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In the realm of chemistry, the concept of allotropy unveils the mesmerizing ability of an element to exist in multiple forms, known as allotropes, each exhibiting distinct physical and chemical properties. Among the myriad elements that showcase this intriguing phenomenon, carbon stands out as a versatile and captivating element with a plethora of allotropes. Understanding the diverse carbon allotropes, their mechanical and chemical properties, as well as the characterization methods used to unveil their secrets, is essential for unlocking their potential in various scientific and technological applications.

Carbon Allotropes: A Kaleidoscope of Structures and Properties

Carbon, with its ability to form strong covalent bonds and diverse molecular structures, manifests in several allotropes, each with unique properties and applications. Here are some of the prominent carbon allotropes:

1. Diamond: The epitome of elegance and durability, diamond features a three-dimensional network of carbon atoms arranged in a tetrahedral structure. Renowned for its exceptional hardness, thermal conductivity, and optical properties, diamond finds applications in jewellery, cutting tools, and industrial abrasives.

2. Graphite: In contrast to diamond’s rigid structure, graphite embodies layers of carbon atoms arranged in hexagonal rings, imparting lubricating properties. Graphite is commonly used in pencil leads, lubricants, and electrodes due to its soft and slippery nature.

3. Graphene: A single layer of graphite arranged in a two-dimensional hexagonal lattice structure, graphene boasts remarkable mechanical strength, electrical conductivity, and thermal properties. This wonder material holds promise for applications in electronics, energy storage, and sensors.

4. Carbon Nanotubes: These cylindrical structures composed of rolled-up graphene sheets exhibit exceptional mechanical strength, electrical conductivity, and thermal properties. Carbon nanotubes find applications in nanotechnology, composites, and electronics due to their unique structural characteristics.

5. Fullerenes: Hollow carbon molecules with cage-like structures, fullerenes like Buckminsterfullerene (C60) possess intriguing properties such as high electron affinity and reactivity. Fullerenes are utilized in diverse fields ranging from drug delivery to superconductors.

Mechanical and Chemical Properties of Carbon Allotropes

Each carbon allotrope showcases a distinctive set of mechanical and chemical properties based on its unique structure and bonding arrangement:

– Diamond: Exceptional hardness, transparency, high thermal conductivity.
– Graphite: Softness, lubricating properties, opaque nature.
– Graphene: High electrical conductivity, mechanical strength, thermal conductivity.
– Carbon Nanotubes: Exceptional mechanical strength, electrical conductivity, and thermal properties.
– Fullerenes: High electron affinity, reactivity, unique cage-like structures.

Characterization Methods for Carbon Allotropes

To unravel the mysteries of carbon allotropes and understand their properties at a molecular level, various sophisticated characterization techniques are employed:

1. Fourier Transform Infrared Spectroscopy (FTIR)

Fourier-transform infrared (FTIR) spectroscopy is another powerful analytical technique that can aid in the characterization of different allotropies of carbon by providing information about their chemical bonding, functional groups, and structural properties. Here’s how FTIR analysis can be utilized to study various carbon allotropes:

a. Functional Group Identification: FTIR spectroscopy can be used to identify specific functional groups present in different carbon allotropes based on the characteristic absorption bands observed in their infrared spectra. For example, the presence of sp2 and sp3 hybridized carbon bonds in graphene, carbon nanotubes, and diamond can be distinguished by analyzing the peaks corresponding to C=C and C-H stretching vibrations, respectively. Additionally, functional groups such as hydroxyl (-OH), carbonyl (C=O), carboxyl (-COOH), and epoxy (-O-) groups can be detected in carbon materials through their distinctive IR absorption bands, allowing researchers to assess the surface chemistry and reactivity of the allotropes.

b. Structural Analysis: FTIR spectroscopy can provide insights into the structural characteristics of carbon allotropes by probing the vibrational modes of carbon-carbon bonds and other chemical interactions within the materials. The presence of sp2 and sp3 hybridized carbon atoms, aromatic rings, double bonds, and functional groups can be inferred from the intensity, position, and shape of the absorption bands in the FTIR spectrum. By correlating the vibrational frequencies of carbon allotropes with their structural features, researchers can elucidate the bonding configurations, lattice arrangements, and crystallographic orientations of the materials.

c. Surface Modification and Functionalization: FTIR spectroscopy is a valuable tool for studying surface modifications, functionalization reactions, and chemical interactions on the surface of carbon allotropes. By comparing the FTIR spectra of pristine and modified carbon samples, researchers can identify changes in the absorption bands associated with functional groups introduced during surface treatments, chemical derivatization, or doping processes. This enables the characterization of surface functionalization strategies, quantification of surface coverage, and evaluation of chemical stability in functionalized carbon materials.

d. Quantitative Analysis: FTIR spectroscopy can be utilized for quantitative analysis of functional groups, impurities, and contaminants in carbon allotropes by measuring the absorbance intensities at specific wavenumbers corresponding to characteristic vibrational modes. By establishing calibration curves or using peak area integration methods, researchers can quantify the relative concentrations of different functional groups or impurities in a carbon sample, providing valuable information about its chemical composition, purity, and quality.

e. Stability and Degradation Studies: FTIR spectroscopy can be employed to investigate the stability, degradation mechanisms, and chemical reactivity of carbon allotropes under various environmental conditions. By monitoring changes in the FTIR spectra over time or upon exposure to external factors (e.g., temperature, humidity, oxidation), researchers can assess the material’s resistance to degradation, identify degradation products or by-products, and elucidate the underlying chemical processes that influence its performance and longevity.

2. Raman Spectroscopy

By studying the vibrational modes of carbon materials, Raman spectroscopy offers valuable information about their structural properties and defects. Raman spectroscopy is a powerful analytical technique that can provide valuable insights into the structural and vibrational properties of different carbon allotropes. Here’s how Raman spectroscopy can help characterize various carbon allotropes:

a. Structural Analysis: Raman spectroscopy can distinguish between different carbon allotropes based on their unique structural characteristics. Each allotrope exhibits specific Raman-active vibrational modes, allowing researchers to identify and differentiate between diamond, graphite, graphene, carbon nanotubes, and fullerenes.

b. Defect Detection: Carbon allotropes may contain defects or impurities that can influence their properties. Raman spectroscopy can detect and characterize these defects by analyzing changes in the Raman spectra, such as shifts in peak positions or intensity variations. This information is crucial for understanding the quality and purity of carbon materials.

c. Quantitative Analysis: Raman spectroscopy can be used for quantitative analysis of carbon allotropes, providing information about the relative abundance of different phases or structures within a sample. By correlating Raman spectral features with specific carbon allotropes, researchers can quantitatively assess the composition and distribution of various forms of carbon in a sample.

d. Chemical Functionalization: Raman spectroscopy is sensitive to chemical modifications and functional groups present on the surface of carbon allotropes. By analyzing changes in Raman spectra upon functionalization or chemical treatment, researchers can characterize the interaction between carbon materials and other substances, enabling the design of tailored functionalized carbon materials for specific applications.

3. X-ray Photoelectron Spectroscopy (XPS)

XPS is another valuable technique that can aid in the characterization of different allotropies of carbon. Here’s how XPS analysis can provide insights into the structural and chemical properties of various carbon allotropes:

a. Elemental Composition: XPS analysis can determine the elemental composition of carbon allotropes by measuring the binding energies of core-level electrons, such as the carbon 1s peak. Different carbon allotropes exhibit distinct binding energy values for their core-level electrons due to variations in the local chemical environment and bonding configurations. By comparing the XPS spectra of carbon allotropes with reference data, researchers can identify the presence of specific elements and quantify their relative concentrations.

b. Chemical State Analysis: XPS analysis can reveal information about the chemical state and bonding characteristics of carbon allotropes. The peak shapes, positions, and intensities in the XPS spectra provide insights into the oxidation state, functional groups, and bonding configurations present in a carbon sample. For example, XPS can differentiate between sp2 and sp3 hybridized carbon atoms in graphene and diamond, respectively, based on their distinct chemical environments and electronic structures.

c. Surface Sensitivity: XPS analysis is a surface-sensitive technique that probes the top few nanometers of a material, making it well-suited for characterizing the surface chemistry of carbon allotropes. By analyzing the elemental composition and chemical states at the surface of a carbon sample, researchers can gain valuable information about surface contaminants, functionalization, and modifications that may influence the material’s properties and reactivity.

d. Dopant Identification: XPS analysis can help identify dopants or impurities incorporated into carbon allotropes to modify their electronic, optical, or catalytic properties. By analyzing the XPS spectra of doped carbon materials, researchers can detect changes in the core-level binding energies and chemical states of the dopant atoms, providing insights into their distribution, concentration, and interaction with the host carbon lattice.

e. Depth Profiling: XPS analysis can also be combined with depth profiling techniques to investigate the chemical composition and structure of carbon allotropes as a function of depth below the surface. Depth profiling methods, such as angle-resolved XPS or sputter depth profiling, allow researchers to study the layer-by-layer composition, doping profiles, and interface properties of carbon materials, enabling a comprehensive understanding of their structure-property relationships.

4. Ultraviolet-Visible Spectroscopy (UV-Vis)

UV-Vis spectroscopy aids in studying the optical properties of carbon allotropes, including absorption and emission spectra.

UV-Vis spectroscopy is another valuable technique that can aid in the characterization of different allotropies of carbon by providing insights into their electronic and optical properties. Here’s how UV-Vis analysis can be utilized to study various carbon allotropes:

a. Bandgap Determination: UV-Vis spectroscopy can be used to determine the bandgap energy of carbon allotropes, which is a crucial parameter that influences their electronic and optical properties. By measuring the absorption spectrum of a carbon sample in the UV and visible regions, researchers can identify the onset of absorption (i.e., the bandgap energy) and characterize the material’s semiconducting or insulating behavior. Different carbon allotropes, such as graphene, carbon nanotubes, and diamond, exhibit distinct bandgap energies due to variations in their electronic structure and bonding configurations.

b. Optical Absorption Features: UV-Vis spectroscopy can reveal information about the optical absorption features of carbon allotropes, such as excitonic transitions, interband transitions, and localized electronic states. The absorption spectrum of a carbon sample can exhibit characteristic peaks, shoulders, or broad absorption bands corresponding to specific electronic transitions within the material. By analyzing the shape, intensity, and position of these absorption features, researchers can gain insights into the electronic structure, energy levels, and optical properties of different carbon allotropes.

c. Defects and Functional Groups: UV-Vis spectroscopy can be used to detect defects, functional groups, and chemical modifications in carbon allotropes that affect their electronic and optical properties. Defect-induced states, surface functionalization, and doping can introduce additional absorption features or modify the intensity of existing peaks in the UV-Vis spectrum of a carbon sample. By comparing the UV-Vis spectra of pristine and modified carbon materials, researchers can identify changes in the electronic structure, bandgap energy, and optical response resulting from defects or functionalization.

d. Quantitative Analysis: UV-Vis spectroscopy can also be employed for quantitative analysis of carbon allotropes by correlating the absorption intensity with the concentration of specific components or impurities in a sample. By measuring the absorbance at characteristic wavelengths and establishing calibration curves for different carbon species or dopants, researchers can quantify the relative abundance of components in a complex mixture or determine the doping level in doped carbon materials.

e. Stability and Degradation Studies: UV-Vis spectroscopy can provide valuable information about the stability, degradation, and photochemical behavior of carbon allotropes under various environmental conditions. By monitoring changes in the UV-Vis absorption spectrum over time or under different exposure conditions (e.g., light irradiation, temperature variations), researchers can assess the material’s photochemical stability, degradation mechanisms, and resistance to environmental factors that may impact its performance and longevity.

5. X-ray Diffraction (XRD)

X-ray diffraction (XRD) analysis is another powerful technique that can provide valuable insights into the structural properties of different carbon allotropes. Here’s how XRD analysis can help characterize various allotropies of carbon:

a. Crystal Structure Determination: XRD analysis can be used to determine the crystal structure of carbon allotropes by analyzing the diffraction patterns generated when X-rays interact with the periodic arrangement of atoms in a material. Different carbon allotropes have distinct crystal structures, such as the hexagonal lattice of graphite, the cubic structure of diamond, and the helical structure of carbon nanotubes. By comparing experimental XRD patterns with reference data, researchers can identify and confirm the crystal structure of a carbon allotrope.

b. Phase Identification: XRD analysis can help identify and distinguish between different phases or polymorphs of carbon allotropes present in a sample. By analyzing the positions and intensities of diffraction peaks in the XRD pattern, researchers can determine the presence of specific allotropes, such as graphite, diamond, graphene, carbon nanotubes, and fullerenes. This information is essential for characterizing the composition and phase distribution within a carbon sample.

c. Crystallite Size and Orientation: XRD analysis can provide information about the crystallite size and orientation of carbon allotropes. By analyzing the broadening of XRD peaks, researchers can estimate the average crystallite size of a material, which is crucial for understanding its structural properties. Additionally, XRD can reveal information about the preferred orientation or texture of crystallites within a sample, offering insights into the growth and alignment of carbon allotropes.

d. Strain Analysis: XRD analysis can also be used to investigate the presence of strain or defects in carbon allotropes. Changes in the peak positions and peak shapes in the XRD pattern can indicate the presence of lattice strain, dislocations, or defects in the crystal structure of a material. By quantifying these structural imperfections, researchers can assess the mechanical stability and performance of carbon allotropes.

e. Thermal Stability and Phase Transitions: XRD analysis can be employed to study the thermal stability and phase transitions of carbon allotropes under varying temperature and pressure conditions. By monitoring changes in the XRD patterns as a function of temperature or pressure, researchers can identify phase transformations, melting points, and structural changes in carbon materials, providing crucial information for understanding their behaviour under different environmental conditions.

Conclusion

In conclusion, the captivating world of carbon allotropes unveils a treasure trove of possibilities for scientific exploration and technological innovation. By delving into the diverse structures and properties of carbon allotropes and employing advanced characterization methods, researchers can unlock the full potential of these fascinating materials across a wide range of applications. The allure of carbon allotropes continues to inspire groundbreaking discoveries and advancements in materials science and beyond.

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We present here results on a Raman spectroscopic study of the deposited defected graphene on Si substrates by chemical vapor deposition (thermal decomposition of acetone). The graphene films are not deposited on the (001) Si substrate directly but on two types of interlayers of mixed phases unintentionally deposited on the substrates: а diamond-like carbon (designated here as DLC) and amorphous carbon (designated here as αC) are dominated ones.

The performed thorough Raman spectroscopic study of as-deposited as well as exfoliated specimens by two different techniques using different excitation wavelengths (488, 514, and 613 nm) as well as polarized Raman spectroscopy establishes that the composition of the designated DLC layers varies with depth: the initial layers on the Si substrate consist of DLC, nanodiamond species, and C₇₀fullerenes while the upper ones are dominated by DLC with an occasional presence of C₇₀ fullerenes. The αC interlayer is dominated by turbostratic graphite and contains a larger quantity of C₇₀ than the DLC-designated interlayers. The results of polarized and unpolarized Raman spectroscopic studies of as-grown and exfoliated graphene films tend to assume that single- to three-layered defected graphene is deposited on the interlayers. It can be concluded that the observed slight upshift of the 2D band as well as the broadening of 2D band should be related to the strain and doping.

1. Introduction

Graphene is a one-atom-thick layered material that consists of completely sp²-bonded carbon atoms tightly packed into a honeycomb lattice. It has a lot of unique properties promising a huge number of possible applications (see, e.g., [1]). A lot of different ways of synthesizing graphene were experimentally tested during the last decade; however, only thermally and plasma-assisted chemical vapor deposition (CVD/PECVD) on metal substrates (copper, nickel, etc.) [2, 3] as well as epitaxial growth on SiC substrates and so on [4, 5] were developed for industrial application. The latter method is based on C (or Si) termination of the (0001)_C (or (0001)_Si) SiC surface and requires high temperature and expensive SiC substrates. The CVD method is based on the plasma-enhanced thermal decomposition of a carbon-containing precursor on a catalytic metal surface. This method provides high reliability and relatively high quality of graphene films, and now, there are a lot of suppliers of reactors for PECVD of graphene. The most preferred precursor is methane (CH₄) as the chemical bond in CH₄ is relatively strong and prevents fast decomposition of the reagent at temperatures below 1000°C (see, e.g., [6]). However, production for microelectronic applications requires transfer of the graphene layers on an insulating surface and, consequently, a large number of additional defects affecting the properties of graphene can be introduced. Therefore, the problem with the deposition of graphene on silicon (or surfaces compatible with silicon technology such as SiO₂) still remains unsolved. We demonstrated the possible application of acetone as a precursor in a thermally assisted CVD and showed that few-layered defected graphene/folded graphene can be deposited on commercially available metal foils—Ni, (Cu_0.5Ni_0.5), μ-metal, and stainless steel SS 304 in a recently published work [7]. Further, we established (see [8]) by Raman spectroscopy, scanning electron microscopy (SEM), X-ray diffraction (XRD), and grazing incidence X-ray diffraction (GIXRD) as well as by X-ray photoelectron spectroscopy (XPS) the presence of single- to few-layered defected graphene on two different types of interlayers deposited on (100) Si surface: (i) a diamond-like carbon (DLC) layer with some SiC contents (in the range below 5w%) and some residual quantities of SiO₂, and (ii) a complex amorphous carbon layer consisting of a mixture of sp²– and sp³-hybridized carbon as well as very small amount of fullerenes, SiO, and so on.

Here, we focus our experimental study on the Raman spectroscopic characterization of defected as-deposited graphene layers (including polarized spectroscopy) as well as graphene flakes exfoliated from similar specimens by two different ways using 488, 514, and 633 nm excitation laser wavelengths aiming at unambiguous confirmation of the graphene deposition of as well as the identification of the exact composition of the interlayers between the Si substrate and graphene layer/s.

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2. Experimental

2.1. CVD Process

We use 2 inches in diameter (001) Si substrates and a horizontal tube quartz CVD reactor with an internal diameter of approximately 70 mm. The experimental setup also consists of a gas-supply system (inlet and outlet parts), a thermostat with acetone evaporation alert/indication system, a quartz substrate holder, and a resistive heating furnace. The CVD process is based on thermal decomposition of acetone in Ar main gas flow. The deposition temperature was in the range 1150–1160°C. The temperature of the thermostat was kept at 12°C. In order to prevent the supersaturation in the high-temperature zone of the reactor, we used a “pulsed” regime in experiments by alternating the flow of the gas mixture of Ar + C₃H₆O) for 3 min on top of the main flow of pure Ar of about 150–180 cm³/min for 1.5 min for each pulse. The optimal results (predominantly single-layered graphene) were obtained after two deposition pulses.

2.2. Exfoliation

We exfoliated the carbon films deposited on (001) Si substrates by the following two different techniques:(i)The Scotch tape method (see, e.g., [9]): we put tightly the adhesive Scotch tape on the multilayered graphene side of the specimens. After peeling the tape off the specimen, a single- to few-layered graphene remains on the tape’s surface and the interlayer between the upper few layers of graphene and the substrate becomes accessible for spectroscopic examination. Then, we put tightly the Scotch tape with graphene flakes either on 320 nm SiO₂/Si or on glass substrate. About 30–50% of the graphene flakes remain adhered to the SiO₂ or glass substrate after removing the tape due to the Van der Waals force.(ii)We also adhered the multilayered graphene side of the specimens to epoxy resin. After careful cleavage, the most part of the graphene layer/s remains on the surface of the resin. Then, the adhered to the resin graphene film becomes accessible for spectroscopic examination. The Raman spectrum of the epoxy resin does not contain strong peaks around the 2D band of graphene (the area around 2630–2670 cm⁻¹). We established that the 2D band of graphene is clearly distinguishable for graphene regions lying on gas bubbles close to the surface of the resin; otherwise, the 2D band of graphene is weak.

2.3. Characterization

The Raman measurements were carried out in backscattering geometry at a micro-Raman HORIBA Jobin Yvon Labram HR 800 visible spectrometer equipped with a Peltier-cooled CCD detector with a He-Ne (633 nm wavelength and 0.5 mW) laser excitation. The 514 nm (about 23 mW) as well as 488 nm (about 24 mW) lines of an external Ar laser were also used. The laser beam was focused on a spot of about 1 μm in diameter, the spectral resolution being 0.5, 0.7, and 1 cm⁻¹, respectively, or better.

The Raman spectrum of graphene is a clearly established fingerprint of this 2D material (see [10]). The main first-order features in the Raman spectra of graphene and defect-infested graphene excited at 633 nm wavelength are the following:(i)G band (~1582 cm⁻¹) is the only band in graphene allowed by selection rules for first-order Raman effect; it is ascribed to optical (iTO and LO) doubly degenerate phonons of E_2g symmetry at the Γ point (initially described by Tuinstra and Koenig [11]).(ii)D band (~1330 cm⁻¹) is due to breathing-like bands of C hexagonal rings (corresponding to transverse optical phonons near the K point) and requires a defect for its activation via an intervalley double-resonance Raman process (see [12]).(iii)D’ band (at about 1615 cm⁻¹; defect induced similarly to the D band) occurs via an intravalley double-resonance process (see, e.g., [13]).(iv)D” band (at about 1145 cm⁻¹) is resulting from double-resonance intervalley scattering of LA phonons on defects (see [14]). The intensity of this band should be about 100 times lower than that of the D band.

Overtones and combination bands:(i)2D band (historically known from graphite and carbon nanotube-related literature as G’- peak) appears at about 2648–2665 cm⁻¹. It is clearly shown [15–20] that the shape and width of the 2D band can be used for the identification of the mono-, bi-, and three-layered graphene.(ii)The overtone of the D’- peak (2D’) and combination G (phonons), as well as (D+D’) bands, occur around 3230, 2450, and 2930 cm⁻¹, respectively (see [21]).

3. Results and Discussion

Two areas with different surface morphologies are observed by optical microscopy (Figure 1(a)): a clear relief (ridge-like formations) lying along <001> directions covers the first area denoted as DLC while the second area (denoted as αC) is covered by an inhomogeneous film with a constant depth. It should be also mentioned that optical inhomogeneities are observed on the DLC as well as αC-marked areas.
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(e)Figure 1Optical microscopy image of the surface morphology of (a) as-deposited graphene and graphene-related phases on diamond-like carbon (DLC) and amorphous carbon (αC) interlayers. The arrows remarked [100] and [010] directions of the Si substrate. The marker represents 20 μm. (b) The exfoliated and transferred graphene flakes on 320 nm SiO₂. The Raman spectra are taken from “+”-marked positions. The marker represents 30 μm. (c) The layers remaining on the surface of the substrate after exfoliation by Scotch tape. The Raman spectra are taken from the “+”-marked positions near points 1, 2, and 3. The marker represents 30 μm. (d) The exfoliated and transferred graphene flakes on glass substrate. The Raman spectra are taken from the “+”-marked positions near points 1 and 2. The marker represents 20 μm. (e) A graphene flake on air bubble near the epoxy resin surface. The Raman spectra are taken from the square-marked position. The marker represents 20 μm.

It should be recalled that the Raman spectrum (excited at 633 nm laser wavelength) taken from αC- and DLC-denoted areas (see [8]) contains all features typical for graphene: symmetric and clearly pronounced 2D band with full width at a half maximum (FWHM) of 40–56 cm⁻¹, I_2D/I_G ratio between 2 and 3.5, and I_2D/I_D ratio between 2 and 4. However, the 2D band appears at about 2660–2668 cm⁻¹ (for single- and bilayered graphene, respectively), that is, it is blueshifted by about 20 cm⁻¹ relative to the results presented in [15, 22, 23].

Due to the double-resonance origin of most of the monitored spectral features, we perform a Raman spectroscopy examination of as-deposited defected graphene at 488, 514, and 633 nm excitation wavelengths and the results are presented in Figure 2. The 2D bands are blueshifted by about 20 cm⁻¹ and can be typically deconvoluted into (a) a single Lorentzian with FWHM of about 40-41 cm⁻¹ (see Figure 3(a)); (b) four Lorentzians (FWHM of 22 (±1) cm⁻¹) for 2D band with total width of 45–56 cm⁻¹ (see Figure 3(b)); and (c) six Lorentzians (Figure 3(c)) for 2D band with total width larger than 56 cm⁻¹. The results of the deconvolution indicate the presence of single-, bi-, and three-layered defected graphene, respectively (see [15–20]). We did not establish a clear difference between the graphene layers deposited on αC and DLC interlayers; however, bi- and three-layered areas were more frequently observed on DLC interlayers. The results for predominantly single-layered (SL) and bilayered (BL) defected graphene (according to the deconvolution of 2D bands) are summarized in Table 1.

Figure 2Raman spectra taken from as-grown films excited at 633 nm (red trace), 514 nm (blue trace), and 488 nm (green trace) laser wavelengths.
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(c)Figure 3Deconvolution of 2D band identified as coming from single-layered (a), bilayered (b), and three-layered defected graphene deposited on αC. The spectrum is excited at 633 nm laser wavelength.Table 1Summarized results of Raman spectroscopy examination of as-deposited defected graphene films.

In order to access the interlayers as well as graphene flakes for Raman examination, the so-called Scotch tape method was initially used for exfoliation. The Raman spectra of the graphene flakes exfoliated in this way with some occasional amorphous (αC) interlayers transferred to Si/SiO₂ (300 nm) or glass substrate are shown in Figures 4 and 5, respectively.

Figure 4The Raman spectrum of defected 1-2-layered graphene transferred on 320 nm SiO₂. The 2D band is symmetric and appears at 2658-2659 cm⁻¹ with FWHM of 38–40 cm⁻¹ (measured in point 2) and 40–42 cm⁻¹ (measured in point 1).

Figure 5The Raman spectrum of the interlayer (point 1, Figure 1(c)) of αC after exfoliation by Scotch tape. The features observed at about 1450 and 1530 cm⁻¹ are typical for C₇₀ fullerenes.

A lot of flakes (of the order of 10²) were transferred on Si/SiO₂ and examined by Raman spectroscopy. The Raman spectra are enhanced due to interference effects caused by the SiO₂ 300 nm layer over the Si substrate, and I_2D/I_G varies in the range 3.5-6.0. However, it was impossible to isolate single-layered graphene flake (or to obtain clear Raman response of single-layered graphene) in this way. The exfoliated flakes were never transparent (see point 1 in Figure 1(b)). The best spectra were recorded from the points in a darker contrast (point 2 in Figure 1(b)), but the FWHM of 2D Raman band remains >35 cm⁻¹. Moreover, the D” band slightly overlaps with the second order of Si substrate when the spectrum is excited at 514 as well as 488 nm laser wavelengths.

After peeling the tape off the specimen, the interlayer between the upper flake and the substrate is accessed. The remaining interlayers have different optical contrasts (see Figure 1(c)) and Raman spectra: the spectrum of typically retained interlayer (point 1 in Figure 1(c)) in Figure 5 is very similar to that of turbostratic graphite (see [24]), but weak peaks of C₇₀ fullerenes (the features observed at about 1450 and 1530 cm⁻¹ (see [25, 26])) are also clearly distinguished (Figure 5). The strong modes of fullerenes C₇₀ at about 1180 and 1568 cm⁻¹ are merged with D” and G bands.

The Raman spectra (Figure 6) taken from points 2 and 3 (Figure 1(c)) are similar as they contain the most prominent modes of C₇₀ peaks at 1160, 1220, 1454, 1526, and 1565 cm⁻¹ [25, 26], nanodiamond (Nd) peaks at 1330 and 1620 cm⁻¹ (see [27]), and turbostratic graphite. The D, G, and D’ bands are found at 1335, 1590, and 1612 cm⁻¹, respectively, but in a different proportion: the spectrum from point 3 is dominated by the peaks of C₇₀ and Nd while the spectrum from point 2 is dominated by turbostratic graphite (see Figure 6). It should be also remarked that features of C₆₀ fullerenes (see, e.g., [25, 26]) were not observed.

Figure 6Raman spectra of the interlayer that remains on the substrate after exfoliation by the Scotch tape method taken from points 2 and 3 (Figure 1(c)).

As it was mentioned above, the D” band overlaps with the second-order band of Si substrate especially when the spectrum is excited at 488 and 514 nm laser wavelengths. In order to distinguish the dispersion of the D” band of several Scotch tape methods, exfoliated flakes were transferred on glass substrates. The flakes have very similar surface morphology to those transferred on SiO₂/Si substrates (Figure 1(d)). The Raman spectrum of such flakes is not significantly different from that of the as-deposited layers (Figure 7); however, the D” band appears at 1096 (for 488 nm excitation) and at 1135 cm⁻¹ (for 633 nm excitation), respectively, that is, they coincide with the data of Herziger et al. [14].

Figure 7The Raman spectra of as-grown graphene on αC excited at 488 nm (green trace) and 633 nm (black trace) wavelengths. The similar spectra of exfoliated graphene transferred on a glass substrate excited at 488 nm (blue trace) and 633 nm (red trace). The inset: magnified part of the region 900–1200 cm⁻¹.

According to the above results, we conclude that the exfoliation by the Scotch tape method does not enable splitting up between the defected graphene and the interlayers (especially the αC-designated one). Another way for exfoliation was probed (by exfoliation on epoxy resin), and the optical micrograph image of the area of the edge of a resin bubble and the Raman spectrum taken from this area (excited at 633 nm) are shown in Figures 1(e) and 8, respectively. The Raman spectrum of epoxy resin does not contain any features in the 2D region of graphene (upper trace in Figure 8); hence, 2D bands of a single- and bilayered graphene were identified at the edge of a lot of bubbles on the surface of the resin (Figure 1(e)). It should be clearly remarked that the measured FWHM of the 2D band of such single-layered graphene is about 27–29 cm⁻¹, but it is situated at 2654–2656 cm⁻¹, that is, it remains upshifted with about 10–15 cm⁻¹.

Figure 8Raman spectra of graphene films situated on air bubbles/cavities. The 2D band is situated at 2655 cm⁻¹ and has FWHM ~28 cm⁻¹ (i.e., it corresponds to single-layered graphene—blue trace).

Recently, Li et al. [28] established that the intensity of 2D band varies as a cosine to the fourth power when the laser propagation direction is parallel to the graphene layer and the polarization is rotated around it. They also derived the orientation distribution function of monolayered graphene as well as that of graphene paper and highly oriented pyrolytic graphite. We perform similar measurements in X(Y_φY_φ)X geometry, φ being the angle between the incident laser beam polarization and the graphene layer plane; Z is the axis perpendicular to the graphene plane, and the laser beam propagates transversely to the graphene layer along the X direction (see Figure 9(a)). The excitation laser beam was focused in a manner to comprise no more than 30% of the edge of the Si substrate and graphene film (Figure 9(a)). The parallel scattering geometry was used. The measurements were performed starting from φ = 0° (corresponding to X(YY)X in Porto notations) and finished at φ = 180°. The preliminary results of these rotational angle-dependent Raman measurements of as-deposited specimen are presented in Figure 9. The signal significantly drops upon changing the angle from 0° to 90° and increases again in the interval between 90 and 180° which resembles indeed the cos⁴ law. At 90° (corresponding to X(ZZ)X in Porto notations), the Raman signal is very weak but still observable (Figure 9), and the rotational angle-independent features of C₇₀ fullerenes and nanodiamond (Nd) dominate the spectrum. The residual features in the Raman spectra taken at φ = 90° point out that the measured polarized Raman spectra are taken from graphene deposited on DLC interlayer. The measurements in this scattering geometry (X(YY)X in Porto notations) access measurements of the interlayer/s without exfoliation. On the other hand, the polarized Raman study confirms the deposition of graphene because the intensities of the most prominent Raman features of graphite (D, G, and 2D bands) show similar behavior in similar conditions as those of graphene. However, the intensity of the Raman features of graphene decreases significantly slower than those of graphene as it is shown in [28].
(a)
(b)
(a)
(b)Figure 9(a) Optical photography of the specimen in X(YY)X geometry (in Porto notations). The inset: optical photography of the specimen in Z(YY)Z geometry (in Porto notations). The arrow-remarked laser spots are eye guide showing the real area of the laser spot during measurements. The marker represents 10 μm. (b) Spatially resolved Raman spectra of as-deposited defected graphene at 633 nm excitation.

It is worth noting that the 2D band from the single-layered graphene regions is symmetric and strong, but it is somewhat broadened with FWHM of about 40–42 cm⁻¹and is blueshifted by 15–20 cm⁻¹ in as-grown specimens. It is well known that such behavior is usually related to strain (see [29–32]) and doping [33]. Moreover, Lee et al. [34] and Bouhafs et al. [35] experimentally studied the influence of these parameters on the position and FWHM of G and 2D bands in single- and bi-/multilayered graphene, respectively. The deduced simple plot of the 2D versus G band positions enables distinguishing the influence of doping and strain on the positions of G and 2D bands. In our single-layered specimens, the G band is slightly uphifted by 1-2 cm⁻¹ while the 2D band is more significantly blueshifted and broadened by 10–20 cm⁻¹. Therefore it can be assumed that the 2D band blueshift and broadening are due to the lattice strain predominantly as well as to the doping. It can be suggested that the lattice strain is due to the bonding between graphene and the interlayers while the doping should be related to charge transfer from the interlayers/interfaces to graphene as well as to different intrinsic (grain boundaries, etc.) and extrinsic (trapped nitrogen, oxygen, and impurities during the deposition) defects, that is, it can be related to the influence of the interlayers/substrates as well as of the deposition process.

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4. Conclusions

We extended the analysis of defected graphene deposited by CVD as well as the two types of interlayers between the defected graphene layer/s and Si substrates by both unpolarized and polarized Raman spectroscopy. The performed Raman spectroscopy examination of as-deposited defected graphene at 488, 514, and 633 nm excitation wavelengths enables the most of the monitored spectral features of double-resonance origin (D, D”, and 2D bands). The Raman studies of exfoliation by the so-called Scotch tape method revealed that (a) the composition of the designated DLC interlayers varies with depth: the initial layers on the Si substrate consist of a mixed phase of turbostratic graphite, nanodiamond/diamond-like carbon, and C₇₀ fullerenes while the upper ones are dominated by diamond-like carbon and some C₇₀ fullerenes and (b) the amorphous carbon interlayer is dominated by turbostratic graphite and contains a larger quantity of C₇₀ than the DLC-designated interlayers. Single- and bilayered defected graphene flakes were exfoliated on epoxy resin. The preliminary results of polarized Raman experiments show that the intensity of the 2D band varies as a cosine to the fourth power when the laser propagation direction is parallel to the graphene layer and the polarization is rotated around it which is an additional indication of the deposition of single-layered graphene. The results of Raman spectroscopic studies of as-grown and exfoliated graphene films tend to assume that the observed slight upshift of the 2D band as well as the broadening of 2D band is due to the strain and can be related to the bonding between the graphene and the interlayers, that is, it could be regarded as an influence of the interlayers between the defected graphene and the Si substrates.

Conflicts of Interest

The authors declare that there is no conflict of interest regarding the publication of this paper.

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Authors: T. I. Milenov,¹ E. Valcheva,² and V. N. Popov²

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Nuclear Magnetic Resonance (NMR) spectroscopy is an incredibly powerful tool for characterizing molecular structures. When submitting to the FDA or other regulatory agencies, full structural characterization by NMR provides crucial evidence of compound identity. A combination of 1-dimensional and 2-dimensional NMR experiments are necessary for complete confidence in chemical structure.

This post will walk you through the steps to fully characterize a molecule by 1- and 2-dimensional NMR, including on how to perform NMR interpretation.

Step 1: ¹H-NMR

The first step in structural characterization is 1-dimensional proton ¹H-NMR. The chemical shift, multiplicity, coupling constants, and integration are all factors to consider when assigning protons. In this example, only three protons can be assigned by the proton spectrum alone: protons 3, 4, and 6.

To begin, let’s start with proton 3. Proton 3 is the only methyl group in the structure, and therefore must integrate to 3 protons. The only peak with an integration of 3 is the doublet at 1.770 ppm. The high field chemical shift supports this assignment. The peak is split into a doublet with a coupling constant of 1.2 Hz, reflecting the long-range coupling between protons 3 and 4, which also supports this assignment.

Protons that are coupled to each other should exhibit the same coupling constant. The long-range coupling constant observed for proton 3 (J=1.2 Hz, split into a doublet by proton 4) is reflected in the coupling constant for proton 4 (J=1.2 Hz, split into a quartet by proton 3). Therefore, the peak at 7.690 ppm must represent proton 4! The integration and chemical shift support the assignment, as proton 4 is the only aromatic proton in the structure.

There is only one singlet in the ¹H-NMR spectrum. The only proton that should show up as a singlet is proton 6, as it has no neighboring protons that would split the peak (the nearest proton is 5 bonds away!). The chemical shift of 11.256 ppm supports this assignment, as imide protons often show up far downfield. The peak also integrates to 1 proton, supporting the assignment.

The remaining protons are doublets, triplets, and multiplets that can be assigned by 2-dimensional COSY.

Step 2: ¹H-¹H COSY

¹H-¹H Correlation Spectroscopy (COSY) shows the correlation between hydrogens which are coupled to each other in the ¹H NMR spectrum. The ¹H spectrum is plotted on both 2D axes. While 2-bond and 3-bond ¹H-¹H coupling is easily visible by COSY, long range coupling can also be observed with long acquisition times. The cross-peaks (not on the diagonal) that are symmetric to the diagonal show the COSY correlations. For example, protons 3 and 4 are coupled to each other, since they form a box pattern symmetric to the diagonal. This confirms assignments 3 and 4 made from the proton spectrum alone.

Two types of COSY coupling: 3-bond short range coupling between protons 7 and 8 (red) and 4-bond long range coupling between protons 3 and 4 (blue).

My favorite way to analyze a COSY spectrum with many unassigned protons is to make a table of correlations, like the one seen here. Look at the table for any clear differences in correlation and begin there! In this example, all unassigned protons show one or two COSY correlations-except the proton at 4.233 ppm, which correlates to threeother protons by COSY. The only proton expected to correlate with three nonequivalent protons is proton 9!

Now that proton 9 has been assigned, the fun really begins. Thymidine’s structure suggests that proton 9 should couple protons 8, 10, and 11. Based on the COSY, proton 9 couples protons at 2.068 ppm (2H), 3.754 ppm (1H), and 5.209 ppm (1H). From this list, we can easily assign proton 8 as the peak at 2.068 ppm based on its integration of 2 protons. To differentiate protons 10 and 11, take a look at our COSY table; 3.754 ppm shows two COSY correlations, while 5.209 ppm only shows one. Therefore, we can assign proton 10 as 5.209 ppm and proton 11 as 3.754 ppm.

Once proton 8 has been assigned, we can easily assign proton 7 based on the remaining COSY correlation for proton 8. Proton 7’s peak at 6.163 ppm is split into a triplet by the two 8 protons, confirming the assignment.

All that remains are protons 12 and 13. We can assign proton 12 (3.564 ppm) based on its integration of 2H and its COSY correlation to proton 11. The last remaining peak at 4.999 ppm must be proton 13; this is confirmed by COSY correlation with proton 12, triplet multiplicity based on splitting by proton 12, and integration of one proton.

Now we have a fully assigned ¹H-NMR spectrum! This spectrum will help us assign our carbons using HSQC and HMBC NMR spectroscopy.

Step 3: ¹³C-NMR

Carbon NMR is a necessary step in full structural characterization. However, ¹³C-NMR alone does not provide enough information to assign the carbons in the molecule. The NMR spectrum below does confirm the number of carbons in the molecule; however, HSQC and HMBC (we will get to these soon!) are necessary to assign the carbons with confidence. Note that one of the carbons is hidden beneath the solvent signal but is clearly visible after zooming into that region.

Step 4: DEPT-45, 90, and 135

Distortionless Enhancement of Polarization Transfer (DEPT) experiments help assign carbon peaks by determining the number of protons attached to each carbon. For very simple molecules, DEPT may be enough to partially or fully assign all carbons. In complex molecules, DEPT and HSQC together are useful for confirming both carbon and proton assignments. For example, the DEPT experiments below can only identify carbon 3-it is the only CH₃ peak. I always go back and use DEPT to confirm the carbons I assigned by HSQC.

• DEPT-45 shows CH, CH₂, and CH₃ carbons as positive peaks. Carbons with no protons are not visible.
• DEPT-90 shows only CH peaks as positive peaks. Carbons with no protons, CH₂, and CH₃ carbons are not visible.
• DEPT-135 shows CH and CH₃ carbons as positive peaks and CH₂ carbons as negative peaks. Carbons with no protons are not visible.

Step 5: ¹H-¹³C HSQC

¹H-¹³C Heteronuclear Single Quantum Coherence Spectroscopy (HSQC) shows which hydrogens are directly attached to which carbon atoms. The ¹H spectrum is shown on the horizontal axis and the ¹³C spectrum is shown on the vertical axis. The HSQC spectrum is most valuable when protons have already been assigned.

For example, HSQC shows a correlation between proton 4 and the carbon at 136.113 ppm; this carbon is now assigned as carbon 4. Carbons 3, 4, 7, 8, 9, 11, and 12 are assigned by HSQC. Only 1-bond correlations are observed, so HSQC assignments are relatively straightforward. The DEPT experiments also confirm these assignments. HSQC is also useful in confirming proton assignments of nitrogen or oxygen-bound protons; they show no signal by HSQC. This further supports the assignments of protons 6, 10, and 13.

An example correlation between proton and carbon 4 is observed by HSQC.

Step 6: ¹H-¹³C HMBC

¹H-¹³C Heteronuclear Multiple Bond Correlation Spectroscopy (HMBC) shows the correlations between protons and carbons that are separated by multiple bonds. The ¹H spectrum is shown on the horizontal axis and the ¹³C spectrum is shown on the vertical axis. Correlated atoms are shown in blue and the connecting atoms are shown in red. Note that direct hydrogen-carbon bonds (1-bond correlations) are generally not seen. For example, hydrogen 4 shows correlations with carbons 1, 2, 3, 5, and 7, but not carbon 4.

HMBC interactions between proton 4 and carbons 1, 2, 3, 5, and 7.

HMBC is incredibly useful for assigning carbons that have no protons attached. In this example, carbons 1, 2, and 5 have no protons attached. Carbon 1 is assigned by HMBC interactions with protons 3, 4, and 6; carbon 2 by interaction with protons 3, 4, 6, and 7; and carbon 5 by interactions with protons 4 and 7 only. The chemical environment of carbon 5 suggests it would appear more downfield than carbon 1, which confirms these assignments.

HMBC also confirms assignments that were based solely on the proton and COSY spectrum. For example, protons 10 and 13 are differentiated by HMBC; proton 10 is confirmed by interactions with carbons 8, 9, and 11, while proton 13 is confirmed by interactions with 11 and 12. HMBC supports all proton and all carbon assignments, unambiguously confirming both the structure and analysis of thymidine.

At Emery Pharma, we are experts in 1D and 2D NMR characterization and structure elucidation; in fact, 2D NMR projects are some of our favorites! We have supported numerous pharmaceutical companies in full NMR characterization for API submissions to regulatory agencies, as well as complete structure elucidation of impurities. We provide a fully annotated report with images similar to those seen here and support our results with high resolution mass spectrometry and elemental analysis. 

Some nuclei rotate around their axis like electrons. In the presence of an external magnetic field, a rotating nucleus has only a small number of stable orientations. Nuclear magnetic resonance (NMR) occurs when a spinning core is excited from a lower energy orientation to a higher energy orientation in the presence of a magnetic field by absorbing enough electromagnetic radiation. Nuclear magnetic resonance spectroscopy involves measuring the amount of energy required to change spin nuclei from a stable orientation to a more unstable orientation in a magnetic field. Because spin-core nuclei change direction in a magnetic field at different frequencies, different frequencies of absorbing radiation are needed to change the orientation of spin-core nuclei. The frequency at which the absorption takes place is used for analysis and spectroscopy [1].

Nuclear magnetic resonance was first discovered independently in 1946 by Felix Bloch of Stanford University and Edward Parcel of Harvard University. They were able to show the absorption of electromagnetic radiation as a result of the transfer of the energy level of the nucleus in a strong magnetic field. The two physicists won the Nobel Prize in 1952 for their work. In the first five years after the discovery of the nuclear magnetic resonance method, chemists discovered that the molecular environment of objects affects the absorption of radiation by nuclei in the presence of a magnetic field, and this effect could be related to the structure of the molecule. Since then, the growth of magnetic resonance spectroscopy has been explosive and this method has had a significant effect on the development of organic chemistry, inorganic chemistry and biochemistry [2]. In 1999, a team of Canadian physicists developed a new method using the Beta Nuclear Magnetic Resonance Method, which is capable of demonstrating the magnetic and electrical properties of very thin layers and surfaces. BetaNMR methods are used in nanoscience. Be [3].

The magnitude of the spin angle motion in the nuclei is determined by the quantum number of the nucleus spin. Quantum number The core spin of any number can be integer or semi-integer. In 16 O and 12C non-spin nuclei, the quantum spin number of the nucleus is zero. Cores that are not spin and therefore do not have the magnitude of the spin angle motion can not be detected by NMR spectroscopy. Spin-core cores with spherical charge distribution have a spin quantum number of 1/2. Examples of these nuclei include 13C, 19F, 3H, 15N, 31P and 1H, which have a quantum number of 1/2 and a magnetic moment. In order for a nucleus in a magnetic field to absorb a large amount of electromagnetic radiation, it must have a high frequency in the sample and also have a relatively large magnetic moment (µ). Cores that have both properties in question include 1H, 19F, 21P. Most NMR measurements are usually performed for 1 h. Measurements of other nuclei are often performed using signal amplification methods to observe the spectrum. Usually, among the nuclei with low relative frequency that show the magnetic resonance of the nucleus, 12 C, 15N, 16O are the most important for chemists. The magnetic resonance method of the hydrogen nucleus (1H), which is used more than other nuclei, has a magnetic torque of about 79.2 برای. It will be magnetic. For other cores used for nuclear magnetic resonance spectroscopy, the magnetic torque for 21P, 19F 12C is 6873.2, 1305.1 and 0.7022, respectively [4]. In most cases, the sensitivity of non-proton core magnetic resonance devices, such as 12C, etc., is lower than that of HNMR. Also, in most compounds, the natural abundance of non-proton magnetic nuclei is significantly lower than that of protons. This factor causes the NMR spectra of non-proton nuclei to have a relatively low noise signal. The peaks of these spectra are small, and often the spectrum cannot be determined if the same device used for proton nucleus (PMR) NMR is used. Due to the low signal-to-noise ratio in these cases, most devices designed to record the NMR spectra of non-proton nuclei use multiple traverses with signal averaging techniques. The most common devices for spectral peak extraction use the Fourier transform. Fourier transformers are also used to prepare PMR spectra of dilute solutions and complex molecules, such as proteins, in which the amount of a particular proton in the molecule is small. The difference between PMR spectra and other NMR spectra is in the range of chemical displacement. The chemical displacement range for PMR is 10PPM in most cases. While for the 12C core the chemical displacement is up to about 200PPM, for the 19F and 21P spectra it is 300 and 400PPM, respectively. In NMR methods, the units used are usually time (seconds), angle (degrees or radians), temperature (Kelvin), magnetic field strength (Tesla, T), energy (joules), vibration (rpm) and power ( Watts) is. [5] Components of the NMR Device The important components of an NMR spectrometer are shown schematically in Figure (1). A brief description of each component is given below.

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Over the past fifty years nuclear magnetic resonance spectroscopy, commonly referred to as nmr, has become the preeminent technique for determining the structure of organic compounds. Of all the spectroscopic methods, it is the only one for which a complete analysis and interpretation of the entire spectrum is normally expected. Although larger amounts of sample are needed than for mass spectroscopy, nmr is non-destructive, and with modern instruments good data may be obtained from samples weighing less than a milligram. To be successful in using nmr as an analytical tool, it is necessary to understand the physical principles on which the methods are based.

The nuclei of many elemental isotopes have a characteristic spin (I). Some nuclei have integral spins (e.g. I = 1, 2, 3 ….), some have fractional spins (e.g. I = 1/2, 3/2, 5/2 ….), and a few have no spin, I = 0 (e.g. ¹²C, ¹⁶O, ³²S, ….). Isotopes of particular interest and use to organic chemists are ¹H, ¹³C, ¹⁹F and ³¹P, all of which have I = 1/2. Since the analysis of this spin state is fairly straightforward, our discussion of nmr will be limited to these and other I = 1/2 nuclei.

The following features lead to the nmr phenomenon:

1. A spinning charge generates a magnetic field, as shown by the animation on the right. The resulting spin-magnet has a magnetic moment (μ) proportional to the spin.
2. In the presence of an external magnetic field (B0), two spin states exist, +1/2 and -1/2. The magnetic moment of the lower energy +1/2 state is aligned with the external field, but that of the higher energy -1/2 spin state is opposed to the external field. Note that the arrow representing the external field points North.
3. The difference in energy between the two spin states is dependent on the external magnetic field strength, and is always very small. The following diagram illustrates that the two spin states have the same energy when the external field is zero, but diverge as the field increases. At a field equal to Bx a formula for the energy difference is given (remember I = 1/2 and μ is the magnetic moment of the nucleus in the field).
Strong magnetic fields are necessary for nmr spectroscopy. The international unit for magnetic flux is the tesla (T). The earth’s magnetic field is not constant, but is approximately 10-4 T at ground level. Modern nmr spectrometers use powerful magnets having fields of 1 to 20 T. Even with these high fields, the energy difference between the two spin states is less than 0.1 cal/mole. To put this in perspective, recall that infrared transitions involve 1 to 10 kcal/mole and electronic transitions are nearly 100 time greater. For nmr purposes, this small energy difference (ΔE) is usually given as a frequency in units of MHz (106 Hz), ranging from 20 to 900 Mz, depending on the magnetic field strength and the specific nucleus being studied. Irradiation of a sample with radio frequency (rf) energy corresponding exactly to the spin state separation of a specific set of nuclei will cause excitation of those nuclei in the +1/2 state to the higher -1/2 spin state. Note that this electromagnetic radiation falls in the radio and television broadcast spectrum. Nmr spectroscopy is therefore the energetically mildest probe used to examine the structure of molecules.  The nucleus of a hydrogen atom (the proton) has a magnetic moment μ = 2.7927, and has been studied more than any other nucleus. The previous diagram may be changed to display energy differences for the proton spin states (as frequencies) by mouse clicking anywhere within it.
4. For spin 1/2 nuclei the energy difference between the two spin states at a given magnetic field strength will be proportional to their magnetic moments. For the four common nuclei noted above, the magnetic moments are: 1H μ = 2.7927, 19F μ = 2.6273, 31P μ = 1.1305 & 13C μ = 0.7022. These moments are in nuclear magnetons, which are 5.05078•10-27 JT-1. The following diagram gives the approximate frequencies that correspond to the spin state energy separations for each of these nuclei in an external magnetic field of 2.35 T. The formula in the colored box shows the direct correlation of frequency (energy difference) with magnetic moment (h = Planck’s constant = 6.626069•10-34 Js).

      2. Proton NMR Spectroscopy
This important and well-established application of nuclear magnetic resonance will serve to illustrate some of the novel aspects of this method. To begin with, the nmr spectrometer must be tuned to a specific nucleus, in this case the proton. The actual procedure for obtaining the spectrum varies, but the simplest is referred to as the continuous wave (CW) method. A typical CW-spectrometer is shown in the following diagram. A solution of the sample in a uniform 5 mm glass tube is oriented between the poles of a powerful magnet, and is spun to average any magnetic field variations, as well as tube imperfections. Radio frequency radiation of appropriate energy is broadcast into the sample from an antenna coil (colored red). A receiver coil surrounds the sample tube, and emission of absorbed rf energy is monitored by dedicated electronic devices and a computer. An nmr spectrum is acquired by varying or sweeping the magnetic field over a small range while observing the rf signal from the sample. An equally effective technique is to vary the frequency of the rf radiation while holding the external field constant.

As an example, consider a sample of water in a 2.3487 T external magnetic field, irradiated by 100 MHz radiation. If the magnetic field is smoothly increased to 2.3488 T, the hydrogen nuclei of the water molecules will at some point absorb rf energy and a resonance signal will appear. An animation showing this may be activated by clicking the Show Field Sweep button. The field sweep will be repeated three times, and the resulting resonance trace is colored red. For visibility, the water proton signal displayed in the animation is much broader than it would be in an actual experiment.

Since protons all have the same magnetic moment, we might expect all hydrogen atoms to give resonance signals at the same field / frequency values. Fortunately for chemistry applications, this is not true. By clicking the Show Different Protons button under the diagram, a number of representative proton signals will be displayed over the same magnetic field range. It is not possible, of course, to examine isolated protons in the spectrometer described above; but from independent measurement and calculation it has been determined that a naked proton would resonate at a lower field strength than the nuclei of covalently bonded hydrogens. With the exception of water, chloroform and sulfuric acid, which are examined as liquids, all the other compounds are measured as gases.

Why should the proton nuclei in different compounds behave differently in the nmr experiment ? 
The answer to this question lies with the electron(s) surrounding the proton in covalent compounds and ions. Since electrons are charged particles, they move in response to the external magnetic field (B_o) so as to generate a secondary field that opposes the much stronger applied field. This secondary field shields the nucleus from the applied field, so B_o must be increased in order to achieve resonance (absorption of rf energy). As illustrated in the drawing on the right, B_o must be increased to compensate for the induced shielding field. In the upper diagram, those compounds that give resonance signals at the higher field side of the diagram (CH₄, HCl, HBr and HI) have proton nuclei that are more shielded than those on the lower field (left) side of the diagram. 
The magnetic field range displayed in the above diagram is very small compared with the actual field strength (only about 0.0042%). It is customary to refer to small increments such as this in units of parts per million (ppm). The difference between 2.3487 T and 2.3488 T is therefore about 42 ppm. Instead of designating a range of nmr signals in terms of magnetic field differences (as above), it is more common to use a frequency scale, even though the spectrometer may operate by sweeping the magnetic field. Using this terminology, we would find that at 2.34 T the proton signals shown above extend over a 4,200 Hz range (for a 100 MHz rf frequency, 42 ppm is 4,200 Hz). Most organic compounds exhibit proton resonances that fall within a 12 ppm range (the shaded area), and it is therefore necessary to use very sensitive and precise spectrometers to resolve structurally distinct sets of hydrogen atoms within this narrow range. In this respect it might be noted that the detection of a part-per-million difference is equivalent to detecting a 1 millimeter difference in distances of 1 kilometer.

Chemical Shift

Unlike infrared and uv-visible spectroscopy, where absorption peaks are uniquely located by a frequency or wavelength, the location of different nmr resonance signals is dependent on both the external magnetic field strength and the rf frequency. Since no two magnets will have exactly the same field, resonance frequencies will vary accordingly and an alternative method for characterizing and specifying the location of nmr signals is needed. This problem is illustrated by the eleven different compounds shown in the following diagram. Although the eleven resonance signals are distinct and well separated, an unambiguous numerical locator cannot be directly assigned to each.

One method of solving this problem is to report the location of an nmr signal in a spectrum relative to a reference signal from a standard compound added to the sample. Such a reference standard should be chemically unreactive, and easily removed from the sample after the measurement. Also, it should give a single sharp nmr signal that does not interfere with the resonances normally observed for organic compounds. Tetramethylsilane, (CH₃)₄Si, usually referred to as TMS, meets all these characteristics, and has become the reference compound of choice for proton and carbon nmr.
Since the separation (or dispersion) of nmr signals is magnetic field dependent, one additional step must be taken in order to provide an unambiguous location unit. This is illustrated for the acetone, methylene chloride and benzene signals by clicking on the previous diagram. To correct these frequency differences for their field dependence, we divide them by the spectrometer frequency (100 or 500 MHz in the example), as shown in a new display by again clicking on the diagram. The resulting number would be very small, since we are dividing Hz by MHz, so it is multiplied by a million, as shown by the formula in the blue shaded box. Note that ν_ref is the resonant frequency of the reference signal and ν_samp is the frequency of the sample signal. This operation gives a locator number called the Chemical Shift, having units of parts-per-million (ppm), and designated by the symbol δ   Chemical shifts for all the compounds in the original display will be presented by a third click on the diagram.

The compounds referred to above share two common characteristics:

• The hydrogen atoms in a given molecule are all structurally equivalent, averaged for fast conformational equilibria. 
• The compounds are all liquids, save for neopentane which boils at 9 °C and is a liquid in an ice bath.

The first feature assures that each compound gives a single sharp resonance signal. The second allows the pure (neat) substance to be poured into a sample tube and examined in a nmr spectrometer. In order to take the nmr spectra of a solid, it is usually necessary to dissolve it in a suitable solvent. Early studies used carbon tetrachloride for this purpose, since it has no hydrogen that could introduce an interfering signal. Unfortunately, CCl₄ is a poor solvent for many polar compounds and is also toxic. Deuterium labeled compounds, such as deuterium oxide (D₂O), chloroform-d (DCCl₃), benzene-d₆(C₆D₆), acetone-d₆ (CD₃COCD₃) and DMSO-d₆ (CD₃SOCD₃) are now widely used as nmr solvents. Since the deuterium isotope of hydrogen has a different magnetic moment and spin, it is invisible in a spectrometer tuned to protons.

From the previous discussion and examples we may deduce that one factor contributing to chemical shift differences in proton resonance is the inductive effect. If the electron density about a proton nucleus is relatively high, the induced field due to electron motions will be stronger than if the electron density is relatively low. The shielding effect in such high electron density cases will therefore be larger, and a higher external field (B_o) will be needed for the rf energy to excite the nuclear spin. Since silicon is less electronegative than carbon, the electron density about the methyl hydrogens in tetramethylsilane is expected to be greater than the electron density about the methyl hydrogens in neopentane (2,2-dimethylpropane), and the characteristic resonance signal from the silane derivative does indeed lie at a higher magnetic field. Such nuclei are said to be shielded. Elements that are more electronegative than carbon should exert an opposite effect (reduce the electron density); and, as the data in the following tables show, methyl groups bonded to such elements display lower field signals (they are deshielded). The deshielding effect of electron withdrawing groups is roughly proportional to their electronegativity, as shown by the left table. Furthermore, if more than one such group is present, the deshielding is additive (table on the right), and proton resonance is shifted even further downfield.

The general distribution of proton chemical shifts associated with different functional groups is summarized in the following chart. Bear in mind that these ranges are approximate, and may not encompass all compounds of a given class. Note also that the ranges specified for OH and NH protons (colored orange) are wider than those for most CH protons. This is due to hydrogen bonding variations at different sample concentrations.

Proton Chemical Shift Ranges*
Low Field Region		High Field Region
	* For samples in CDCl3 solution. The δ scale is relative to TMS at δ = 0.

To make use of a calculator that predicts aliphatic proton chemical shifts Click Here. This application was developed at Colby College.

Signal Strength

The magnitude or intensity of nmr resonance signals is displayed along the vertical axis of a spectrum, and is proportional to the molar concentration of the sample. Thus, a small or dilute sample will give a weak signal, and doubling or tripling the sample concentration increases the signal strength proportionally. If we take the nmr spectrum of equal molar amounts of benzene and cyclohexane in carbon tetrachloride solution, the resonance signal from cyclohexane will be twice as intense as that from benzene because cyclohexane has twice as many hydrogens per molecule. This is an important relationship when samples incorporating two or more different sets of hydrogen atoms are examined, since it allows the ratio of hydrogen atoms in each distinct set to be determined. To this end it is necessary to measure the relative strength as well as the chemical shift of the resonance signals that comprise an nmr spectrum. Two common methods of displaying the integrated intensities associated with a spectrum are illustrated by the following examples. In the three spectra in the top row, a horizontal integrator trace (light green) rises as it crosses each signal by a distance proportional to the signal strength. Alternatively, an arbitrary number, selected by the instrument’s computer to reflect the signal strength, is printed below each resonance peak, as shown in the three spectra in the lower row. From the relative intensities shown here, together with the previously noted chemical shift correlations, the reader should be able to assign the signals in these spectra to the set of hydrogens that generates each. If you click on one of the spectrum signals (colored red) or on hydrogen atom(s) in the structural formulas the spectrum will be enlarged and the relationship will be colored blue.
Hint: When evaluating relative signal strengths, it is useful to set the smallest integration to unity and convert the other values proportionally.

Hydroxyl Proton Exchange and the Influence of Hydrogen Bonding

The last two compounds in the lower row are alcohols. The OH proton signal is seen at 2.37 δ in 2-methyl-3-butyne-2-ol, and at 3.87 δ in 4-hydroxy-4-methyl-2-pentanone, illustrating the wide range over which this chemical shift may be found. A six-membered ring intramolecular hydrogen bond in the latter compound is in part responsible for its low field shift, and will be shown by clicking on the hydroxyl proton. We can take advantage of rapid OH exchange with the deuterium of heavy water to assign hydroxyl proton resonance signals . As shown in the following equation, this removes the hydroxyl proton from the sample and its resonance signal in the nmr spectrum disappears. Experimentally, one simply adds a drop of heavy water to a chloroform-d solution of the compound and runs the spectrum again. The result of this exchange is displayed below.

R-O-H   +   D2O      R-O-D   +   D-O-H

Hydrogen bonding shifts the resonance signal of a proton to lower field ( higher frequency ). Numerous experimental observations support this statement, and a few of these will be described here.

i)   The chemical shift of the hydroxyl hydrogen of an alcohol varies with concentration. Very dilute solutions of 2-methyl-2-propanol, (CH3)3COH, in carbon tetrachloride solution display a hydroxyl resonance signal having a relatively high-field chemical shift (< 1.0 δ ). In concentrated solution this signal shifts to a lower field, usually near 2.5 δ.
ii)   The more acidic hydroxyl group of phenol generates a lower-field resonance signal, which shows a similar concentration dependence to that of alcohols. OH resonance signals for different percent concentrations of phenol in chloroform-d are shown in the following diagram (C-H signals are not shown).
iii)   Because of their favored hydrogen-bonded dimeric association, the hydroxyl proton of carboxylic acids displays a resonance signal significantly down-field of other functions. For a typical acid it appears from 10.0 to 13.0 δ and is often broader than other signals. The spectra shown below for chloroacetic acid (left) and 3,5-dimethylbenzoic acid (right) are examples.
iv)   Intramolecular hydrogen bonds, especially those defining a six-membered ring, generally display a very low-field proton resonance. The case of 4-hydroxypent-3-ene-2-one (the enol tautomer of 2,4-pentanedione) not only illustrates this characteristic, but also provides an instructive example of the sensitivity of the nmr experiment to dynamic change. In the nmr spectrum of the pure liquid, sharp signals from both the keto and enol tautomers are seen, their mole ratio being 4 : 21 (keto tautomer signals are colored purple). Chemical shift assignments for these signals are shown in the shaded box above the spectrum. The chemical shift of the hydrogen-bonded hydroxyl proton is δ 14.5, exceptionally downfield. We conclude, therefore, that the rate at which these tautomers interconvert is slow compared with the inherent time scale of nmr spectroscopy.
Two structurally equivalent structures may be drawn for the enol tautomer (in magenta brackets). If these enols were slow to interconvert, we would expect to see two methyl resonance signals associated with each, one from the allylic methyl and one from the methyl ketone. Since only one strong methyl signal is observed, we must conclude that the interconversion of the enols is very fast-so fast that the nmr experiment detects only a single time-averaged methyl group (50% α-keto and 50% allyl).

Although hydroxyl protons have been the focus of this discussion, it should be noted that corresponding N-H groups in amines and amides also exhibit hydrogen bonding nmr shifts, although to a lesser degree. Furthermore, OH and NH groups can undergo rapid proton exchange with each other; so if two or more such groups are present in a molecule, the nmr spectrum will show a single signal at an average chemical shift. For example, 2-hydroxy-2-methylpropanoic acid, (CH₃)₂C(OH)CO₂H, displays a strong methyl signal at δ 1.5 and a 1/3 weaker and broader OH signal at δ 7.3 ppm. Note that the average of the expected carboxylic acid signal (ca. 12 ) and the alcohol signal (ca. 2 ) is 7. Rapid exchange of these hydrogens with heavy water, as noted above, would cause the low field signal to disappear.

π-Electron Functions

An examination of the proton chemical shift chart (above) makes it clear that the inductive effect of substituents cannot account for all the differences in proton signals. In particular the low field resonance of hydrogens bonded to double bond or aromatic ring carbons is puzzling, as is the very low field signal from aldehyde hydrogens. The hydrogen atom of a terminal alkyne, in contrast, appears at a relatively higher field. All these anomalous cases seem to involve hydrogens bonded to pi-electron systems, and an explanation may be found in the way these pi-electrons interact with the applied magnetic field.
Pi-electrons are more polarizable than are sigma-bond electrons, as addition reactions of electrophilic reagents to alkenes testify. Therefore, we should not be surprised to find that field induced pi-electron movement produces strong secondary fields that perturb nearby nuclei. The pi-electrons associated with a benzene ring provide a striking example of this phenomenon, as shown below. The electron cloud above and below the plane of the ring circulates in reaction to the external field so as to generate an opposing field at the center of the ring and a supporting field at the edge of the ring. This kind of spatial variation is called anisotropy, and it is common to nonspherical distributions of electrons, as are found in all the functions mentioned above. Regions in which the induced field supports or adds to the external field are said to be deshielded, because a slightly weaker external field will bring about resonance for nuclei in such areas. However, regions in which the induced field opposes the external field are termed shielded because an increase in the applied field is needed for resonance. Shielded regions are designated by a plus sign, and deshielded regions by a negative sign. 
The anisotropy of some important unsaturated functions will be displayed by clicking on the benzene diagram below. Note that the anisotropy about the triple bond nicely accounts for the relatively high field chemical shift of ethynyl hydrogens. The shielding & deshielding regions about the carbonyl group have been described in two ways, which alternate in the display.

Sigma bonding electrons also have a less pronounced, but observable, anisotropic influence on nearby nuclei. This is seen in the small deshielding shift that occurs in the series CH₃–R, R–CH₂–R, R₃CH; as well as the deshielding of equatorial versus axial protons on a fixed cyclohexane ring.

Solvent Effects

Chloroform-d (CDCl₃) is the most common solvent for nmr measurements, thanks to its good solubilizing character and relative unreactive nature ( except for 1º and 2º-amines). As noted earlier, other deuterium labeled compounds, such as deuterium oxide (D₂O), benzene-d6 (C₆D₆), acetone-d6 (CD₃COCD₃) and DMSO-d6 (CD₃SOCD₃) are also available for use as nmr solvents. Because some of these solvents have π-electron functions and/or may serve as hydrogen bonding partners, the chemical shifts of different groups of protons may change depending on the solvent being used. The following table gives a few examples, obtained with dilute solutions at 300 MHz.

For most of the above resonance signals and solvents the changes are minor, being on the order of ±0.1 ppm. However, two cases result in more extreme changes and these have provided useful applications in structure determination. First, spectra taken in benzene-d₆ generally show small upfield shifts of most C–H signals, but in the case of acetone this shift is about five times larger than normal. Further study has shown that carbonyl groups form weak π–π collision complexes with benzene rings, that persist long enough to exert a significant shielding influence on nearby groups. In the case of substituted cyclohexanones, axial α-methyl groups are shifted upfield by 0.2 to 0.3 ppm; whereas equatorial methyls are slightly deshielded (shift downfield by about 0.05 ppm). These changes are all relative to the corresponding chloroform spectra.
The second noteworthy change is seen in the spectrum of tert-butanol in DMSO, where the hydroxyl proton is shifted 2.5 ppm down-field from where it is found in dilute chloroform solution. This is due to strong hydrogen bonding of the alcohol O–H to the sulfoxide oxygen, which not only de-shields the hydroxyl proton, but secures it from very rapid exchange reactions that prevent the display of spin-spin splitting. Similar but weaker hydrogen bonds are formed to the carbonyl oxygen of acetone and the nitrogen of acetonitrile. A useful application of this phenomenon is described elsewhere in this text.

Spin-Spin Interactions

The nmr spectrum of 1,1-dichloroethane (below right) is more complicated than we might have expected from the previous examples. Unlike its 1,2-dichloro-isomer (below left), which displays a single resonance signal from the four structurally equivalent hydrogens, the two signals from the different hydrogens are split into close groupings of two or more resonances. This is a common feature in the spectra of compounds having different sets of hydrogen atoms bonded to adjacent carbon atoms. The signal splitting in proton spectra is usually small, ranging from fractions of a Hz to as much as 18 Hz, and is designated as J (referred to as the coupling constant). In the 1,1-dichloroethane example all the coupling constants are 6.0 Hz, as illustrated by clicking on the spectrum.

1,2-dichloroethane		1,1-dichloroethane

The splitting patterns found in various spectra are easily recognized, provided the chemical shifts of the different sets of hydrogen that generate the signals differ by two or more ppm. The patterns are symmetrically distributed on both sides of the proton chemical shift, and the central lines are always stronger than the outer lines. The most commonly observed patterns have been given descriptive names, such as doublet (two equal intensity signals), triplet (three signals with an intensity ratio of 1:2:1) and quartet (a set of four signals with intensities of 1:3:3:1). Four such patterns are displayed in the following illustration. The line separation is always constant within a given multiplet, and is called the coupling constant (J). The magnitude of J, usually given in units of Hz, is magnetic field independent.

The splitting patterns shown above display the ideal or “First-Order” arrangement of lines. This is usually observed if the spin-coupled nuclei have very different chemical shifts (i.e. Δν is large compared to J). If the coupled nuclei have similar chemical shifts, the splitting patterns are distorted (second order behavior). In fact, signal splitting disappears if the chemical shifts are the same. Two examples that exhibit minor 2nd order distortion are shown below (both are taken at a frequency of 90 MHz). The ethyl acetate spectrum on the left displays the typical quartet and triplet of a substituted ethyl group. The spectrum of 1,3-dichloropropane on the right demonstrates that equivalent sets of hydrogens may combine their influence on a second, symmetrically located set. 
Even though the chemical shift difference between the A and B protons in the 1,3-dichloroethane spectrum is fairly large (140 Hz) compared with the coupling constant (6.2 Hz), some distortion of the splitting patterns is evident. The line intensities closest to the chemical shift of the coupled partner are enhanced. Thus the B set triplet lines closest to A are increased, and the A quintet lines nearest B are likewise stronger. A smaller distortion of this kind is visible for the A and C couplings in the ethyl acetate spectrum.

What causes this signal splitting, and what useful information can be obtained from it ? 
If an atom under examination is perturbed or influenced by a nearby nuclear spin (or set of spins), the observed nucleus responds to such influences, and its response is manifested in its resonance signal. This spin-coupling is transmitted through the connecting bonds, and it functions in both directions. Thus, when the perturbing nucleus becomes the observed nucleus, it also exhibits signal splitting with the same J. For spin-coupling to be observed, the sets of interacting nuclei must be bonded in relatively close proximity (e.g. vicinal and geminal locations), or be oriented in certain optimal and rigid configurations. Some spectroscopists place a number before the symbol J to designate the number of bonds linking the coupled nuclei (colored orange below). Using this terminology, a vicinal coupling constant is ³J and a geminal constant is ²J.

The following general rules summarize important requirements and characteristics for spin 1/2 nuclei :

1)   Nuclei having the same chemical shift (called isochronous) do not exhibit spin-splitting. They may actually be spin-coupled, but the splitting cannot be observed directly.
2)   Nuclei separated by three or fewer bonds (e.g. vicinal and geminal nuclei ) will usually be spin-coupled and will show mutual spin-splitting of the resonance signals (same J’s), provided they have different chemical shifts. Longer-range coupling may be observed in molecules having rigid configurations of atoms.
3)   The magnitude of the observed spin-splitting depends on many factors and is given by the coupling constant J (units of Hz). J is the same for both partners in a spin-splitting interaction and is independent of the external magnetic field strength.
4)   The splitting pattern of a given nucleus (or set of equivalent nuclei) can be predicted by the n+1 rule, where n is the number of neighboring spin-coupled nuclei with the same (or very similar) Js. If there are 2 neighboring, spin-coupled, nuclei the observed signal is a triplet ( 2+1=3 ); if there are three spin-coupled neighbors the signal is a quartet ( 3+1=4 ). In all cases the central line(s) of the splitting pattern are stronger than those on the periphery. The intensity ratio of these lines is given by the numbers in Pascal’s triangle. Thus a doublet has 1:1 or equal intensities, a triplet has an intensity ratio of 1:2:1, a quartet 1:3:3:1 etc. To see how the numbers in Pascal’s triangle are related to the Fibonacci series click on the diagram.

If a given nucleus is spin-coupled to two or more sets of neighboring nuclei by different J values, the n+1 rule does not predict the entire splitting pattern. Instead, the splitting due to one J set is added to that expected from the other J sets. Bear in mind that there may be fortuitous coincidence of some lines if a smaller J is a factor of a larger J.

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Spin 1/2 nuclei include ¹H, ¹³C, ¹⁹F & ³¹P. The spin-coupling interactions described above may occur between similar or dissimilar nuclei. If, for example, a ¹⁹F is spin-coupled to a ¹H, both nuclei will appear as doublets having the same J constant.  Spin coupling with nuclei having spin other than 1/2 is more complex and will not be discussed here.

To make use of a calculator that predicts first order splitting patterns Click Here. This application was developed at Colby College.

Some Examples

Test your ability to interpret ¹H nmr spectra by analyzing the seven examples presented below. The seven spectra may be examined in turn by clicking the “Toggle Spectra” button. Try to associate each spectrum with a plausible structural formula. 
Although the first four cases are relatively simple, keep in mind that the integration values provide ratios, not absolute numbers. In two cases additional information from infrared spectroscopy is provided. When you have made an assignment you may check your answer by clicking on the spectrum itself. In the sixth example, a similar constitutional isomer cannot be ruled out by the data given.

      3. Carbon NMR Spectroscopy
The power and usefulness of ¹H nmr spectroscopy as a tool for structural analysis should be evident from the past discussion. Unfortunately, when significant portions of a molecule lack C-H bonds, no information is forthcoming. Examples include polychlorinated compounds such as chlordane, polycarbonyl compounds such as croconic acid, and compounds incorporating triple bonds (structures below, orange colored carbons).

Even when numerous C-H groups are present, an unambiguous interpretation of a proton nmr spectrum may not be possible. The following diagram depicts three pairs of isomers (A & B) which display similar proton nmr spectra. Although a careful determination of chemical shifts should permit the first pair of compounds (blue box) to be distinguished, the second and third cases (red & green boxes) might be difficult to identify by proton nmr alone.

These difficulties would be largely resolved if the carbon atoms of a molecule could be probed by nmr in the same fashion as the hydrogen atoms. Since the major isotope of carbon (¹²C) has no spin, this option seems unrealistic. Fortunately, 1.1% of elemental carbon is the ¹³C isotope, which has a spin I = 1/2, so in principle it should be possible to conduct a carbon nmr experiment. It is worth noting here, that if much higher abundances of ¹³C were naturally present in all carbon compounds, proton nmr would become much more complicated due to large one-bond coupling of ¹³C and ¹H.

The most important operational technique that has led to successful and routine ¹³C nmr spectroscopy is the use of high-field pulse technology coupled with broad-band heteronuclear decoupling of all protons. The results of repeated pulse sequences are accumulated to provide improved signal strength. Also, for reasons that go beyond the present treatment, the decoupling irradiation enhances the sensitivity of carbon nuclei bonded to hydrogen. 
When acquired in this manner, the carbon nmr spectrum of a compound displays a single sharp signal for each structurally distinct carbon atom in a molecule (remember, the proton couplings have been removed). The spectrum of camphor, shown on the left below, is typical. Furthermore, a comparison with the ¹H nmr spectrum on the right illustrates some of the advantageous characteristics of carbon nmr. The dispersion of ¹³C chemical shifts is nearly twenty times greater than that for protons, and this together with the lack of signal splitting makes it more likely that every structurally distinct carbon atom will produce a separate signal. The only clearly identifiable signals in the proton spectrum are those from the methyl groups. The remaining protons have resonance signals between 1.0 and 2.8 ppm from TMS, and they overlap badly thanks to spin-spin splitting.

Unlike proton nmr spectroscopy, the relative strength of carbon nmr signals are not normally proportional to the number of atoms generating each one. Because of this, the number of discrete signals and their chemical shifts are the most important pieces of evidence delivered by a carbon spectrum. The general distribution of carbon chemical shifts associated with different functional groups is summarized in the following chart. Bear in mind that these ranges are approximate, and may not encompass all compounds of a given class. Note also that the over 200 ppm range of chemical shifts shown here is much greater than that observed for proton chemical shifts.

13C Chemical Shift Ranges*
Low Field Region		High Field Region
	* For samples in CDCl3 solution. The δ scale is relative to TMS at δ=0.

The isomeric pairs previously cited as giving very similar proton nmr spectra are now seen to be distinguished by carbon nmr. In the example on the left below (blue box), cyclohexane and 2,3-dimethyl-2-butene both give a single sharp resonance signal in the proton nmr spectrum (the former at δ 1.43 ppm and the latter at 1.64 ppm). However, in its carbon nmr spectrum cyclohexane displays a single signal at δ 27.1 ppm, generated by the equivalent ring carbon atoms (colored blue); whereas the isomeric alkene shows two signals, one at δ 20.4 ppm from the methyl carbons (colored brown), and the other at 123.5 ppm (typical of the green colored sp² hybrid carbon atoms).

The C₈H₁₀ isomers in the center (red) box have pairs of homotopic carbons and hydrogens, so symmetry should simplify their nmr spectra. The fulvene (isomer A) has five structurally different groups of carbon atoms (colored brown, magenta, orange, blue and green respectively) and should display five ¹³C nmr signals (one near 20 ppm and the other four greater than 100 ppm). Although ortho-xylene (isomer B) will have a proton nmr very similar to isomer A, it should only display four ¹³C nmr signals, originating from the four different groups of carbon atoms (colored brown, blue, orange and green). The methyl carbon signal will appear at high field (near 20 ppm), and the aromatic ring carbons will all give signals having δ > 100 ppm. Finally, the last isomeric pair, quinones A & B in the green box, are easily distinguished by carbon nmr. Isomer A displays only four carbon nmr signals (δ 15.4, 133.4, 145.8 & 187.9 ppm); whereas, isomer B displays five signals (δ 15.9, 133.3, 145.8, 187.5 & 188.1 ppm), the additional signal coming from the non-identity of the two carbonyl carbon atoms (one colored orange and the other magenta).

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1. NMRshiftdb

NMRshiftdb2 is a NMR database (web database) for organic structures and their nuclear magnetic resonance (nmr) spectra. It allows for spectrum prediction (¹³C, ¹H and other nuclei) as well as for searching spectra, structures and other properties. The nmrshiftdb2 software is open source, the data is published under an open content license. The core of nmrshitdb2 are fully assigned spectra with raw data and peak lists (we have pure peak lists as well). Those datasets are peer reviewed by a board of reviewers. The project is supported by a scientific advisory board.

nmrshiftdb2 is part of the NFDI4Chem initiative and will provide a component for a curated repository there. Please consult the documentation for more detailed information.

See: https://nmrshiftdb.nmr.uni-koeln.de/portal

2. ACD/NMR

ACD/NMR Workbook Suite is a comprehensive NMR software application with an intuitive interface. It features a full suite of advanced processing, analysis, and databasing functionalities for 1D and 2D NMR data from all major vendor formats. NMR Workbook Suite is built upon cutting-edge algorithms for the most reliable NMR data interpretation. It is designed to streamline routine NMR workflows, simplify structure characterization, and much more.

Powerful NMR Interpretation Software | Highlights

• Import and process 1D and 2D NMR data from all major instrument vendor formats in a single collaborative platform
• Process NMR data manually or automate routine processing workflows—Fourier transformation, calibration, peak picking, integration, multiplet analysis, etc.
• Synchronize peak picking and assignments across datasets within a project
• Confidently verify structures with 3 different verification levels
• Perform targeted analysis of known mixture components and optimize untargeted mixture analysis workflow
• Perform Conformational Analysis using NOESY/ROESY spectra
• Create comprehensive multiplet reports and publication-ready data
• Store, manage, and share live NMR spectra

Synchronize peak picking and assignments across NMR datasets using NMRSync—our game-changing technology. Plus, the associated peaks from NMRSync, NMR prediction, and connectivity-based algorithms are automatically used to only identify the assignments that match all data. This quick and accurate peak picking and assignment workflow helps you to maximize your productivity in the following ways:

• Use any peak in any spectrum to initiate NMRSync
• Integrate a peak in any spectrum and all related peaks in the 1D and 2D NMR spectra of that dataset will be identified and linked in real time
• Automatically resolve overlapping ¹H and ¹³C peaks from 2D NMR data
• Receive immediate color-coded feedback on the best assignment for instant decision-making purposes

NMR Workbook Suite includes three levels of structure verification that evaluate alternative structures to varying degrees for added flexibility in your NMR analysis. This ensures the best structure that matches the experimental NMR data is confirmed with much less time and effort than manual interpretation.

• Determine how well your proposed structure matches the datasets in your NMR project with single structure verification
• Generate a specified number of alternative structures, based on the user-defined proposed structure, and evaluate whether they are a better match to the NMR dataset using Combined and Concurrent Verification
• Generate and view every alternative structural and cis/trans isomer that matches the experimental data in real-time using Unbiased Verification for an absolute level of confidence. This workflow eliminates the user bias and ensures the assigned structure is indeed the best structure that fits the experimental data.

See: https://www.acdlabs.com/products/spectrus/workbooks/nmr/

3. See: http://www.cheminfo.org/Spectra/NMR/Predictions/1H_Prediction/index.html

4. See: https://www.nmrprocflow.org/

5. See: https://chem.washington.edu/facilities/data-processing

6. See: https://www.cgl.ucsf.edu/home/sparky/

7. See: http://www.nmrdb.org/about/

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Nuclear Magnetic Resonance (NMR) interpretation plays a pivotal role in molecular identifications. As interpreting NMR spectra, the structure of an unknown compound, as well as known structures, can be assigned by several factors such as chemical shift, spin multiplicity, coupling constants, and integration. This Module focuses on the most important ¹H and ¹³C NMR spectra to find out structure even though there are various kinds of NMR spectra such as ¹⁴N, ¹⁹F, and ³¹P. NMR spectrum shows that x- axis is chemical shift in ppm. It also contains integral areas, splitting pattern, and coupling constant.

Strategy for Solving Structure

Here is the general strategy for solving structure with NMR:

1. Molecular formula is determined by chemical analysis such as elementary analysis
2. Double-bond equivalent (also known as Degree of Unsaturation) is calculated by a simple equation to estimate the number of the multiple bonds and rings. It assumes that oxygen (O) and sulfur (S) are ignored and halogen (Cl, Br) and nitrogen is replaced by CH. The resulting empirical formula is C_aH_b

3. Structure fragmentation is determined by chemical shift, spin multiplicity, integral (peak area), and coupling constants (1J1J, 2J2J)
4. Molecular skeleton is built up using 2-dimensional NMR spectroscopy.
5. Relative configuration is predicted by coupling constant (³J).

¹H NMR

Chemical Shift

Chemical shift is associated with the Larmor frequency of a nuclear spin to its chemical environment. Tetramethylsilane (TMS, (CH3)4Si(CH3)4Si) is generally used as an internal standard to determine chemical shift of compounds: δ_TMS=0 ppm. In other words, frequencies for chemicals are measured for a ¹H or ¹³C nucleus of a sample from the ¹H or ¹³C resonance of TMS. It is important to understand trend of chemical shift in terms of NMR interpretation. The proton NMR chemical shift is affect by nearness to electronegative atoms (O, N, halogen.) and unsaturated groups (C=C,C=O, aromatic). Electronegative groups move to the down field (left; increase in ppm). Unsaturated groups shift to downfield (left) when affecting nucleus is in the plane of the unsaturation, but reverse shift takes place in the regions above and below this plane. ¹H chemical shift play a role in identifying many functional groups. Figure 11. indicates important example to figure out the functional groups.

Chemical equivalence

Protons with Chemical equivalence has the same chemical shift due to symmetry within molecule (CH3COCH3CH3COCH3) or fast rotation around single bond (-CH₃; methyl groups).

Spin-Spin Splitting

Spin-Spin splitting means that an absorbing peak is split by more than one “neighbor” proton. Splitting signals are separated to J Hz, where is called the coupling constant. The spitting is a very essential part to obtain exact information about the number of the neighboring protons. The maximum of distance for splitting is three bonds. Chemical equivalent protons do not result in spin-spin splitting. When a proton splits, the proton’s chemical shift is determined in the center of the splitting lines.

Spin Multiplicity (Splitting pattern)

Spin Multiplicity plays a role in determining the number of neighboring protons. Here is a multiplicity rules: In case of AmBnAmBn system, the multiplicity rule is that Nuclei of BB element produce a splitting the AA signal into nB+1nB+1 lines. The general formula which applies to all nuclei is 2nI+12nI+1, where II is the spin quantum number of the coupled element. The relative intensities of the each lines are given by the coefficients of the Pascal’s triangle (Figure 22).

First-order splitting pattern

The chemical shift difference in Hertz between coupled protons in Hertz is much larger than the JJ coupling constant:ΔνJ≥8(1)(1)ΔνJ≥8

Where ΔνΔν is the difference of chemical shift. In other word, the proton is only coupled to other protons that are far away in chemical shift. The spectrum is called first-order spectrum. The splitting pattern depends on the magnetic field. The second-order splitting at the lower field can be resolved into first-order splitting pattern at the high field. The first-order splitting pattern is allowed to multiplicity rule (N+1) and Pascal’s triangle to determine splitting pattern and intensity distribution.

Example 11

The note is that structure system is A₃M₂X₂. H_a and H_x has the triplet pattern by Hm because of N+1 rule. The signal of Hm is split into six peaks by H_x and H_a(Figure3) The First order pattern easily is predicted due to separation with equal splitting pattern.

High-order splitting pattern

High-order splitting pattern takes place when chemical shift difference in Hertz is much less or the same that order of magnitude as the j coupling.ΔvJ≤10(2)(2)ΔvJ≤10

The second order pattern is observed as leaning of a classical pattern: the inner peaks are taller and the outer peaks are shorter in case of AB system (Figure 44). This is called the roof effect.

Here is other system as an example: A₂B₂ (Figure 55). The two triplet incline toward each other. Outer lines of the triplet are less than 1 in relative area and the inner lines are more than 1. The center lines have relative area 2.

Coupling constant (J Value)

Coupling constant is the strength of the spin-spin splitting interaction and the distance between the split lines. The value of distance is equal or different depending on the coupled nuclei. The coupling constants reflect the bonding environments of the coupled nuclei. Coupling constant is classified by the number of bonds:

Geminal proton-proton coupling (²J_HH)

Germinal coupling generates through two bonds (Figure 66). Two proton having geminal coupling are not chemically equivalent. This coupling ranges from -20 to 40 Hz. ²J_HHdepends on hybridization of carbon atom and the bond angle and the substituent such as electronegative atoms. When S-character is increased, Geminal coupling constant is increased: ²J_sp1>²J_sp2>²J_sp3 The bond angle(HCH) gives rise to change ²J_HH value and depend on the strain of the ring in the cyclic systems. Geminal coupling constant determines ring size. When bond angle is decreased, ring size is decreased so that geminal coupling constant is more positive. If a atom is replace to an electronegative atom, Geminal coupling constant move to positive value.

Vicinal proton-proton coupling (³J_HH)

Vicinal coupling occurs though three bonds (Figure 77.). The Vicinal coupling is the most useful information of dihedral angle, leading to stereochemistry and conformation of molecules. Vicinal coupling constant always has the positive value and is affected by the dihedral angle (?;HCCH), the valence angle (?; HCC), the bond length of carbon-carbon, and the effects of electronegative atoms. Vicinal coupling constant depending on the dihedral angle (Figure 88) is given by the Karplus equation.3J=7.0−0.5cosϕ+4.5cos2ϕ(3)(3)3J=7.0−0.5cos⁡ϕ+4.5cos2⁡ϕ

When ? is the 90^o, vicinal coupling constant is zero. In addition, vicinal coupling constant ranges from 8 to 10 Hz at the and ?=180^o, where ?=0^o and ?=180^o means that the coupled protons have cis and trans configuration, respectively.

The valence angle(?;Figure 88) also causes change of ³J_HH value. Valence angle is related with ring size. Typically, when the valence angle decreases, the coupling constant reduces. The distance between the carbons atoms gives influences to vicinal coupling constant

The coupling constant increases with the decrease of bond length. Electronegative atoms affect vicinal coupling constants so that electronegative atoms decrease the vicinal coupling constants.

Integral

Integral is referred to integrated peak area of 1H signals. The intensity is directly proportionally to the number of hydrogen.

¹³C NMR

Chemical Shift

Spin-Spin splitting

Comparing the ¹H NMR, there is a big difference thing in the ¹³C NMR. The ¹³C- ¹³ C spin-spin splitting rarely exit between adjacent carbons because ¹³C is naturally lower abundant (1.1%)

• ¹³C-¹H Spin coupling: ¹³C-¹H Spin coupling provides useful information about the number of protons attached a carbon atom. In case of one bond coupling (¹J_CH), -CH, -CH₂, and CH₃ have respectively doublet, triplet, quartets for the ¹³C resonances in the spectrum. However, ¹³C-¹H Spin coupling has an disadvantage for ¹³C spectrum interpretation. ¹³C-¹H Spin coupling is hard to analyze and reveal structure due to a forest of overlapping peaks that result from 100% abundance of ¹H.
• Decoupling: Decoupling is the process of removing ¹³C-¹H coupling interaction to simplify a spectrum and identify which pair of nuclei is involved in the J coupling. The decoupling ¹³C spectra shows only one peak(singlet) for each unique carbon in the molecule(Figure 1010.). Decoupling is performed by irradiating at the frequency of one proton with continuous low-power RF.

• Distortionless enhancement by polarization transfer (DEPT): DEPT is used for distinguishing between a CH₃ group, a CH₂ group, and a CH group. The proton pulse is set at 45^o, 90^o, or 135^o in the three separate experiments. The different pulses depend on the number of protons attached to a carbon atom. Figure 1111. is an example about DEPT spectrum.

2-dimensional NMR spectroscopy (COSY)

COSY stands for COrrelation SpectroscopY. COSY spectrum is more useful information about what is being correlated.

¹H-¹H COSY (COrrelation SpectroscopY)

¹H-¹H COSY is used for clearly indicate correlation with coupled protons. A point of entry into a COSY spectrum is one of the keys to predict information from it successfully. Relation of Coupling protons is determined by cross peaks(correlation peaks) and in the COSY spectrum. In other words, Diagonal peaks by lines ar e coupled to each other. Figure 1212 indicates that there are correlation peaks between proton H₁ and H₂ as well as between H₂ and H₄. This means the H₂ coupled to H₁ and H₄.

¹H-¹³C COSY (HETCOR)

¹H-¹³C COSY is the heteronuclear correlation spectroscopy. The HETCOR spectrum is correlated ¹³C nuclei with directly attached protons. ¹H-¹³C coupling is one bond. The cross peaks mean correlation between a proton and a carbon (Figure 1313). If a line does not have cross peak, this means that this carbon atoms has no attached proton (e.g. a quaternary carbon atom)

References

1. Balc*, M., Basic p1 sH- and p13 sC-NMR spectroscopy. 1st ed.; Elsevier: Amsterdam ; Boston, 2005; p xii, 427.
2. Breitmaier, E., Structure elucidation by NMR in organic chemistry : a practical guide. 3rd rev. ed.; Wiley: Chichester, West Sussex, England, 2002; p xii, 258.
3. Jacobsen, N. E., NMR spectroscopy explained : simplified theory, applications and examples for organic chemistry and structural biology. Wiley-Interscience: Hoboken, N.J., 2007; p xv, 668.
4. Silverstein, R. M.; Webster, F. X., Spectrometric identification of organic compounds. 6th ed.; Wiley: New York, 1998; p xiv, 482.

Outside Links

• NMRShiftDB: a Free web database for NMR data : nmrshiftdb.chemie.uni-mainz.de/nmrshiftdb
• NMR database from ACD/LAbs : www.acdlabs.com/products/spec_lab/exp_spectra/spec_libraries/aldrich.html
• NMR database from John Crerar Library : http://crerar.typepad.com/crerar_lib…h_ir_nmr_.html

Problems

Draw the 1H NMR spectrum for 2-Hydroxypropane in CDCl3. Assume sufficient resolution to provide a first-order spectrum and ignore vicinal proton-proton coupling(3JHH)

Solution

1) the structure of 2-hydoroxyporpane is drawn

Figure out which protons are chemically equivalent, i.e., two methyl (-CH₃) groups are chemical equivalent.

4) Splitting pattern is determined by (N+1) rule: Ha is split into two peaks by H_b(#of proton=1). H_b has the septet pattern by H_a (#of proton=6). H_c has one peak.(Note that H_c has doublet pattern by H_b due to vicinal proton-proton coupling.)

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This handout relates the basic theory of NMR described on the theory web handout with spectra of real molecules and how to deduce structure from the spectra. Before reading this handout, you need to be thoroughly familiar with all of theory concepts that were described.

1.0 The NMR spectrum.

1.1 Because different amounts of electron density are around different non-eqivalent nuclei, the different non-equivalent nuclei in a molecule are experiencing slightly different net magnetic fields in an NMR experiment (Review Section 5.2A of the theory handout). Recall also that the difference in energy between the two allowed spin states (+1/2 and -1/2 spin states) of a spin 1/2 nucleus (like in 1H and 13C nuclei) depends on the exact magnetic field felt by the nucleus (Review Section 2.3Cin the theory handout). Recall further that in the NMR experiment, when and only when nuclei are irradiated with electromagnetic radiation of energy that exactlycorresponds to the energy difference between the +1/2 and -1/2 spin states, the nuclei absorb the energy and the NMR spectrometer measures this absorbance (Review section 3.1 of the theory handout). The absorbance of energy to convert a nucleus from a +1/2 to a -1/2 spin state is referred to as “resonance” of that nucleus.1.1A The key conclusion is that nuclei with different electron densities have +1/2 and -1/2 spin states that differ in energy by differing amounts, so these nuclei will absorb electromagnetic radiation of different frequencies in the NMR experiment.

1.1B Nuclei surrounded by greater amounts of electron density will be more shielded from the external magnetic field, so they will absorb electromagnetic radiation of lower energy, that is, lower frequency (energy is proportional to frequency). You may want to review Section 5.2A of the theory handout again.

1.1C The converse is also true, namely that nuclei surrounded by lesser amounts of electron density will be less shielded (referred to as being “deshielded”) from the external magnetic field, so they will absorb electromagnetic radiation of higher energy, that is, higher frequency(energy is proportional to frequency).

1.1D The three most important factors influencing the electron density around a hydrogen nucleus are: (i) adjacent electronegative atoms remove electron density; (ii) hybridization of the attached carbon atom, increasing shielding is observed in the order sp2, sp, sp3; (iii) adjacent pi bonds are deshielding, which relates to (ii).1.2 An NMR spectrum is a plot of absorbance versus frequency.

1.2A To make different spectra directly comparable, a standard is used for all NMR spectra. For 1H NMR spectra, the standard is called tetramethylsilane (TMS) and a small amount of TMS is usually added to any 1H NMR sample.

1.2B Magnets of different strengths lead to absorbance of electromagnetic radiation at different frequencies for the same nucleus, meaning that if simple frequency were plotted in an NMR spectra, you could not compare spectra taken of the same sample on machines with different magnet strengths. To solve this problem, the frequency of absorption plotted on NMR spectra are corrected for the magnet strength. In addition, frequency is correlated to the reference compound TMS. The frequency at which TMS absorbs is defined as 0 frequency by convention. In the NMR spectrum, absorbance frequencies of electromagnetic radiation are plotted as chemical shift (d) listed in units called parts per million (ppm) that is defined by the following equation:

1.3 The bottom line to this entire section is that the hydrogen atoms of different functional groups (methyl groups, -CH2- groups, aldehyde -C(O)H, alkene C-H, etc.) have characteristic chemical shifts, i.e. absorbance frequencies. These characteristic chemical shifts are collected in tables such as Fgure 13.8 and Appendix 4 of your book. From the chemical shift information, you thus know what functional groups are present in a molecule.

1.4 Chemically equivalent hydrogen atoms will have the same chemical shift and therefore give rise to the same signal. This is why we defined equivalent atoms in Section6.1 of the theory handout. Non-equivalent groups of hydrogens will have different chemical shifts. Thus, you will have as many different signals in an NMR spectrum as there are chemically non-equivalent groups of hydrogen atoms.

2.0 The nuclear spin of hydrogen atoms creates a magnetic field that influences the chemical shift of nearby hydrogen atoms (Review Sections 5.1 and 5.2).

2.1 Nuclear spin magnetic fields will influence hydrogen atoms that are three or fewer bonds away from each other in the same molecule. Hydrogen atoms that are four or greater bonds away usually do not influence each other.

2.2 A hydrogen atom with a nuclecus in a spin state of +1/2 produces a slightly different magnetic field than a one in a –1/2 spin state.

2.3 Even in a strong magnetic field, across a population of molecules, there is only a very slight excess of nuclei in the +1/2 spin state.

2.4 Putting all of these ideas together means the following: Consider a hydrogen X adjacent (three bonds away) to another hydrogen Y in a molecule. In around half of the molecules in the NMR sample, hydrogen X feels the magnetic field from a Y with nuclear spin of +1/2. The other half feel from Y a nuclear spin of –1/2. Thus, when you look at the spectrum, there are actually two different, but closely spaced peaks as the signal for hydrogen X. This phenomenon is called “spin-spin” splitting, and the distance between the two signals for X is called the “coupling constant”, often denoted as “J”. Similarly, the signal for Y actually has two peaks because of spin-spin splitting by X.

2.5 Consider a –CH2- group adjacent to a hydrogen X. Both of the hydrogen atoms in the –CH2- are chemically equivalent and could be either in the +1/2 or –1/2 nuclear spin state. Thus, there are three situations possible: i) +1/2,+1/2; ii) +1/2,-1/2, which is the same as –1/2, +1/2 and iii) –1/2,-1/2. Thus, there are actually three different magnetic fields that are felt by X in molecules of the sample, in a 1:2:1 ratio. Thus, the signal for hydrogen X is split into three peaks in a 1:2:1 ratio.

2.6 The same holds for a –CH3 group, that will split an adjacent hydrogen signal into four peaks, with a 1:3:3:1 ratio. You should verify this for yourself by making all the possible combinations of nuclear spins for the three equivalent hydrogen atoms of a methyl group.

2.7 In the general case, N equivalent hydrogen atoms will split an adjacent signal into (N+1) peaks, with relative ratios that are predicted by Pascal’s triangle (Figure 13.16 in the book).

3.0 Following the same logic, the splitting should multiply if a single hydrogen atom is adjacent to hydrogen atoms on either side. Think about combining all the possible nuclear spin states for these nearby sets of hydrogen atoms. Thus, if you have a hydrogen atom X between one –CH2- and one –CH3 group, it should be split into an amazing (2+1) x (3 + 1) = 12 signals because there are that many different combinations of +1/2 and -1/2 spins possible.

3.1 Thus, if the coupling constants (J) from the –CH2- and –CH3 groups are significantly different from each other, then 12 peaks will be observed as the signal for hydrogen X.

3.2 However, in practice, coupling constants (J) are pretty close to the same value for almost all sets of hydrogen atoms in organic molecules, simplifying the splitting pattern, since now many of the twelve peaks will overlap with each other. What this means is that for almost all the spectra you will see, if a hydrogen X is surrounded by N hydrogen atoms, the signal for X will be split into only (N+1) peaks, no matter how those N hydrogen atoms are grouped in terms of sets of equivalent atoms. Thus, what is actually seen for the example above is that the signal for X would appear in the spectrum to be split into 2 + 3 + 1 = 6 peaks, not 12, peaks. This is the so-called “N+1” rule.

3.3 The diagram below shows these two different situations. When nuclei from hydrogen atoms Z and Y split the signal for hydrogen X with very different coupling constants (notice how the coupling constant J for the red Z hydrogen nuclei is larger than J for the blue Y hydrogen nuclei), all twelve peaks are spread out and identifiable. Below that is shown the situation in which the coupling constants are the same for nuclei of both Z and Y, so only 6 peaks are actually observed in the signal for hydrogen X due to extensive overlap. This latter case, with six peaks, is what you will almost always see in reality since coupling constants tend to be similar in organic molecules.

3.3 The above explanation of splitting can confuse students for a while. The important point is that in the example given, you see 6 different peaks in the spectrum (N+1 rule) even though there are really 12 peaks produced, it is just that several of them are on top of each other because the coupling constants are the same. For alkyl groups in organic molecules, the coupling constants are generally the same so you will almost always see the fewer peaks, corresponding to the simple N+1 rule, rather than the greater number of peaks derived from the multiplication rule.

3.4 The bottom line here is that by seeing how a given signal is split, you can figure out how many hydrogen atoms are adjacent on the molecule, namely the number of peaks in the signal minus 1. From this information you can piece together what a molecule looks like if you know how many atoms of each type are present (i.e. the molecular formula such as C4H10N2O). You get the molecular formula information from something called a mass spectrum, described later in the text. Molecular formulas will be provided to you in homework or test questions.

4.0 For a given signal, integrating the signal (include all splitting peaks for a given signal) gives you a relative value that is proportional to the number of equivalent hydrogen atoms that gave rise to the signal. Thus, by looking at the integration values, you can deduce how many of each type of equivalent hydrogen atoms are in the molecule. For example, a -CH3 group would have a signal that integrates to a relative value of 3 (no matter how the signal is split), and a -CH2- group would have a relative integration of 2, etc. Note that sometimes integrations are simply given as absolute numbers, and you must find the common factor to deduce how many hydrogen atoms are represented by each integration value.

5.0 Putting it all together: How to deduce a structure from an NMR spectrum. First, you must be given the molecular formula, so you know how many of each type of atom are present. Second, count the number of different signals and their relative integrations to see how many different sets of equivalent hydrogen atoms are in a molecule, and how many of each set are present. Compare the chemical shifts of each signal to tables to identify what functional groups are present. Finally, use the signal splittings to determine which hydrogen atoms must be no more than 3 bonds away from each other.

6.0 For alkenes, the pi bond prevents bond rotation so the different hydrogen atoms on an unsymmetrical alkene are not equivalent, so they all have different signals, and splitting follows the multiplicative rule (the coupling constants are usually significantly different for geminal vs. cis. vs. trans relationships).

7.0 For hydrogens in a -CH2- group adjacent to a chiral center, the two different H atoms are no longer equivalent, because even with bond rotation, the two hydrogens are never in the same environment with respect to the groups on the adjacent chiral center. Thus, each H of -CH2- group adjacent to a chiral center usually has its own signal in the NMR spectrum.

8.0 There is a great deal more to NMR than this, I am only trying to give you the basics here.