1H NMR coupling patterns: DT vs. TD

Proton-proton couplings arise from influences on the proton’s magnetic field from the magnetic field of neighbouring protons. Being capable to recognize among the vast array of coupling patterns can facilitate the time spent on interpreting a 1H NMR.

The diagram below illustrates two coupling patterns: a triplet of doublets (td) and a doublet of triplets (dt). The J coupling constants for both multiplets are measured at 3.2 and 8.3 Hz. The difference is the left multiplet (td) exhibits two couplings of 8.3 Hz and one of 3.2 Hz and the right multiplet (dt) exhibits one coupling of 8.3 Hz and two of 3.2 Hz.

1H NMR coupling patterns: DT vs. TD

Proton-proton couplings arise from influences on the proton’s magnetic field from the magnetic field of neighbouring protons. Being capable to recognize among the vast array of coupling patterns can facilitate the time spent on interpreting a 1H NMR.

The diagram below illustrates two coupling patterns: a triplet of doublets (td) and a doublet of triplets (dt). The J coupling constants for both multiplets are measured at 3.2 and 8.3 Hz. The difference is the left multiplet (td) exhibits two couplings of 8.3 Hz and one of 3.2 Hz and the right multiplet (dt) exhibits one coupling of 8.3 Hz and two of 3.2 Hz.

A first pass at detecting an impurity signal in a 1H NMR

I got some great comments from Tony and Gary on the “Dealing with a Mixture…” post that I decided to do a follow-up post. http://acdlabs.typepad.com/elucidation/2008/02/dealing-with-a.html

In my lab days, I would routinely workup a newly acquired 1H NMR for an unknown sample prior to acquiring a 2D NMR dataset. One of my goals was to examine the integrals from the 1H NMR spectrum. Based on the integral ratios, I could quickly determine, although not with complete certainty, which signals did not belong to the compound of interest.

From the 1H NMR shown below, making a general assumption that the doublet at 0.83 ppm belongs to a single CH3 group, the multiplets at 1.06 and 1.17 ppm can be classified as not belonging to the compound of interest.

A first pass at detecting an impurity signal in a 1H NMR

I got some great comments from Tony and Gary on the “Dealing with a Mixture…” post that I decided to do a follow-up post. http://acdlabs.typepad.com/elucidation/2008/02/dealing-with-a.html

In my lab days, I would routinely workup a newly acquired 1H NMR for an unknown sample prior to acquiring a 2D NMR dataset. One of my goals was to examine the integrals from the 1H NMR spectrum. Based on the integral ratios, I could quickly determine, although not with complete certainty, which signals did not belong to the compound of interest.

From the 1H NMR shown below, making a general assumption that the doublet at 0.83 ppm belongs to a single CH3 group, the multiplets at 1.06 and 1.17 ppm can be classified as not belonging to the compound of interest.

Why collect NMR or MS^n data?

As would-be elucidators dive into an elucidation for an organic unknown, narrowing down a single molecular formula (MF) becomes vital in simplifying the elucidation. However, one hurdle quickly leads into another. As the total atom count adds up, the number of possible isomers increases just as much (See Table and Graph below).

I can’t imagine drawing by hand every possible isomer for C12H26 at 355. And even if I did, how would I determine the candidate structure? The only choice is to acquire additional data to help eliminate the poor candidates. In my experience, the best choices are 1D and 2D NMR, MSⁿ, and/or single-crystal XRD data.

MF                         Number of Isomers
CH4                                   1
C2H6                                 1
C3H8                                 1
C4H10                               2
C5H12                               3
C6H14                               5
C7H16                               9
C8H18                              18
C9H20                             35
C10H22                            75
C11H24                            159
C12H26                            355
C15H32                           4,347
C20H42                        366,319
C30H62               4,111,846,763
C40H82       62,491,178,805,831
C10H17Br2ClO2        50,502,293
C15H22O2          38,136,211,624
C15H20O1          37,568,150,635
C12H12O3          68,930,547,646

Why collect NMR or MS^n data?

As would-be elucidators dive into an elucidation for an organic unknown, narrowing down a single molecular formula (MF) becomes vital in simplifying the elucidation. However, one hurdle quickly leads into another. As the total atom count adds up, the number of possible isomers increases just as much (See Table and Graph below).

I can’t imagine drawing by hand every possible isomer for C12H26 at 355. And even if I did, how would I determine the candidate structure? The only choice is to acquire additional data to help eliminate the poor candidates. In my experience, the best choices are 1D and 2D NMR, MSⁿ, and/or single-crystal XRD data.

MF                         Number of Isomers
CH4                                   1
C2H6                                 1
C3H8                                 1
C4H10                               2
C5H12                               3
C6H14                               5
C7H16                               9
C8H18                              18
C9H20                             35
C10H22                            75
C11H24                            159
C12H26                            355
C15H32                           4,347
C20H42                        366,319
C30H62               4,111,846,763
C40H82       62,491,178,805,831
C10H17Br2ClO2        50,502,293
C15H22O2          38,136,211,624
C15H20O1          37,568,150,635
C12H12O3          68,930,547,646

Dealing with a Mixture in a 13C NMR spectrum

One of the hardest parts of interpreting an NMR dataset is separating peaks from a mixture of compounds. To illustrate this point, below are the aromatic regions of two 75 MHz 13C NMR run on a mixture of methylbenzene and ethylbenzene in CDCl3.

If the mixture exists at an approximate 1:1 ratio (top figure), then deciding what peak belongs to which compound becomes very difficult with the current 1D NMR spectrum. However, if the ratio of the compounds is different enough, e.g. something like a 1:2 ratio (bottom figure), the peak height provides a key to differentiating the peaks. That is, the smaller peaks belong to the CHs from the methylbenzene.

TIP: As a first pass at a 1D NMR spectrum, check for any relative differences in areas or heights of the peaks.

Dealing with a Mixture in a 13C NMR spectrum

One of the hardest parts of interpreting an NMR dataset is separating peaks from a mixture of compounds. To illustrate this point, below are the aromatic regions of two 75 MHz 13C NMR run on a mixture of methylbenzene and ethylbenzene in CDCl3.

If the mixture exists at an approximate 1:1 ratio (top figure), then deciding what peak belongs to which compound becomes very difficult with the current 1D NMR spectrum. However, if the ratio of the compounds is different enough, e.g. something like a 1:2 ratio (bottom figure), the peak height provides a key to differentiating the peaks. That is, the smaller peaks belong to the CHs from the methylbenzene.

TIP: As a first pass at a 1D NMR spectrum, check for any relative differences in areas or heights of the peaks.

The intricacies of solving for an unknown structure using solution NMR data

Let me begin by stating my experiences with elucidations on organic molecules. I have led over 60 training sessions attended by chemists and Spectroscopists from Academia to industry around the world. Some more successful than others—this came down to expertise of the attendee. I have also conversed with dozens of chemists/Spectroscopists, experts and non-experts, at conferences, onsite visits and through email on this amazing subject.

Note: Some of the people I have met are listed at the following website. http://www.acdlabs.com/feedback/chaleluc.html The list is longer but I am not up to pestering the chemists and asking for their permission to list the institution where they work.

By the way, I am not going into how I got into elucidation while in graduate school, especially helping all those poor synthetic chemists solve their unknowns. :)

How about this for pressure? Imagine standing before an audience and asked to elucidate a structure from NMR data especially since you have never seen the data before. Well I have done that and on at least 10 occasions that I can remember. I am not trying to toot my horn but I want to express that my experience in this matter is applicable.

Taking a step back, the one thing all my training encounters have in common is success is judged by how much practice and time one denotes to this task. Just like anything new, practice through repetition makes things better and easier to handle. It is not that easy and not every chemist/Spectroscopist realizes the complexity behind teaching this process.

I am going to try to list all the possible nuances an NMR elucidator must take note of for any unknown whether they are aware of it or not. I want to talk about MS but I also want to keep the blog short. Maybe in the future another blog will follow on this subject.

1. The toughest elucidations and the ones I do not like doing are samples that have mixtures, salts, polymers or rotamers. Trying to pick out what belongs to what compound is a big headache especially when the compounds exist at an approximately equal ratio to each other. A big ouch.

2. Size of the structure can complicate matters. I have heard peptides and protein elucidation are tough. I managed once to do an elucidation of a peptide (~1200 Da), however, I knew beforehand what the end product so I would not classify this as a true unknown elucidation.

3. Knowing when to use what combination of NMR experiments is crucial. There are hundreds of choices for NMR experiments and some are more useful than others are. It is so easy to waste your time and instrument time. Experience and versatility count here.

4. Learning how to interpret the NMR data. I find the bulk of any elucidation falls under this classification. There are so many points to cover here so I will try to list as many as I can think of.
a. The tough stuff comes from differentiating between true signals and artifacts. Experience really counts here.
b. Dealing with overlapping 2D NMR correlations in a crowded region.
c. Deciding whether a correlation in an HMBC experiment is showing a 2J or 3J separation.
d. Missing signals due to symmetry, broad peaks, low S/N or paramagnetic metals present.
e. Few 2D correlations from a structure with a high RDBE (Ring and Double Bond Equivalence or element of unsaturation) or containing few hydrogens. Lots of possible candidate structures here.

5. Narrowing down to a single Molecular Formula. I find combining NMR with MS data very applicable here.

6. Deciding the carbon multiplicity (C, CH, CH2, CH3) should be easy to do. Knowing the MF could be helpful here and vice versa. Having this information lets you know how many exchangeable atoms are present.

7. Searching across a library/database of chemical shifts and coupling constants like C-F. Having information on the starting material or derivatives can speed things up. Absolutely an easy thing to do has the potential to save lots of time.

8. Deciding carbon bond order (hybridization) – should I consider sp carbons? I rarely run into sp carbons but it is always a thought in the back of my mind.

9. I like to ask myself these questions when dealing with heteroatoms. Do I have bonds between heteroatoms or bonds between heteroatoms of the same type? Do I have a pentavalent nitrogen? I find IR data to be helpful here. If I have a salt, which atom(s) is charged?

10. Exclude (or bias) possibilities based on NMR shifts. If I have a carbon at 200 ppm, then I immediately jump to a C=O group whereas a carbon at 120 ppm I exclude C=O as a possibility. Biasing the data can be a bad thing sometimes.

11. Apply an internal filter. I do this without realizing I’m doing this. For example, if I see a list of 6 aromatic carbons, I jump to a benzene ring. On an initial pass, I commonly exclude 3 and 4 membered rings and even the higher up rings such as 8-10 membered rings as I rarely run into these. I also try not to draw fragments and structures that do not have crossing (concatenation) bonds. Knowing whether the unknown is natural or not can help sometimes.

12. I like to draw out possible fragments and candidate structures especially since it helps to get a visual on things. The next step is to rank the list and see what can be eliminated. Logics and reasoning do the trick here.

13. With a candidate structure or structures, I like to go back and see if the couplings make sense and thus verify the elucidation. Nothing worse to the ego than passing on a wrong structure.

14. If there is any stereochemistry in the structure, additional experiments like NOESY data are needed. Remember for every chiral carbon, there 2^n possibilities where n is the number of carbons.

Just a final note. In my experience, there are two classifications for unknowns for small molecules: synthetic/impurity and natural unknown.

A synthetic/impurity unknown generally has additional information, that is, information on the starting material and probable product(s). It can be a bad thing to know this and I have spoken with some elucidators who choose not to know anything about the starting material and expected product(s) as this could bias the elucidator and send them on the wrong track. That being said, due to time constraints, it is so tempting to use this information and speed up an elucidation. 

Natural unknowns are tough especially if there is very little material. The sample size can restrict the types and acquisition time of NMR experiments. This is very similar to impurity identification. Dereplication (searching a database) can help assuming it is not a novel compound.

The intricacies of solving for an unknown structure using solution NMR data

Let me begin by stating my experiences with elucidations on organic molecules. I have led over 60 training sessions attended by chemists and Spectroscopists from Academia to industry around the world. Some more successful than others—this came down to expertise of the attendee. I have also conversed with dozens of chemists/Spectroscopists, experts and non-experts, at conferences, onsite visits and through email on this amazing subject.

Note: Some of the people I have met are listed at the following website. http://www.acdlabs.com/feedback/chaleluc.html The list is longer but I am not up to pestering the chemists and asking for their permission to list the institution where they work.

By the way, I am not going into how I got into elucidation while in graduate school, especially helping all those poor synthetic chemists solve their unknowns. :)

How about this for pressure? Imagine standing before an audience and asked to elucidate a structure from NMR data especially since you have never seen the data before. Well I have done that and on at least 10 occasions that I can remember. I am not trying to toot my horn but I want to express that my experience in this matter is applicable.

Taking a step back, the one thing all my training encounters have in common is success is judged by how much practice and time one denotes to this task. Just like anything new, practice through repetition makes things better and easier to handle. It is not that easy and not every chemist/Spectroscopist realizes the complexity behind teaching this process.

I am going to try to list all the possible nuances an NMR elucidator must take note of for any unknown whether they are aware of it or not. I want to talk about MS but I also want to keep the blog short. Maybe in the future another blog will follow on this subject.

1. The toughest elucidations and the ones I do not like doing are samples that have mixtures, salts, polymers or rotamers. Trying to pick out what belongs to what compound is a big headache especially when the compounds exist at an approximately equal ratio to each other. A big ouch.

2. Size of the structure can complicate matters. I have heard peptides and protein elucidation are tough. I managed once to do an elucidation of a peptide (~1200 Da), however, I knew beforehand what the end product so I would not classify this as a true unknown elucidation.

3. Knowing when to use what combination of NMR experiments is crucial. There are hundreds of choices for NMR experiments and some are more useful than others are. It is so easy to waste your time and instrument time. Experience and versatility count here.

4. Learning how to interpret the NMR data. I find the bulk of any elucidation falls under this classification. There are so many points to cover here so I will try to list as many as I can think of.
a. The tough stuff comes from differentiating between true signals and artifacts. Experience really counts here.
b. Dealing with overlapping 2D NMR correlations in a crowded region.
c. Deciding whether a correlation in an HMBC experiment is showing a 2J or 3J separation.
d. Missing signals due to symmetry, broad peaks, low S/N or paramagnetic metals present.
e. Few 2D correlations from a structure with a high RDBE (Ring and Double Bond Equivalence or element of unsaturation) or containing few hydrogens. Lots of possible candidate structures here.

5. Narrowing down to a single Molecular Formula. I find combining NMR with MS data very applicable here.

6. Deciding the carbon multiplicity (C, CH, CH2, CH3) should be easy to do. Knowing the MF could be helpful here and vice versa. Having this information lets you know how many exchangeable atoms are present.

7. Searching across a library/database of chemical shifts and coupling constants like C-F. Having information on the starting material or derivatives can speed things up. Absolutely an easy thing to do has the potential to save lots of time.

8. Deciding carbon bond order (hybridization) – should I consider sp carbons? I rarely run into sp carbons but it is always a thought in the back of my mind.

9. I like to ask myself these questions when dealing with heteroatoms. Do I have bonds between heteroatoms or bonds between heteroatoms of the same type? Do I have a pentavalent nitrogen? I find IR data to be helpful here. If I have a salt, which atom(s) is charged?

10. Exclude (or bias) possibilities based on NMR shifts. If I have a carbon at 200 ppm, then I immediately jump to a C=O group whereas a carbon at 120 ppm I exclude C=O as a possibility. Biasing the data can be a bad thing sometimes.

11. Apply an internal filter. I do this without realizing I’m doing this. For example, if I see a list of 6 aromatic carbons, I jump to a benzene ring. On an initial pass, I commonly exclude 3 and 4 membered rings and even the higher up rings such as 8-10 membered rings as I rarely run into these. I also try not to draw fragments and structures that do not have crossing (concatenation) bonds. Knowing whether the unknown is natural or not can help sometimes.

12. I like to draw out possible fragments and candidate structures especially since it helps to get a visual on things. The next step is to rank the list and see what can be eliminated. Logics and reasoning do the trick here.

13. With a candidate structure or structures, I like to go back and see if the couplings make sense and thus verify the elucidation. Nothing worse to the ego than passing on a wrong structure.

14. If there is any stereochemistry in the structure, additional experiments like NOESY data are needed. Remember for every chiral carbon, there 2^n possibilities where n is the number of carbons.

Just a final note. In my experience, there are two classifications for unknowns for small molecules: synthetic/impurity and natural unknown.

A synthetic/impurity unknown generally has additional information, that is, information on the starting material and probable product(s). It can be a bad thing to know this and I have spoken with some elucidators who choose not to know anything about the starting material and expected product(s) as this could bias the elucidator and send them on the wrong track. That being said, due to time constraints, it is so tempting to use this information and speed up an elucidation. 

Natural unknowns are tough especially if there is very little material. The sample size can restrict the types and acquisition time of NMR experiments. This is very similar to impurity identification. Dereplication (searching a database) can help assuming it is not a novel compound.