly correct baseline across the entire spectrum, only correcting a smaller range often produces better result. For this, you will need to properly choose the left and right limit of the range that you want the baseline correction be done. Choose the limits such that tails of peaks do not extend there, i.e., there is only baseline or noise near there. In the figure below, I chose the limits to be 4.5 and 0 ppm. Write your choices down for the next step. (2) shape of the polynomial. I often find that lower orders of polynomial (linear, which means I only use the shape A + Bx; or quadratic, which means I only use the shape A + Bx + Cx2) do a better job than the ones that use all five orders, which often overdo the job.

Exit Multiple Display mode.

Click the tab ProcBars (on top row of the spectrum window). In Baseline Correction category, set ABSG to 1. This means that you allow the computer to fit the baseline with only a linear function (A + Bx), not the entire five orders of polynomial. I found this usually produces good result. Sometimes 2 is better. Experiment with it yourself. Set ABSF1 and ABSF2 to the left and right limits of your choice.

Click the Spectrum tab. On the command line, type abs2. This will perform an automatic baseline correction in the range of spectrum that you chose. Go back to Multiple Display and check the slices. It should look like the figure below. Note that the baseline of the spectrum between 4.5 and 0 ppm has been corrected. There are step changes at 4.5 and 0 ppm, which is because the spectrum within the window has been corrected for baseline and that outside the window still has very negative baseline. This is normal.


If you don’t like the correction result, just type xf2 again. This will erase all the baseline corrections that you have done. Start fresh from here.

The Multiple Display Function

The Multiple Display module can be accessed by clicking the Topspin icon that shows two spectra on top of each other. This module has two very useful functions: 1. compare a number of different 1D spectra; and 2. view each 1D slice of a 2D or pseudo-2D spectrum.

To compare two 1D spectra, let’s say spectrum A and B, (1) load A onto the display; (2) click Multiple Display button; (3) find B in the browser, and drag it into the Multiple Display window. See the picture below. You can highlight one of the spectrum in the lower left window (the one below the Browser), then use the buttons to move or rescale it.


To view each slice of a 2D or pseudo-2D spectrum, first go into Multiple Display mode, then click the Scan Rows button (the highlighted button in the figure below). Then hover the mouse above the contour lines and you will see the slice. The first slice is at the very bottom while the last slice is at the top. You can use left click to make the peaks bigger, or click the mouse wheel to make them smaller. Making the peaks taller will help you better see if phase correction and/or baseline correction is needed.


The picture below shows one slice (slice #2, as the info box on the upper left corner shows “Index = 2”) of a pseudo-2D file.


Phase Correction for Pseudo-2D data

A pseudo-2D file is very similar to a 2D file, with the only difference being that the former only does Fourier Transformation in the horizontal dimension (by command xf2) while the latter needs FT in both dimensions (by command xfb). The data in these files are arranged in a matrix, with rows and columns. In pseudo-2D data, we only do phase correction and baseline correction on rows.

You can phase correct a pseudo-2D file by clicking the menu Processing -> Phase Correction, or by clicking the icon with a little distorted peak picture. In the new window, you need to select 2-3 slices as “representatives” of all the slices. Right click on the bottom-most slice, which is the first slice (you can tell that by the message “row: … Index = 1” in the info window on the top left corner), select Add. You will see a red circle being marked for the slice. Move mouse to one of the middle slices, repeat the Add operation. Repeat again for the last slice. Then click the button “R” (which means phase correcting rows), and you will see a window showing the three slices that you picked, like this:


Now you can phase correct the pseudo-2D file just like the way you correct 1D files. Drag the button 0 and 1 up and down so that all the peaks on all the slices have positive intensity and the baseline are not distorted. Click the Save and Return button. Then click Return, to go back to the main menu.

Note that for Inversion Recovery experiments, the first few rows are negative.

Then use the Multiple Display function to visually make sure each slice indeed has good phase. You are done.


13C Integration Can Be Very Helpful If Used Wisely

Although 13C spectra are commonly known as not being quantitative, peak integrations can be helpful in many situations. All protonated carbons have similar integration values, as you can see in this example:


Figure 1 is aromatic area, in which all protonated carbons have integration values of ca. 1.0. All unprotonated carbons have values of 0.4-0.5. The tallest peak is solvent.


Figure 2 is aliphatic area, in which each carbon (all are protonated) has an area of 1.1, slightly bigger than the protonated aromatic ones, but quite close.

Integrations can also help to solve puzzles of missing peaks. In this molecule, seven aromatic carbon peaks are expected, but only six show up on Figure 1. Why?


Let’s look at the 134.6ppm peak closely.  Integration of this is 1.4 – this means that this is not just one protonated carbon (area = 1), but contains two overlapping peaks – one protonated and one unprotonated, which would have a total area of ca. 1.4. This solves the mystery of the missing carbon.


Processing T1/T2, kinetics, and diffusion data using Dynamics Center

Topspin’s new feature Dynamics Center does everything the old T1/T2 module does, and with much more capability and much less buggy issues. It also processes diffusion data better than the dosy2d command.

To set up T1/T2/kinetics experiments, please visit this entry. An example of a kinetics experiment can be found here.

Click here for instructions on how to set up diffusion experiments.

The data from these experiments are in pseudo-2D format. Each pseudo-2D file consists of many 1D spectra, each of which is called a slice. To process these data, follow these steps:

First, you need to properly phase correct your spectra. When done, examine each slice of the pseudo-2D file in Multiple Display mode. Scan through each slice to see if phase correction is good.

Next, you need to perform baseline correction. This is especially imperative if you are tracking some small peaks when there are other much larger peaks present on the spectra, in which imperfect baseline will severely skew your results.

Then you can launch the Dynamics Center in Analysis -> T1/T2 module -> Dynamics Center. Or you can type dync at the command line. It opens up a new window “Dynamics Center”:

Click the option that you need (T1; T2; diffusion; etc). Then click each step within the option: Sample -> Data -> …  For the Data step, you will need to look for the “2rr” file that has been generated by your experiment, as shown in the above figure (unfortunately there are many layers of folders that you will have to dig through). The 2rr file stores the pseudo-2D data, which contains a number of 1D spectra.

Using peak areas is often more desired than using peak intensities, especially your target peaks are broad and ugly (which is actually often the case for interesting objects of relaxation and diffusion study!). In the Data step, after finding the 2rr file in the Spectra tab, click the Integrals tab, select “Use peak areas (user defined) integrals”.


After clicking OK, a spectrum will be displayed. You can define your interested peaks and peak integration limits here. There might be a number of peaks that the computer has picked for you that you don’t really care about. Move cursor near one of the peaks, right click, select Delete in a region. Then drag a box around all the computer-picked peaks that you want to get rid of. This will delete these selections.


Next, select the peaks that you are interested in. move the cursor near the peak, right click, then select Add peak integration area. A black bar will appear on the bottom of the peak (see the figure below). You can drag the bar around or resize it.  Repeat for all the peaks you are interested in. You could also right click near a peak and select “Resize All” option to change the width of the integral for all the peaks.


In Analysis, you can define the function that you want to fit, and various fitting parameters. For T1 experiment by Inversion Recovery, pick the following fitting function:


In View, you can define various options for display. Then you should get a window like this:


You can get a decay curve for each peak you pick (lower left window; move your mouse to other defined peaks in the upper left window to see decay curves for other peaks in the lower left window), and a 2D spectrum (upper right window) with the vertical dimension displaying the dynamics information (T1, T2, diffusion coefficients, etc). For diffusion data, the upper right figure would be a DOSY spectrum (I think the Dynamics Center does a better job than the “dosy2d” command).

The Report function summarizes all the fitting results. You should pay particular attention to the “error” column, which is the standard deviation of your fitting. Usually, relative standard deviation should be < 5% to indicate a high-quality fit. High errors usually indicate either poor data processing (phase correction; baseline correction) or multiple dynamic components existing in your sample.

The Export function can export the fitting results to an Excel file.

The Help menu has a manual in which you can find the information on how to navigate this relatively easy software package.

Rotors for Solid-State NMR

Users need to order a rotor (container for your sample) from Bruker online store. The part number is B200147 (4 mm rotor with 3 Kel-F caps). It is kind of expensive but it can be used many times.

The rotor spins at very high speed (usually >=4khz) during experiments. The acceleration can reach several thousand g. No liquid is allowed in the rotor as it will be squeezed out during spinning. This would contaminate the probe and cause arcing, and make the spinning difficult. A poorly spinning rotor could explode inside the probe and incur thousands of dollars of repairs.




Running NMR with non-deuterated solvents


It is possible to run NMR experiments with non-deuterated solvents. It will have to be run in an unlocked state. Follow these steps:

  1. rsh
  2. In bsmsdisp window, go to LOCK tab, if the LOCK button is either red or green, click it so that it is white.
  3. Click the SWEEP button so that it is white.
  4. rga
  5. (optional) shim on the FID. Type gs (similar to zg but without accumulating data, used for real time adjustment of various parameters), then adjust z and z2 in bsmsdisp window  to make the FID as long and thick as possible.
  6. zg. If step 5 is not done, the spectrum collected will be of lower resolution as shim is not optimized.

Using Graphic Output on Topspin

Graphic outputs are advantageous over printouts in many respects. They are easier to store and file.  They have higher resolution and are easier to incorporate into papers or reports.

There are two ways to generate graphic outputs in Topspin:

1. In xwp, choose Print and select Print to File.  It will generate a .ps file which is of high resolution and can be easily processed in standard graphic software such as Adobe Illustrator.

2. In main menu, click File and select Export.  you will need to add an extension name to specify the graphic format.  Popular choices are .png and .jpg.  The resolution of these files is not as high as the .ps file generated in xwp.

These files are saved in your home folder (/home/…) rather than the Bruker data file folder (/opt/topspin…).  You will need to delete those files in you home folder frequently since they reside on a small disc partition, which gets full quickly.

Converting signal area ratio to molar and weight ratio


Assuming you are dealing with two peaks, each from a different molecule (A and B), and you want to figure out their molar ratio and weight ratio.


First, measure the signal area of the two peaks, area(A) and area(B).


Second, count number of protons (or other nuclei in question) contributing to the peak, N(A) and N(B).


Third, find out the molecular weight of the molecules, MW(A) and MW(B).


The molar ratio is: 

mol(A)/mol(B) = [area(A)/N(A)]/[area(B)/N(B)]



The weight ratio is:

wt(A)/wt(B) = [mol(A) * mw(A)]/[mol(B) * mw(B)]


You can also calculate the concentration of one sample using another sample as a reference with known structure and concentration.  First, run NMR for the two samples.  They will have to be run with the same NMR techniques, same parameters, and with the same rg (receiver gain).  Second, integrate the peaks in the reference sample.  Third, integrate the peaks in the sample in which you want to find the concentration, then right click on the integration and select “Use last scale for calibration”.  Then you will be able to find the area ratio between the two samples.  This ratio, normalized by number of scans, can be used to further determine mole ratio, weight ratio, or concentration ratio.

Same principles can be applied when calculating moles and weights of interested molecules in solid-state NMR. For two samples A and B, the weight ratio for the molecules contributing to the peaks of interest is:

wt(A)/wt(B) ={ [area(A)*mw(A)]/[N(A)*NS(A)] } / { [area(B)*mw(B)]/[(N(B)*NS(B)] }