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1- DTSA-II

DTSA-II is a multi-platform software package for quantitative x-ray microanalysis. DTSA-II was inspired by the popular Desktop Spectrum Analyzer (DTSA) package developed by Chuck Fiori, Carol Swyt-Thomas, and Bob Myklebust at NIST and NIH in the ’80’s and early ’90’s.

DTSA-II has being designed with the goal of making standards-based microanalysis more accessible for the novice microanalyst. We want to encourage standards-based analysis by making it as easy as possible to get reliable results. Many operations which had previously required user intervention under DTSA now are performed entirely by the software. Furthermore, the software attempts to guide the user step-by-step through common processes while performing quality control sanity checks. While this might not provide the flexibility that some sophisticated users may desire, we feel that this philosophy is more consistent with the way laboratories are moving towards technicians responsible for multiple techniques and away from experts in single techiques. We encourage users who desire the additional power and flexibility available in the EPQ library to learn to script using Jython or to create their own alternative user interface. EPQ is much more capable than the fraction exposed via DTSA-II.

DTSA-II is based on an entirely new code base written by Nicholas W. M. Ritchie. The codebase has been carefully divided into a shared algorithm library which forms the basis for a handful of software products and a user interface shell. DTSA-II is the user interface shell and the EPQ library is the algorithm library.

DTSA-II remains under active development. Many features – some fairly basic – remain unimplemented. Other features have not been tested as much as the developer might like. The program made available to the public via this web site represents the current best available version in the judgement of the developer. DTSA-II remains experimental software and no representations are made regarding the suitability of the product for any particular application.

Major features:

• Basic IO and Display
  • Read energy dispersive x-ray spectra in a variety of different commercial and non-commercial formats including the industry standard EMSA format
  • Display and overlay spectra with various scaling options on linear/log/sqrt axes
  • Copy/save/print the spectrum display as a bitmap/PNG file
  • Output the spectra as a GNUPlot file for publication quality output
  • Overlay labeled x-ray emission lines and x-ray absorption edges
  • Define and integrate regions-of-interest
  • View spectrum contextual information
  • Archive spectra to a searchable database
  • Sub-sampling of spectral data to simulate shorter acquisition times
  • Basic spectrum math functions
  • Background modeling or background stripping
  • Energy axis linearization
  • Spectrum smoothing
  • Peak removal (trimming)
  • Peak search / identification
• Spectrum Simulation
  • Analytical (φ(ρz)) simulations of energy dispersive x-ray spectra
    • Normal or oblique incidence angle
    • Variable beam energies, beam fluxes, materials
  • Monte carlo simulations of energy dispersive x-ray spectra
    • Spectra from bulk samples
    • Mounted or unmounted thin films
    • Cubical or spherical particles with or without a substrate
  • Simulated spectra may be manipulated as experimental spectra
  • Variety of detector options including Si(Li), SDD and microcalorimeter
• Standards-based Quantification
  • Standards-based quantification of EDS spectra
  • Filter-fit linear-least squares fitting of reference spectra
  • Quantification based on references or like-standards
  • φ(ρz) correction of the k-ratios
  • ζ-factor correction of thin-film samples
  • Results reported as HTML with estimates of uncertainty
• Reporting
  • Actions are recorded in a daily HTML activity report
  • Report may be opened in an alternative HTML viewer
• Platforms and Source Code
  • DTSA-II is based on the EPQ library – a full-featured library of electron probe quantification algorithms
  • DTSA-II only exposes a fraction of the power within the EPQ library. The remainder may be accessed via custom Java applications or via Jython scripting.
  • The EPQ library includes the full NISTMonte for Monte Carlo simulation of electron/x-ray transport
  • DTSA-II / EPQ library are available as source code
  • DTSA-II / EPQ library are written in Java SE 6 compatible source
  • DTSA-II / EPQ library can execute on any platform supporting Java SE 6 or later
  • DTSA-II / EPQ library is regularly tested on Windows XP, Ubuntu Linux & Apple OS X

Disclaimer

This software was developed at the National Institute of Standards and Technology by employees of the Federal Government in the course of their official duties. Pursuant to title 17 Section 105 of the United States Code this software is not subject to copyright protection and is in the public domain. DTSA and the EPQ library are experimental systems. NIST assumes no responsibility whatsoever for its use by other parties, and makes no guarantees, expressed or implied, about its quality, reliability, or any other characteristic. We would appreciate acknowledgement if the software is used. This software can be redistributed and/or modified freely. The author requests that any derivative works bear some notice that they are derived from it, and any modified versions bear some notice that they have been modified.

Any mention of commercial products is for information only; it does not imply recommendation or endorsement by NIST nor does it imply that the products mentioned are necessarily the best available for the purpose.

See: https://cstl.nist.gov/div837/837.02/epq/dtsa2/

2. HyperSpy

HyperSpy is an open source Python library which provides tools to facilitate the interactive data analysis of multi-dimensional datasets that can be described as multi-dimensional arrays of a given signal (e.g. a 2D array of spectra a.k.a spectrum image). HyperSpy aims at making it easy and natural to apply analytical procedures that operate on an individual signal to multi-dimensional arrays, as well as providing easy access to analytical tools that exploit the multi-dimensionality of the dataset. Its modular structure makes it easy to add features to analyze different kinds of signals.

Highlights

• Two families of named and scaled axes: signal and navigation.
• Visualization tools for multi-dimensional spectra and images.
• Easy access multi-dimensional curve fitting and blind source separation.
• Built on top of NumPy, SciPy, matplotlib and scikit-learn.
• Modular design for easy extensibility.

The development has been motivated by the data analysis needs of the electron microscopy community but it is proving useful in many other fields.

See: https://hyperspy.org/

3. AZtec

• AZtecFeature is an innovative particle analysis system specifically optimised for usability and high-speed throughput. It combines the raw speed and sensitivity of the Ultim Max Silicon Drift Detector with the superior analytical performance and ease of use of the AZtec® EDS analysis suite to create the most advanced automated particle analysis platform on the market. Gunshot Residue Analysis in the SEM with AZtecGSR is fast and accurate: it gives reproducible Gunshot Residue Analysis results to ASTM E1588 – 10e1.
• AZtecGSR combines ease of use through its guided workflow, with the ultimate accuracy using the latest Ultim Max detectors and Tru-Q® algorithms. LayerProbe is an exciting software tool for thin film analysis in the SEM. An option for the AZtec EDS microanalysis system, LayerProbe is faster, more cost-effective and higher resolution than dedicated thin film measurement tools.The most powerful EBSD software available, AZtecHKL combines speed and accuracy of results for routine analysis, with the flexibility and power required for applications that push the frontiers of EBSD.
• AZtec3D combines simultaneous EDS and EBSD data acquisition & analysis with the automated milling capabilities of a FIB-SEM.AZtecLiveOne software platform is the ideal solution for carrying out a complex task like EDS as quickly and as easily as possible. No need for substantial training or advanced knowledge of the EDS technique. Users can be trained in a matter of minutes and will have complete confidence in their results. AZtecTEM is an innovative EDS software specifically optimised for advanced TEM applications. AZtecSynergy provides a powerful solution for the simultaneous collection of EDS and EBSD data. All of the tools to collect excellent integrated data are included in one place with no complicated switching from one navigator to another.
• AZtecSteel is an automated steel inclusion analysis package developed specifically for the analysis and classification of steel inclusions using Energy Dispersive X-ray microanalysis (EDS) in a scanning electron microscope (SEM). It detects, measures and analyses the inclusions, processes the resulting data set to published standard methods, and includes functionality to plot complex ternary diagrams. AZtecLive is a revolutionary new approach to EDS analysis that enables a radical change in the way users perform sample investigation in the SEM. It combines a live electron image with live X-ray chemical imaging to give an intuitive new way of interacting with your samples. Collecting good quality data is only the beginning of any complete EBSD analysis. AZtecCrystal provides all the necessary tools to process and interrogate your EBSD data and to solve your materials problems. Seamlessly integrated with AZtecHKL or operated as a standalone program, AZtecCrystal sets the standard in EBSD data processing for experts and novices alike.
• AZtecAM is a powerful, automated, solution for the analysis of metal powders used in additive manufacturing. Based on AZtecFeature, AZtecAM optimises the particle analysis workflow to enable the rapid and accurate characterisation of metal powders. AZtecMineral is a powerful, automated, Mineral Liberation Analysis solution. It enables ore characterisation, provides vital data on metal recovery and enables process yield characterisation using multipurpose SEMs. It is also a valuable tool for the characterisation of rocks in research environments, enabling the automation of otherwise laborious optical analyses.

See: https://engineering.virginia.edu/oxford-instruments-offering-free-aztec-suite-software-electron-microscopy-analysis

4. ESPRIT Family

ESPRIT 2 unites four analytical methods under a single user interface. These include EDS for SEM and (S)TEM, WDS, Micro-XRF for SEM and EBSD. This makes it easy for the user to switch between methods with a single mouse click. Additionally, it facilitates combining different method results from the same sample area and to so gain much more information. To name only the most important, coupling of following methods is supported:

• EDS and EBSD
• EDS and WDS
• EDS and Micro-XRF for SEM

The software is designed to suit the needs of all levels of users – from beginner to expert. Novices will benefit from the assistants that help performing routine tasks without having to learn details of the measurement method. More experienced users will value the option to drill down deeper, when they need it, meaning both detailed setup of measurements as well as in-depth analysis of results and automation of tasks.

See: https://www.bruker.com/en/products-and-solutions/elemental-analyzers/eds-wds-ebsd-SEM-Micro-XRF/software-esprit-family.html

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Interaction of an electron beam with a sample target produces a variety of emissions, including x-rays. An energy-dispersive (EDS) detector is used to separate the characteristic x-rays of different elements into an energy spectrum, and EDS system software is used to analyze the energy spectrum in order to determine the abundance of specific elements. EDS can be used to find the chemical composition of materials down to a spot size of a few microns, and to create element composition maps over a much broader raster area. Together, these capabilities provide fundamental compositional information for a wide variety of materials.

How it Works – EDS

EDS systems are typically integrated into either an SEM or EPMA instrument. EDS systems include a sensitive x-ray detector, a liquid nitrogen dewar for cooling, and software to collect and analyze energy spectra. The detector is mounted in the sample chamber of the main instrument at the end of a long arm, which is itself cooled by liquid nitrogen. The most common detectors are made of Si(Li) crystals that operate at low voltages to improve sensitivity, but recent advances in detector technology make availabale so-called “silicon drift detectors” that operate at higher count rates without liquid nitrogen cooling.

An EDS detector contains a crystal that absorbs the energy of incoming x-rays by ionization, yielding free electrons in the crystal that become conductive and produce an electrical charge bias. The x-ray absorption thus converts the energy of individual x-rays into electrical voltages of proportional size; the electrical pulses correspond to the characteristic x-rays of the element.

Strengths

• When used in “spot” mode, a user can acquire a full elemental spectrum in only a few seconds. Supporting software makes it possible to readily identify peaks, which makes EDS a great survey tool to quickly identify unknown phases prior to quantitative analysis.
• EDS can be used in semi-quantitative mode to determine chemical composition by peak-height ratio relative to a standard.

Limitations

• There are energy peak overlaps among different elements, particularly those corresponding to x-rays generated by emission from different energy-level shells (K, L and M) in different elements. For example, there are close overlaps of Mn-K_α and Cr-K_β, or Ti-K_α and various L lines in Ba. Particularly at higher energies, individual peaks may correspond to several different elements; in this case, the user can apply deconvolution methods to try peak separation, or simply consider which elements make “most sense” given the known context of the sample.
• Because the wavelength-dispersive (WDS) method is more precise and capable of detecting lower elemental abundances, EDS is less commonly used for actual chemical analysis although improvements in detector resolution make EDS a reliable and precise alternative.
• EDS cannot detect the lightest elements, typically below the atomic number of Na for detectors equipped with a Be window. Polymer-based thin windows allow for detection of light elements, depending on the instrument and operating conditions.

Results

A typical EDS spectrum is portrayed as a plot of x-ray counts vs. energy (in keV). Energy peaks correspond to the various elements in the sample. Generally they are narrow and readily resolved, but many elements yield multiple peaks. For example, iron commonly shows strong K_α and K_βpeaks. Elements in low abundance will generate x-ray peaks that may not be resolvable from the background radiation.

EDS spectrum of multi-element glass (NIST K309) containing O, Al, Si, Ca, Ba and Fe (Goldstein et al., 2003). 

EDS spectrum of biotite, containing detectable Mg, Al, Si, K, Ti and Fe (from Goodge, 2003). 

References

• Severin, Kenneth P., 2004, Energy Dispersive Spectrometry of Common Rock Forming Minerals. Kluwer Academic Publishers, 225 p.–Highly recommended reference book of representative EDS spectra of the rock-forming minerals, as well as practical tips for spectral acquisition and interpretation.
• Goldstein, J. (2003) Scanning electron microscopy and x-ray microanalysis. Kluwer Adacemic/Plenum Pulbishers, 689 p.
• Reimer, L. (1998) Scanning electron microscopy : physics of image formation and microanalysis. Springer, 527 p.
• Egerton, R. F. (2005) Physical principles of electron microscopy : an introduction to TEM, SEM, and AEM. Springer, 202.
• Clarke, A. R. (2002) Microscopy techniques for materials science. CRC Press (electronic resource)

Related Links

• Petroglyph–An atlas of images using electron microscope, backscattered electron images, element maps, energy dispersive x-ray spectra, and petrographic microscope– Eric Chrisensen, Brigham Young University
• SEM/EDX webpage from Indiana University – Purdue University Fort Wayne

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X-ray is a kind of electromagnetic wave, the same as light. The wavelength of visible light is 400 to 800nm, while the wavelength of x-ray is much shorter (higher energy), at 0.001nm to 10nm, and is known to have strong penetrating power.

Fig. 1 shows the interactions between a material and X-ray, and various analysis methods that make use of these interactions. These interactions provide important clues for learning the state of a material. As a familiar example, an X-ray image for medical application is a well-known use of transmission X-ray. Here we will introduce an elemental analysis method called fluorescent X-ray spectrometry.

Fig.1 Analytical methods and its application interaction of X-ray and matter

Fluorescent X-ray Spectrometry

When irradiating X-rays onto a material, fluorescent X-ray (characteristic X-ray), which has energy (wavelength) unique to the element that composes the material will be generated. When we measure the fluorescent X-ray energy, the contained element is identified (qualitative analysis), and we can calculate the concentration (quantitative analysis) from the intensity of the fluorescent X-ray of each element. Thus, the qualitative or quantitative analyses of a material by irradiating X-rays onto an unknown material and analyzing the fluorescent X-ray that is generated, is called fluorescent X-ray spectrometry.

There are two types of fluorescent X-ray spectrometry; the wavelength dispersive type (WDXRF) using analyzing crystals, and the energy dispersive type (EDXRF) using semiconductor detectors (EDS).

Comparison between Energy Dispersive Type and Wavelength Dispersive Type Spectrometers

The characteristics of a wavelength dispersive type spectrometer (WDXRF) are high sensitivity, high accuracy, high resolution, and high reproducibility. We can expect sensitivity and accuracy at levels one order of magnitude higher than those of the energy dispersive type spectrometer (EDXRF). These characteristics are provided by a high-power X-ray tube (3 to 4 kW) and its cooling device, a goniometer which makes complicated movements and an exchange mechanism for the analyzing crystal and detector and so on. Naturally, the instruments are larger, with a complicated structure and high price. The specimen surface is required to be flat and the available analysis area is from several mm to 30mm or so. This type of device is suitable for process management where specimens with the same form are analyzed one after another.

The characteristics of the energy dispersive type spectrometer (EDXRF) are simple structure and low price, its adaptability to a variety of specimens, and its user-friendliness. The X-ray bulb is compact (several tens W) and air-cooled, and since the EDS (semiconductor detector) itself performs the analysis, a complicated spectroscopy section is not necessary.

The roughness or shape of specimen does not matter, so analysis of large specimens or micro areas is possible. Each characteristic is shown in Fig. 2. The images are the large instrument for WDXRF, and the compact simple instrument for EDXRF.

Wavelength Dispersive Type (WDXRF) Advantages: High Sensitivity, High Resolution High Accuracy, High Reproducibility Disadvantages: Complicated and large-sized, high price Specimen is limited to flat platesEnergy Dispersive Type (EDXRF) Advantages: Simple Operation, compact, low price Flexibility in specimen shape Disadvantages: Low resolution (overlapped peaks) Cooling mechanism requiring liquid nitrogen or the like

Fig.2 Comparison between Wavelength Dispersive Type (WDXRF) and Energy Dispersive Type (EDXRF)

Sampling of Solid/Powder/Liquid Samples

One of the characteristics of EDXRF is the ease of use. Sampling of solid, powder, and liquid samples is explained below.

Sampling of Solid Sample

Analysis of a solid sample is possible by simply placing the sample at the X-ray illumination position.

In case of small sample, use of a dedicated cell will make it easier to set the sample. Fig. 3 shows a simplified illustration of the solid sample sampling method.

Fig.3 Sampling of Solid Sample

Sampling of Powder Sample (rock, soil, incinerated ash, etc)

Powder samples are typically analyzed by producing a pellet using a compression device. As a simplified method, analysis is possible on the powder placed into a specially-designed cell. Fig. 4 shows a simplified illustration of the powder sample sampling method.

Fig.4 Sampling of Powder Sample

Sampling of Liquid Sample

For liquid samples, a dedicated cell is used. Fill a dedicated cell with the liquid and analyze. In addition, there is another method where you can drop liquid onto a filter, dry it, and then analyze it. Fig. 5 shows a simplified illustration of liquid sample sampling methods.

Fig.5 Sampling of Liquid Sample

FP Quantitative Method / Film Thickness Analysis of Thin Film Sample

FP (fundamental parameter) quantitative method

The EDXRF instrument employs a theoretical calculation method called the FP quantitative method, allowing quantitative analysis of an unknown sample without the need for a standard sample.
The FP quantitative method assumes that the sample is uniform, sufficiently large and thick, and that all elements (100% in total) are quantified. Naturally, a sample must satisfy these assumptions, so attention is needed.The flow chart of FP quantitative method is shown in Fig. 6.

Fig.6 The flow chart of FP quantitative method

Flow Chart Explanation

1. First, measure the unknown sample and obtain the measurement intensity.
2. Assume the initial concentration of the sample and obtain a calculated intensity using the FP method.
3. Compare the measurement intensity and the calculated intensity.
4. Change the assumed concentration so that the measurement intensity and the calculated intensity match.
5. Obtain a new calculated intensity with the new assumed concentration using the FP method.
6. Repeat steps 3 to 5.
7. The assumed concentration that gives a calculated concentration that matches the measurement concentration is the analysis result.

Film Thickness Analysis of Thin Film Sample

In the case of a thin film sample, there is a correlation between the x-ray intensity of the elements composing the film and the film thickness. Therefore, by irradiating X-rays onto the surface of a thin film and measuring the X-ray intensity of the elements composing the film, the film thickness can be analyzed without destroying it.

A Single layer film can be analyzed using a calibration curve, but with the calibration curve method, a standard sample must be prepared for each kind of film. When the thin film FP quantitative method is used, it is not only possible to analyze single layer films, but also to analyze the thickness and composition of each layer in a multi-layer thin film, up to 5 layers , without a standard sample, which is very convenient. Fig. 7 shows a diagram of the thin film FP method, Fig. 8 shows a measurement example of Au/Ni/Cu film.

Thin Film FP (fundamental parameter) method

• Simultaneous non-destructive analysis of thickness and composition of thin film
• Up to 5 layers, and up to 20 elements for each layer
• Film thickness of about 10nm to 10μm (differs depending on element)
• Standard sample is not necessary (theoretical calculation)
• Information of layering order, elements, and density of the film is needed.

Fig.7 Schematic diagram of a thin film FP method

Fig.8 Measurement of the film Au / Ni / Cu thin film FP method

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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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Vibrating Sample Magnetometry (VSM) is a measurement technique which allows to
determine the magnetic moment of a sample with very high precision. The aim of this
lab course M106 is to enlarge upon the use of this widespread technique introduced in
the lab course B512, where different ferromagnetic samples were characterized
concerning magnetic hysteresis and demagnetization. Here, we will gain a deeper
understanding of the behavior of magnetic materials and its measurement.

In order to
lay the foundations, first the measurement principle and the properties of ferromagnetic
materials will be summarized (magnetic domains, magnetic hysteresis,
demagnetization) and then we will elaborate on the magnetic anisotropy of
ferromagnetic materials.

A vibrating sample magnetometer (VSM) systems are used to measure the magnetic properties of materials. The vibrating component causes a change in the magnetic field of the sample, which generates an electrical field in a coil based on Faraday’s Law of Induction.

If the sample is placed within a uniform magnetic field H, a magnetization M will be induced in the sample. In a VSM, the sample is placed within suitably placed sensing coils, also held at the desired angle.

And the vibrating sample component is made to undergo sinusoidal motion, i.e., mechanically vibrated.

The hysteresis loop shows the “history dependent” nature of magnetization of a ferromagnetic material. Once the material has been driven to saturation, the magnetizing field can then be dropped to zero and the material will retain most of its magnetization (it remembers its history).

Procedure
Before using the VSM, you must carry out a series of configuration steps.
• Insert the Ni standard into the VSM. The standard is ball-shaped, therefore
magnetically isotropic, and has a magnetic moment of 6.92 emu at 5000 Oe.
• Find the exact position of the standard in respect to the center of the pickup coils. The
vibrating rod can be adjusted by three screws on top of the VSM for x, y and z
direction. The pickup coils are connected in a way that, the sample being in the center
of the coils, there will be a signal minimum along x-, a maximum along y- and a
maximum again along z-direction.
• Run Calibrations → Moment gain to calibrate the instrument, i.e. to convert the
measured voltage signal into the correct value of the magnetic moment.
After calibration of the VSM, the following measurements aim to address two topics. The
first part covers basic magnetic characterization and the information that can be
deduced from magnetization curves. The second part cope with demagnetization.

1. Magnetocrystaline anisotropy energy:
   Fix the Fe single crystal to the sample holder. Set H0 to 3500 G and record the
   magnetic moment of the crystal during a 360° rotation of the sample.
   Find the angles corresponding to the different crystallographhic / magnetic axes and
   record the magnetization curves of the easy axis and the hard axis.
2. Stress induced magnetic anisotropy
   Mount a sheet sample clamped in a sample holder provided by the supervisor into the
   VSM. Record the magnetization curves of the sample with and without applied stress
   along and perpendicular to the stress direction.
   Determine the volume of each sample that you have measured

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Operation

A sample is made to oscillate using a vibrational unit extended on a rod. The sample is placed between two electromagnetic pieces which are used as the applied field for this this experiment. With the sample oscillating induces a voltage between the search coils which creates a signal to determine the magnetic properties of the sample. Reference coils are used to create a reference signal such that noise generated from the signal can be filtered using a lock-in amplifier [1]. Because the signal and the reference signal are directly related through its voltage and amplitude means that precise measurements can be recorded using a voltmeter. Calibration methods are important to determine the relation between the voltages induced by the magnetic field and the sample and their magnetic properties.

Calibrating the applied field is done by increasing the voltage in steps measuring the field until reaching a maximum. The system is calibrated using a nickel standard normally as a number of volts per unit of magnetic moment. Many materials such as types of barium ferrite or alnico materials can be placed inside to determine properties. These properties include remanence, coercivity, intrinsic coercivity and operating points once the system has been calibrated.

Advantages and Disadvantages in terms of experimental facets

The key advantage is the precision and accuracy of VSMs. Taking measurements at a range of angles once detection arrangements for the coils have been devised can be done. The advantage of sample vibration perpendicularly to the applied field can be found once the detection coils have been arranged appropriately. This means that there is the ability to test the sample at different angles. The positioning of the coils are done in a way to reduce the effects of sample position variation and external field variation- essentially deep into the applied field shown in figure 1. Disadvantages are that they are not well suited for determining the magnetisation loop or the hysteresis curve due to the demagnetising effects of the sample. Another problem is that, particularly for the VSM used in the third year laboratory is that temperature dependence cannot be controlled.

Figure 1. A schematic layout of the VSM

2. B-H Hysteresis Loop Tracer

Operation

The B-H hysteresis loop tracer is essentially two coils, one with a sample and the other which is empty for comparison. The insertion of a sample into the pickup coils causes a voltage proportional to the rate of change of the vector field to occur across the difference amplifier. After passing through an integrator, a voltage proportional to the intrinsic induction is passed to the Y-amp of the oscilloscope. This voltage combined with an X-voltage representing the magnetising field generated from the solenoid without the sample results in the generation of a hysteresis loop on the oscilloscope. Calibration is through a balance and phase adjustment to establish a trace on the oscilloscope. They are done to make sure that the magnetising field is linear and that every vector corresponds to the applied field. Measurements for the magnetic properties can then be made.

Advantages and disadvantages in terms of experimental facets

The coils have the ability to heat the sample such that temperature variance can be observed in the way that the material behaves when influenced by a magnetic field. On the other hand, this could cause overheating of the system which could result in a failure. Using a BH-looper can give the user a more improved visualisation compared to a VSM of the way a material behaves. The values plotted on the scope are only proportional to the absolute values, therefore display yields qualitative not quantitative information about a material magnetic properties. The precision is generally low compared to a VSM. Because a hysteresis loop is viewed using an oscilloscope means that observations of whether the material is a soft or hard magnetic material. And this is why it is used in quality control testing industries like the control of ferromagnetic oxides in a magnetic tape factory.

Figure 2. A schematic layout of a BH loop tracer [2].

3(I) Difference between concepts of Vector Field B, Magnetisation M and the magnetising field H

The vector field B represents the magnetic induction. Magnetisation M is the magnetic moment per unit volume of a solid. Magnetising H field is the magnetic field strength. These three quantities are related by the equation.

With μ₀ being the permittivity of free space. To show the difference between these quantities, hysteresis loops for a magnetic material shown in figure 4 are used. One of the key differences shown is that the magnetisation saturates whereas the B field increases at a constant rate for certain values for H. The magnetisation is generated by the spin and the orbital angular momentum of electrons in the solid. H is generated outside the material by electrical currents[3]. Therefore, from the equation above, the B field is the combination of H and M which shows the difference between the quantities with the inclusion of the permittivity of free space.Find out how UKEssays.com can help you!

A way to show the difference between the 3 parameters is through the representation of a bar magnet in a magnetic field shown in figure 3. Unfortunately, due to the age of the diagram, the labels are a bit old. Hence the ‘True’ field denotes the vector field B and the Applied field represents the magnetisation M. However, the arrows represent the direction and strength of each parameter. It is clear from figure 3 that the Magnetisation is much stronger than the demagnetising field.

Figure 3 An example of a magnet being demagnetised in an applied field

From figure 4, the two sketches representing of B and M against H can give an understanding of other magnetic properties of the material. The curve on the left can show the saturation of the magnetic material as well as the remanence M_r– the remaining magnetisation after the applied field has been turned off. The right hand diagram can show the remanent induction B_r and the saturation point of the applied field. In terms of the difference between the parameters, M, B and H, they yield different properties of the material in question.

Figure 4 Hysteresis loops showing (a) M and (b) B field against H

3(II) The difference between the susceptibility and relative permeability

The relative permeability μ_r and susceptibility χ are very closely related as shown by the equation below:

The relative permeability represents a characterisation of magnetic materials. Paramagnetic or diamagnetic materials have permeabilities close to the permeability of free space. However for ferromagnetic materials, the permeability is large in comparison. It represents a multiplication factor. For example, the use of an iron core with a relative permeability is 200 times greater than just an air coil used. So this is a measure of the actual magnetic field within a ferromagnetic material. Susceptibility is a measure to an extent to which a material may be magnetised in a magnetic field. It represents a ratio of how much a material is magnetised compared to the applied field on that material [4]. So the susceptibility specifies how much the relative permeability differs from one as shown in the equation above.

References

[1] Foner S 1959 Versatile and Sensitive Vibrating-Sample Magnetometer* Rev. Sci. Instrum. 30 548–57

[2] Howling D H 1956 Simple 60-cps Hysteresis Loop Tracer for Magnetic Materials of High or Low Permeability Rev. Sci. Instrum. 27 952

[3] Jiles D 1990 Introduction to Magnetism and Magnetic Materials (Chapman and Hall)

[4] Magnetic Susceptibilty http://www.britannica.com/EBchecked/topic/357313/magnetic-susceptibility

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Introduction to BET (Brunauer, Emmett and Teller)

By BET (Brunauer, Emmett and Teller) the specific surface area of a sample is measured – including the pore size distribution. This information is used to predict the dissolution rate, as this rate is proportional to the specific surface area. Thus, the surface area can be used to predict bioavailability. Further it is useful in evaluation of product performance and manufacturing consistency.

For a quote on BET: Use the contact form 

The specific surface determined by BET relates to the total surface area (reactive surface) as all porous structures adsorb the small gas molecules. The surface area determined by BET is thus normally larger than the surface area determined by air permeability. The method used complies with Ph. Eu.2.9.26 Method II.

Instrument and measuring principle, BET 

The BET instrument applied by Particle Analytical (Micromeritics Gemini 2375 and Gemini V) determines the specific surface area (m²/g) of pharmaceutical samples. The samples are dried with nitrogen purging or in a vacuum applying elevated temperatures. Unless otherwise instructed we use P/P0  of 0,1, 0,2 and 0,3 as standard measurement points. The volume of gas adsorbed to the surface of the particles is measured at the boiling point of nitrogen (-196°C). The amount of adsorbed gas is correlated to the total surface area of the particles including pores in the surface. The calculation is based on the BET theory. Traditionally nitrogen is used as adsorbate gas. Gas adsorption also enables the determination of size and volume distribution of micropores (0.35 – 2.0 nm)..

BET theory

The specific surface area of a powder is determined by physical adsorption of a gas on the surface of the solid and by calculating the amount of adsorbate gas corresponding to a monomolecular layer on the surface. Physical adsorption results from relatively weak forces (van der Waals forces) between the adsorbate gas molecules and the adsorbent surface area of the test powder. The determination is usually carried out at the temperature of liquid nitrogen. The amount of gas adsorbed can be measured by a volumetric or continuous flow procedure.

Multi-point measurements

The data are treated according to the Brunauer, Emmett and Teller (BET) adsorption isotherm equation:

A value of Va is measured at each of not less than 3 values of P/Po. Then the BET value:

is plotted against P/P_o according to equation (1). This plot should yield a straight line usually in the approximate relative pressure range 0.05 to 0.3. The data are considered acceptable if the correlation coefficient, r, of the linear regression is not less than 0.9975; that is, r² is not less than 0.995. From the resulting linear plot, the slope, which is equal to (C − 1)/V_mC, and the intercept, which is equal to 1/V_mC, are evaluated by linear regression analysis. From these values, V_m is calculated as 1/(slope + intercept), while Cis calculated as (slope/intercept) + 1. From the value of V_m so determined, the specific surface area, S, in m²·g^–1, is calculated by the equation:

A minimum of 3 data points is required. Additional measurements may be carried out, especially when non-linearity is obtained at a P/Po value close to 0.3. Because non-linearity is often obtained at a P/Po value below 0.05, values in this region are not recommended. The test for linearity, the treatment of the data, and the calculation of the specific surface area of the sample are described above.

Single point measurement

Normally, at least 3 measurements of Va each at different values of P/Po are required for the determination of specific surface area by the dynamic flow gas adsorption technique (Method I) or by volumetric gas adsorption (Method II). However, under certain circumstances described below, it may be acceptable to determine the specific surface area of a powder from a single value of Va measured at a single value of P/Po such as 0.300 (corresponding to 0.300 mole of nitrogen or 0.001038 mole fraction of krypton), using the following equation for calculating Vm:

The specific surface area is then calculated from the value of V_m by equation (2) given above.

The single-point method may be employed directly for a series of powder samples of a given material for which the material constant C is much greater than unity. These circumstances may be verified by comparing values of specific surface area determined by the single-point method with that determined by the multiple-point method for the series of powder samples. Close similarity between the single-point values and multiple-point values suggests that 1/C approaches zero.

The single-point method may be employed indirectly for a series of very similar powder samples of a given material for which the material constant Cis not infinite but may be assumed to be invariant. Under these circumstances, the error associated with the single-point method can be reduced or eliminated by using the multi-point method to evaluate C for one of the samples of the series from the BET plot, from which C is calculated as (1 + slope/intercept). Then V_m is calculated from the single value of V_a measured at a single value of P/P_o by the equation:

The specific surface area is calculated from V_m by equation (2) given above.

The following section describes the methods to be used for the sample preparation, the dynamic flow gas adsorption technique (Method I) and the volumetric gas adsorption technique (Method II).

Sample preparation: Outgassing: Before the specific surface area of the sample can be determined, it is necessary to remove gases and vapours that may have become physically adsorbed onto the surface after manufacture and during treatment, handling and storage. If outgassing is not achieved, the specific surface area may be reduced or may be variable because an intermediate area of the surface is covered with molecules of the previously adsorbed gases or vapours. The outgassing conditions are critical for obtaining the required precision and accuracy of specific surface area measurements on pharmaceuticals because of the sensitivity of the surface of the materials.

Conditions: The outgassing conditions must be demonstrated to yield reproducible BET plots, a constant weight of test powder, and no detectable physical or chemical changes in the test powder. The outgassing conditions defined by the temperature, pressure and time should be chosen so that the original surface of the solid is reproduced as closely as possible. Outgassing of many substances is often achieved by applying a vacuum, by purging the sample in a flowing stream of a non-reactive, dry gas, or by applying a desorption-adsorption cycling method. In either case, elevated temperatures are sometimes applied to increase the rate at which the contaminants leave the surface. Caution should be exercised when outgassing powder samples using elevated temperatures to avoid affecting the nature of the surface and the integrity of the sample.

If heating is employed, the recommended temperature and time of outgassing are as low as possible to achieve reproducible measurement of specific surface area in an acceptable time. For outgassing sensitive samples, other outgassing methods such as the desorption-adsorption cycling method may be employed.

The volumetric method (Ph. Eu.2.9.26 Method II)

Principle: In the volumetric method (see Figure 2.9.26.-2), the recommended adsorbate gas is nitrogen which is admitted into the evacuated space above the previously outgassed powder sample to give a defined equilibrium pressure, P, of the gas. The use of a diluent gas, such as helium, is therefore unnecessary, although helium may be employed for other purposes, such as to measure the dead volume.

Since only pure adsorbate gas, instead of a gas mixture, is employed, interfering effects of thermal diffusion are avoided in this method.

Procedure: Admit a small amount of dry nitrogen into the sample tube to prevent contamination of the clean surface, remove the sample tube, insert the stopper, and weigh it. Calculate the weight of the sample. Attach the sample tube to the volumetric apparatus. Cautiously evacuate the sample down to the specified pressure (e.g. between 2 Pa and 10 Pa). Alternatively, some instruments operate by evacuating to a defined rate of pressure change (e.g. less than 13 Pa/30 s) and holding for a defined period of time before commencing the next step.

If the principle of operation of the instrument requires the determination of the dead volume in the sample tube, for example, by the admission of a non-adsorbed gas, such as helium, this procedure is carried out at this point, followed by evacuation of the sample. The determination of dead volume may be avoided using difference measurements, that is, by means of reference and sample tubes connected by a differential transducer. The adsorption of nitrogen gas is then measured as described below.

Raise a Dewar vessel containing liquid nitrogen at 77.4 K up to a defined point on the sample cell. Admit a sufficient volume of adsorbate gas to give the lowest desired relative pressure. Measure the volume adsorbed, V_a. For multi-point measurements, repeat the measurement of V_a at successively higher P/P_o values. When nitrogen is used as the adsorbate gas, P/P_o values of 0.10, 0.20, and 0.30 are often suitable.

Reference materials: Periodically verify the functioning of the apparatus using appropriate reference materials of known surface area, such as α-alumina, which should have a specific surface area similar to that of the sample to be examined.

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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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Introduction

The physical properties of colloids (nanoparticles) and suspensions are strongly dependent on the nature and extent of the particle-liquid interface. The behavior of aqueous dispersions between particles and liquid is especially sensitive to the ionic and electrical structure of the interface.

Zeta potential is a parameter that measures the electrochemical equilibrium at the particle-liquid interface. It measures the magnitude of electrostatic repulsion/attraction between particles and thus, it has become one of the fundamental parameters known to affect stability of colloidal particles. It should be noted that that term stability, when applied to colloidal dispersions, generally means the resistance to change of the dispersion with time. Figure 2.5.12.5.1 illustrates the basic concept of zeta potential.

From the fundamental theory’s perspective, zeta potential is the electrical potential in the interfacial double layer (DL) at the location of the slipping plane (shown in Figure 2.5.12.5.1 ). We can regard zeta potential as the potential difference between the dispersion medium and the stationary layer of the fluid attached to the particle layer. Therefore, in experimental concerns, zeta potential is key factor in processes such as the preparation of colloidal dispersions, utilization of colloidal phenomena and the destruction of unwanted colloidal dispersions. Moreover, zeta potential analysis and measurements nowadays have a lot of real-world applications. In the field of biomedical research, zeta potential measurement, in contrast to chemical methods of analysis which can disrupt the organism, has the particular merit of providing information referring to the outermost regions of an organism. It is also largely utilized in water purification and treatment. Zeta potential analysis has established optimum coagulation conditions for removal of particulate matter and organic dyestuffs from aqueous waste products.

Brief History and Development of Zeta Potential

Zeta potential is a scientific term for electrokinetic potential in colloidal dispersions. In prior literature, it is usually denoted using the Greek letter zeta, Ζ, hence it has obtained the name zeta potential as Ζ-potential. The earliest theory for calculating Zeta potential from experimental data was developed by Marian Smoluchowski in 1903 (Figure 2.5.22.5.2 ). Even till today, this theory is still the most well-known and widely used method for calculating zeta potential.

Interestingly, this theory was originally developed for electrophoresis. Later on, people started to apply his theory in calculation of zeta potential. The main reason that this theory is powerful is because of its universality and validity for dispersed particles of any shape and any concentration. However, there still some limitations to this early theory as it was mainly determined experimentally. The main limitations are that Smoluchowski’s theory neglects the contribution of surface conductivity and only works for particles which have sizes much larger than the interface layer, denoted as κ_a (1/κ is called Debye length and a is the particle radius).

Overbeek and Booth as early pioneers in this direction started to develop more theoretical and rigorous electrokinetic theories that were able to incorporate surface conductivity for electrokinetic applications. Modern rigorous electrokinetic theories that are valid almost any κa mostly are generated from Ukrainian (Dukhin) and Australian (O’Brien) scientists.

Principle of Zeta Potential Analysis

Electrokinetic Phenomena

Because an electric double-layer (EDL) exists between a surface and solution, then any relative motion between the rigid and mobile parts of the EDL will result in the generation of an electrokinetic potential. As described above, zeta potential is essentially a electrokinetic potential which rises from electrokinetic phenomena. So it is important to understand different situations where electrokinetic potential can be produced. There are generally four fundamental ways which zeta potential can be produced, via electrophoresis, electro-osmosis, streaming potential, and sedimentation potential as shown from Figure 2.5.32.5.3 .

Calculations of Zeta Potential

There are many different ways of calculating zeta potential . In this section, the methods of calculating zeta potential in electrophoresis and electroosmosis will be introduced.

Zeta Potential in Electrophoresis

Electrophoresis is the movement of charged colloidal particles or polyelectrolytes, immersed in a liquid, under the influence of an external electric field. In such case, the electrophoretic velocity, v_e (ms^-1) is the velocity during electrophoresis and the electrophoretic mobility, u­­_e (m ² V ^-1 s ^-1 ) is the magnitude of the velocity divided by the magnitude of the electric field strength. The mobility is counted positive if the particles move toward lower potential and negative in the opposite case. And therefore, we have the relationship v_e­= u_eE, where E is the externally applied field.

Thus, the formula accounted for zeta potential in electrophoresis case is given in EQ, where ε_rs is the relative permittivity of the electrolyte solution, ε₀ is the electric permittivity of vacuum and η is the viscosity.ue =εrsε0ζη(2.5.1)(2.5.1)ue =εrsε0ζηve =εrsε0ζηE(2.5.2)(2.5.2)ve =εrsε0ζηE

There are two cases regarding the size of κa:

1. κa < 1: the formula is similar, 2.5.32.5.3 .
2. κa > 1: the formula is rather complicated and we need to solve equation for zeta potential, 2.5.42.5.4 , where yeζ= eζ/kTyeζ= eζ/kT , m is about 0.15 for aqueous solution.

ue=23εrsε0ζη(2.5.3)(2.5.3)ue=23εrsε0ζη32ηeεrsε0kTue=32yek−6[yek2−ln 2ζ{1−e−ζyek}]2+ka1+3m/ζ2e−ζyek2(2.5.4)(2.5.4)32ηeεrsε0kTue=32yek−6[yek2−ln 2ζ{1−e−ζyek}]2+ka1+3m/ζ2e−ζyek2

Zeta Potential in Electroosmosis

Electroosmosis is the motion of a liquid through an immobilized set of particles, a porous plug, a capillary, or a membrane, in response to an applied electric field. Similar to electrophoresis, it has the electroosmotic velocity, v_eo (ms ^-1 ) as the uniform velocity of the liquid far from the charged interface. Usually, the measured quantity is the volume flow rate of liquid divided by electric field strength, Q_eo,E (m ⁴ V ^-1 s ^-1 ) or diveided by the electric current, Q_eo,I (m ³ C ^-1 ). Therefore, the relationship is given by 2.5.52.5.5 .Qeo= ∫∫veodS(2.5.5)(2.5.5)Qeo= ∫∫veodS

Thus the formula accounted for Zeta potential in electroosmosis is given in EQ.

As with electrophoresis there are two cases regarding the size of κa:

• κa >>1 and there is no surface conduction, where Ac is the cross-section area and KL is the bulk conductivity of particle.
• κa < 1, 2.5.82.5.8 , where Δu =KσKLΔu =KσKL is the Dukhin number account for surface conductivity, KσKσ is the surface conductivity of the particle.

Qeo,E=−εrsε0ζηAc(2.5.6)(2.5.6)Qeo,E=−εrsε0ζηAcQeo,I=−εrsε0ζη1KL(2.5.7)(2.5.7)Qeo,I=−εrsε0ζη1KLQeo,I=−εrsε0ζη1KL(1+2Δu)(2.5.8)(2.5.8)Qeo,I=−εrsε0ζη1KL(1+2Δu)

Relationship Between Zeta Potential and Particle Stability in Electrophoresis

Using the above theoretical methods, we can calculate zeta potential for particles in electrophoresis. The following table summarizes the stability behavior of the colloid particles with respect to zeta potential. Thus, we can use zeta potential to predict the stability of colloidal particles in the electrokinetic phenomena of electrophoresis.

Instrumentation

In this section, a market-available zeta potential analyzer will be used as an example of how experimentally zeta potential is analyzed. Figure 2.5.42.5.4 shows an example of a typical zeta potential analyzer for electrophoresis.

The inside measuring principle is described in the following diagram, which shows the detailed mechanism of zeta potential analyzer (Figure 2.5.52.5.5 ).

When a voltage is applied to the solution in which particles are dispersed, particles are attracted to the electrode of the opposite polarity, accompanied by the fixed layer and part of the diffuse double layer, or internal side of the “sliding surface”. Using the following formula below of this specific Analyzer and the computer program, we can obtain the zeta potential for electrophoresis using this typical zeta potential analyzer (Figure 2.5.62.5.6 .