Identifying a monosubstituted benzene fragment in a 1H NMR spectrum

Although peak crowding can be a nuisance, a monosubstituted benzene fragment can be identified by a 1H NMR. A good marker for a monosubstituted benzene ring, and thus how an elucidator can clue in to its presence for an unknown, is whether the sum of the relative integrals for the aromatic resonances add up to 5.

Below are 6 1H NMR spectra illustrating the various patterns for a monosubstituted benzene fragment. Although other possibilities can exist, these are the typical patterns to be on the lookout for in the aromatic region.

Identifying a monosubstituted benzene fragment in a 1H NMR spectrum

Although peak crowding can be a nuisance, a monosubstituted benzene fragment can be identified by a 1H NMR. A good marker for a monosubstituted benzene ring, and thus how an elucidator can clue in to its presence for an unknown, is whether the sum of the relative integrals for the aromatic resonances add up to 5.

Below are 6 1H NMR spectra illustrating the various patterns for a monosubstituted benzene fragment. Although other possibilities can exist, these are the typical patterns to be on the lookout for in the aromatic region.

Examining the 12C and 13C ratio in a Mass spectrum – carbon isotopic abundance

On a mass spectrum, the carbon 13 isotope peak appears at approximately one mass unit higher (the actual mass delta 1.00335) than the carbon 12 ion peak. The intensity of these isotopes is proportional to the relative abundance of the naturally occurring isotopes. The relative abundance of the two isotopes is 12C ≈ 98.9% and 13C ≈ 1.1%.

Without any structural information, we can estimate a general ballpark figure for the number of carbons using the peak intensities for the 12C and 13C ion peaks.

For the 12C ion peak (m/z 386.4) shown below, the upper limit on the number of carbons is calculated at 386.4 / 12 = 32.2. Rounding down, we arrive at 32 carbons. Based on this information, the intensity of the 13C peak is expected at 32 * 1.1% = 35.2%.

Experimentally, the intensity of the 13C peak is 23.6% with the 12C peak at 100%. The calculation is (23.6 / 100 *100%) / 1.1% = 21.4.

Formula*:

To estimate the # of Carbons ≈ (Int13C/Int12C * 100%) / 1.1%

*Note: Instrument and the type of experiment can influence the intensity of the 13C peak and thus produce a less reliable estimate. Ideally, the result is best evaluated in conjunction with the carbon count from a 13C NMR.

Examining the 12C and 13C ratio in a Mass spectrum – carbon isotopic abundance

On a mass spectrum, the carbon 13 isotope peak appears at approximately one mass unit higher (the actual mass delta 1.00335) than the carbon 12 ion peak. The intensity of these isotopes is proportional to the relative abundance of the naturally occurring isotopes. The relative abundance of the two isotopes is 12C ≈ 98.9% and 13C ≈ 1.1%.

Without any structural information, we can estimate a general ballpark figure for the number of carbons using the peak intensities for the 12C and 13C ion peaks.

For the 12C ion peak (m/z 386.4) shown below, the upper limit on the number of carbons is calculated at 386.4 / 12 = 32.2. Rounding down, we arrive at 32 carbons. Based on this information, the intensity of the 13C peak is expected at 32 * 1.1% = 35.2%.

Experimentally, the intensity of the 13C peak is 23.6% with the 12C peak at 100%. The calculation is (23.6 / 100 *100%) / 1.1% = 21.4.

Formula*:

To estimate the # of Carbons ≈ (Int13C/Int12C * 100%) / 1.1%

*Note: Instrument and the type of experiment can influence the intensity of the 13C peak and thus produce a less reliable estimate. Ideally, the result is best evaluated in conjunction with the carbon count from a 13C NMR.

Complicating NMR data interpretation

Typically, structure elucidation via NMR can be ascribed by a stepwise workflow:

1. a sample is prepared for NMR, 2. the NMR instrument is optimized for data collection, 3. NMR data is acquired, 4. the spectral data is processed, 5. the spectral data is searched/compared to an internal database for possible hits or similarities, 6. the NMR data is pieced together to create a list of candidate structures, 7. the candidate structures are checked/verified against additional data.

Structure elucidation is not as simple as it sounds. Collecting NMR data on an unknown sample and heading straight down the path to solve it is not a guarantee for success. Optimizing data collection is a critical step and one that is frequently overlooked. Here is a list of issues that may arise when step 2 is inadequately applied: peaks are poorly shimmed, a probe is poorly tuned and/or matched, the presence of solvent impurities, outside interferences on the instrument, the wrong experiment parameters are setup , and the list goes on. The major consequence of any of these actions is NMR information can be misinterpreted.

TIP: spending a few minutes to quickly check the processed NMR data before moving onto the next step can save you loads of time and anguish.

Complicating NMR data interpretation

Typically, structure elucidation via NMR can be ascribed by a stepwise workflow:

1. a sample is prepared for NMR, 2. the NMR instrument is optimized for data collection, 3. NMR data is acquired, 4. the spectral data is processed, 5. the spectral data is searched/compared to an internal database for possible hits or similarities, 6. the NMR data is pieced together to create a list of candidate structures, 7. the candidate structures are checked/verified against additional data.

Structure elucidation is not as simple as it sounds. Collecting NMR data on an unknown sample and heading straight down the path to solve it is not a guarantee for success. Optimizing data collection is a critical step and one that is frequently overlooked. Here is a list of issues that may arise when step 2 is inadequately applied: peaks are poorly shimmed, a probe is poorly tuned and/or matched, the presence of solvent impurities, outside interferences on the instrument, the wrong experiment parameters are setup , and the list goes on. The major consequence of any of these actions is NMR information can be misinterpreted.

TIP: spending a few minutes to quickly check the processed NMR data before moving onto the next step can save you loads of time and anguish.

Identifying fragments using a Neutral Loss spectrum

A calculated neutral loss spectrum is obtained from a mass spectrum by determining the mass differences between the precursor ion m/z and each of the other peaks in the spectrum and plotting the original intensity versus neutral mass. Neutral losses with small masses have limited possibilities for their composition and thus can facilitate the identification of specific species.

Common losses (based on the J.H. Beynon table) for fragments with C, H, O, N elements are reported below:

OH, NH3                                                     nominal mass of 17

CO, N2 C2H4, CH2N                                    nominal mass of 28

CHO2, CH3NO, CH5N2, C2H7N, C2H5O        nominal mass of 45

A positive ion EI mass spectrum of benzoic acid is shown below. The molecular ion (M+') appears at an m/z of 122. Two additional ion clusters appear at m/z of 77 and 105. The Beynon table reports 4 and 13 possible fragments for m/z 77 and 105, respectively. Examining the neutral loss spectrum, shown below, 3 basic ion clusters appear: m/z 17, 28 and 45. The Beynon Table (listed above) reports 2, 4 and 5 possible fragments for m/z 17, 28 and 45, respectively. When working with fewer fragment possibilities, one can reduce the time spent on an elucidation problem.

TIP: Knowing a partial fragment for an unknown can aid in narrowing down a molecular formula and limit the number of candidate structures.

Spectra courtesy of Joe DiMartino, M.Sc.