1. EIS Spectrum Analyser

EIS Spectrum Analyser is a standalone program for analysis and simulation of impedance spectra. The analyser routine is based on algorithms of the PDEIS spectrometer. In the original (potentiodynamic) version the impedance data analysis is applied on a 3D spectrum and gives dependences of the ac response components on electrode potential.

This standalone program has been adapted to solve a wide range of tasks in the common (stationary) impedance spectroscopy. In addition to data fitting to equivalent circuits with resistors, capacitors, inductors, constant phase, Warburg (3 types), user-defined and Gerischer elements, the EIS Spectrum Analyser provides various tests for data consistency and quality of fit. It has also a built-in impedance spectra simulation routine, tools for impedance data processing (subtraction of circuit elements and subcircuits, normalisation for electrode surface area) and plotting in various formats. The program is free for noncommercial use.

See: http://www.abc.chemistry.bsu.by/vi/analyser/

2. ZsimpWin

ZSimpWin is a EIS Data Analysis program that does not require user-input on initial values. ZSimpWin is an Electrochemical Impedance Spectroscopy (EIS) Data Analysis Software integrated with the 
VersaStudio software to provide straightforward and versatile equivalent circuit model fitting.  Innovative concepts have been implemented to achieve the following performance:

• Minimal user input: The user specifies a job by selecting a model for an impedance data set, and simply requests execution to ZSimpWin.  
• Automatic analysis: Parameters associated with the selected model are determined automatically. ZSimpWin assigns an initial guess of these parameters (default = Auto Setup option), starts computation using the initial guess, finds results, improves these results a number of times until desired results are obtained, and then saves the final results. 
• Batch Analysis: Setup a batch by including multiple jobs and process in sequence.  
• Output results in various forms: Results consist of plots, estimated parameters, and historical records of computation process.  Each or several combinations can be printed or copied to Windows clipboard. 
  
• Requires only mouse button clicks:  The whole process requires no entry of numbers or character strings.                                                                
• Compatible with Windows 10, 8 , 7 and XP.

See: https://www.ameteksi.com/products/software/zsimpwin

3. Zview

ZView software from Scribner Associates offers best-in-class equivalent circuit modeling. Fit common circuits instantly, generate publication-quality graphs quickly. ZView integrates easily with SAI measurement softwares, and supports testing hardware from Solartron, PAR, and others. Increase your data processing efficiency quickly and easily with ZView.

See: https://sai-zview1.software.informer.com/3.4/

Introduction of EIS

Electrochemical impedance spectroscopy (EIS) is one of the most powerful methods in the study of corrosion. The EIS method can be used to measure the rate or rate of corrosion, monitor corrosion, determine coating integrity, and study the mechanism of reactions. In this article, which is a compilation, translation and purification of references [1] and [2], the applications, limitations and benefits of this method are introduced. EIS is usually performed by applying an AC current signal to a state-steady electrochemical system and then measuring the current response. Because the amount of disturbance applied, the AC signal, is a small excitation signal, EIS is essentially a non-destructive technique. To apply this method requires a geometrically corroded cell that includes a reference electrode, as well as equipment capable of measuring and recording the electrical response of an electrochemical cell over a wide range of applied AC frequencies. When a small sine voltage is applied to an electrochemical system according to Equation 9, a sine current response in the form of Equation 2 will be observed. Due to the lack of rapid response of relaxation processes or the release of dipoles, the rotation of bipolar components in response to the applied alternating electric field results in a phase change.

Typical measurements in EIS are usually made in a three-electrode system, as shown in Figure 9. The entire set includes an electrochemical cell, a frequency generator, a frequency response analyzer (FRA), and a computer that is used to control experiments and store information. A potentiostat is used to control the electrode potential. The FRA is the heart of the system that calculates the imaginary and real parts of impedance. The frequency studied is usually in the cell range. 0.01–100,000 Hz (cycles / s) Electrochemical, the test material is embedded as an electrode working. Electrode counters, which must be neutral and not involved in the electrochemical reaction, are usually made of Pt, gold, or graphite. Reference electrodes are usually conventional saturated calomel electrodes (SCE) or AgCl / Ag electrodes. However, in many applications, such as thin electrolyte layers or in high temperature environments, conventional reference electrodes do not work properly. In these cases, systems without conventional reference electrodes should be used. As an example, we can refer to the two-electrode system, which usually consists of two identical electrodes consisting of test materials (Figure 2-a) and is widely used in atmospheric corrosion monitoring [5. [Figure 2-b) Indicates that it is used to monitor high-corrosion corrosion. , Multi-electrode array (Figure 2-c) can be used for EIS monitoring.

An uncompensated electrolyte resistance (Rs), a specific capacitance value related to the coating applied to the metal surface (Cc), a hole resistance in the coating of resistance pathways (pore solution resistance) (Rcp) in the coating where ions are transported, a The specific capacitance corresponding to the double layer in the solution / metal (Cdl) and a resistance (Rp) which is the resistance of the charge transfer process (ie corrosion), and in other words, the resistance to polarization at the solution / metal interface. In Beaunier rectified circuits, usually other additional components, such as the constant phase element (CPE), the phase component of the inductance or induction coefficient (L) and the resistor (W (Warburg,) replace the resistor or capacitor. Special capacitance, accuracy and quality of experimental data fitting with these circuits are improved, but the physical interpretation of the results will be ambiguous, this is because the CPE module can not be easily obtained with capacitor capacitance, and the capacity power calculation is calculated. Capacitor from CPE parameters requires accurate knowledge of the physical reasons for CPE behavior [7.] An example of a Nyquist diagram and its equivalent wind diagram in doubt ل 4 is given. The position of the equivalent circuit components in these diagrams is given on each diagram. In addition to common, simple equivalent circuit models, more complex physical models are sometimes used to interpret EIS data obtained from more complex systems. An example is the line transmission model (TML), which was first used by Levie de in his research on porous electrodes [11] [TML model and its modified models for analyzing EIS data on atmospheric corrosion under electrolyte layers Thin [5] as well as stress corrosion [12] have been used.

Atmospheric corrosion is an electrochemical process that usually occurs beneath a thin electrolyte surface layer, in the presence or absence of salt contaminants and dissolved gases in these layers. It has been shown that the atmospheric corrosion rate of metals depends on the thickness of the electrolyte thin film. The thickness of the electrolyte layer affects the rate of oxygen transfer through the electrolyte layer and the dissolution of corrosion products. The rate of oxygen transfer determines the rate of cathodic reaction (in neutral and alkaline solutions) and the dissolution of corrosion products determines the anodic process. Monitoring or corrosion of thin electrolytic films using conventional electrochemical methods is challenging The electrolyte is thin, very high, leading to a sharp drop in ohmic potential and a non-uniform current distribution that makes it difficult to measure the corrosion rate [5. The solution resistance is estimated from the impedance measured in the high frequency range of the EIS spectrum, and the sum of the resistivity (Rp) and the solution resistance (RS) from the impedance in the low frequency range. Figure 4b: The calculated resistive palliation is then converted to the corrosion rate of the metal. To study atmospheric corrosion of the metal surface under thin electrolytic layers (100010-1000 ~) by EIS, it is possible to expose the corrosion cell to the atmosphere. weather Use outside or use laboratory-drier simulation cycles [19–13,10,9,5. Used in epoxy resin (Figure 2-A) to make cells. The study can be done in two ways: either the impedance spectrum is recorded over a wide range of applied frequencies or the impedance value is checked continuously at two constant frequencies. The study of EIS spectrum in a wide frequency range has shown that a one-dimensional equivalent circuit model called TML can be used to model the corrosion rate in these systems [5. Palrization is calculated from the impedance difference measured at the above two frequencies. The corrosion rate is then calculated using the polarization resistance [5]. Nishikata et al. [5] also used shoulder-shaped electrodes to study atmospheric corrosion to EIS. Impedance information was monitored at 10 mHz and 10 kHz. The results showed that the inverse of the mean impedance at low frequency completely corresponds to the corrosion rate obtained by gravimetry. Wetting of Time also occurs when the amount of solution conductivity or high frequency impedance image (Rs image) exceeds a threshold value. One of the disadvantages of this method in the study of atmospheric corrosion is that if the metal surface is covered with a thick layer of corrosion products, the low frequency impedance can not be equated to the polarization resistance. Finally, Ma et al. [20] used a complex multi-electrode system to study atmospheric corrosion and found the results to be more accurate than two-electrode systems. 3.2 Corrosion of reinforced concrete Rebar (in concrete is the main reason for reducing the life of reinforced concrete structures that are exposed to strong corrosive environments) such as marine environment. Therefore, reinforcement corrosion monitoring is very important to assess the health status of reinforced concrete. Various methods are used to evaluate corrosion in reinforced concrete, and electrochemical methods are among the most common. Among these, EIS is an attractive technique because, as mentioned earlier, it is almost a non-destructive method. In addition, EIS is suitable for environments with very high strength, such as concrete, because it is essentially a transient method and does not require the system to be in a stable state [21. [In steel systems (reinforcement) / Concrete, information Various parameters such as the presence of surface films, concrete properties, joint joint corrosion and mass transfer phenomena can be obtained from the EIS method [21. [22] [In addition, the high-frequency impedance of information in Moore Provides dielectric properties of concrete, and low-frequency impedance information on the properties of passive films (surface oxide layers) on steel. Studies that began three decades ago have proven the validity of EIS as a technique for studying rebar corrosion in concrete, both experimentally and theoretically. John et al. (9189) monitored corrosion of rebar in high porosity concrete using EIS. They obtained both the corrosion rate of the steel rebars and the information on the steel surface layers. Later, more fundamental work was done by McDonald et al. To establish the application of EIS in the detection of rebar corrosion in concrete [3]. In this work, the rebar was simulated as a one-dimensional electrical transmission line. Their results show that imaginary and real components of impedance and phase angle can be used to detect corrosion of rebar embedded in concrete, but this is only possible at very low frequencies (for example, 1 mHz). It was also found that monitoring the peak voltage at the concrete surface just above the rebar helps to fully detect corrosion.

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

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

Step 1: ¹H-NMR

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

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

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

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

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

Step 2: ¹H-¹H COSY

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

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

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

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

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

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

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

Step 3: ¹³C-NMR

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

Step 4: DEPT-45, 90, and 135

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

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

Step 5: ¹H-¹³C HSQC

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

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

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

Step 6: ¹H-¹³C HMBC

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

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

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

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

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

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

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

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

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

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

The following features lead to the nmr phenomenon:

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

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

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

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

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

Chemical Shift

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

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

The compounds referred to above share two common characteristics:

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

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

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

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

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

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

Signal Strength

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

Hydroxyl Proton Exchange and the Influence of Hydrogen Bonding

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

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

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

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

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

π-Electron Functions

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

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

Solvent Effects

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

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

Spin-Spin Interactions

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

1,2-dichloroethane		1,1-dichloroethane

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

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

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

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

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

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

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

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

Some Examples

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

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

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

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

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

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

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

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

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

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1. NMRshiftdb

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

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

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

2. ACD/NMR

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

Powerful NMR Interpretation Software | Highlights

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

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

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

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

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

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

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

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

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

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

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

Our XRD interpretation includes:
1. Phase determination
2. Determination of diffracted planes
3- Calculation of crystalline size and microstrain
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1. Profex

What is Profex?

Profex is a graphical user interface for Rietveld refinement of powder X-ray diffraction data with the program BGMN. It provides a large number of convenience features and facilitates the use of the BGMN Rietveld backend in many ways.

• Various options and output formats to create publication-quality graphs
• Main window
• Display hkl line positions from the internal reference structure database
• Powerful text editors support syntax highlighting and various convenience features
• A context help provides descriptions of all refinement parameters
• After the refinement, results are summarized (bottom right)
• Show the refined chemical composition (bottom right)
• A powerful search-match module for phase identification
• CIF / XML import editor to convert CIF or ICDD XML structure files to the native STR format
• Compute electron density maps (Fobs, Fcalc, or difference fourier maps)
• Graphical instrument editor to edit the fundamental parameters
• Generic non-linear curve fitting module
• Various options and output formats to create publication-quality graphs
• Main window

Key features

• Support for a variety of raw data formats, including all major instrument manufacturers (Bruker / Siemens, PANalytical / Philips, Rigaku, Seifert / GE, and generic text formats)
• Export of diffraction patterns to various text formats (ASCII, Gnuplot scripts, Fityk scripts), pixel graphics (PNG), and vector graphics (SVG)
• Batch conversion of raw data scans
• Automatic control file creation and output file name management
• Conversion of CIF and ICDD PDF-4+ XML structure files to BGMN structure files
• Export of refined crystal structures to CIF and Castep CELL format
• Internal database for crystal structure files, instrument configuration files, and predefined refinement presets
• Computation of chemical composition from refined crystal structures
• Batch refinement
• Export of refinement results to spreadsheet files (CSV format)
• Context help for BGMN variables
• Syntax highlighting
• Enhanced text editors for structure and control file management and editing
• Generic support for FullProf.2k as an alternative Rietveld backend to BGMN
• And many more…

Profex runs on Windows, Linux, and Mac OS X operating systems and is available as free software licensed under the GNU General Public License (GPL) version 2 or any later version.

Video tutorials

August 12, 2020. Check out our brand new YouTube channel Profex Tutorials. We will periodically publish new tutorials for selected topics. The first episode explains installation and setup of Profex on three different platforms:https://www.youtube.com/embed/vaWBjTNWG7U?feature=oembed

Profex 4.2 released

August 05, 2020. Profex, our software for Rietveld refinement of powder X-ray diffraction data (XRD), continues to gain popularity and is now established worldwide in the material and earth sciences communities. With the new version 4.2, it has received some long-awaited features that make it easier to use for new and experienced users. As always, Profex remains available as open-source software and is free for academic and commercial use. Visit the What’s new page for an overview of the new features, and download the latest version for Windows, Mac OS or Linux from the Download page.

Feature highlights in version 4.2

Import of CIF structure files has further been improved. Most CIF files require no user input anymore. Wyckoff symbols are determined automatically.

Creating instrument configurations has always been a major obstacle for new users. A brand new graphical instrument editor is easier and more attractive to use. It guides users through the process of creating configuration files for their own devices.

The search-match module for phase identification was introduced with Profex 4.0. With version 4.2, it supports chemical restrictions, which gives more control over the search process and improves the match rate and processing speed

See https://www.profex-xrd.org/

2. OpenXRD

OpenXRD is a program for the analysis of X-ray diffraction data.It will comprise scan treatment (background substraction, peak hunting) as well as mineral identification. OpenXRD will read almost any available data format. OpenXRD is free software and published under the GPL.

We will try to establish a free file with mineral data, fed by scientists and given back to scientists. OpenXRD will be available for Linux/Unix, Windows, and, perhaps Macintosh computers.

Released under GNU General Public License version 2.0 (GPLv2) 

OpenXRD is a free software application from the Other subcategory, part of the Graphic Apps category. The app is currently available in English and it was last updated on 2001-12-27. The program can be installed on All 32-bit MS Windows (95/98/NT/2000/XP) All POSIX (Linux/BSD/UNIX-like OSes) OS X Linux.
OpenXRD (version ) is available for download from our website. Just click the green Download button above to start. Until now the program was downloaded 13911 times. We already checked that the download link to be safe, however for your own protection we recommend that you scan the downloaded software with your antivirus.

See: https://openxrd.soft112.com/

3. FullProf

What is FullProf?

The FullProf program has been mainly developed for Rietveld analysis (structure profile refinement) of neutron (constant wavelength, time of flight, nuclear and magnetic scattering) or X-ray powder diffraction data collected at constant or variable step in scattering angle 2theta. The program can be also used as a Profile Matching (or pattern decomposition using Le Bail method) tool, without the knowledge of the structure. Single crystal refinement can also be performed alone or in combination with powder data. Time of flight (TOF) neutron data analysis is also available. Energy dispersive X-ray data can also be treated but only for profile matching.

Features:

• X-ray diffraction data: laboratory and synchrotron sources
• X-ray diffraction data: laboratory and synchrotron sources
• One or two wavelengths (eventually with different profile parameters)
• Scattering variables: 2theta (in degrees), TOF (in microseconds), energy (in KeV)
• Background: fixed, refinable points or polynomial coefficients, Fourier filtering
• Choice of peak shape for each phase: Gaussian, Lorentzian, modified Lorentzian, pseudo-Voigt, Pearson-VII, Thompson-Cox-Hastings (TCH) pseudo-Voigt, numerical, split pseudo-Voigt, convolution of a double exponential with a TCH pseudo-Voigt for TOF
• Multi-phase (up to 16 phases)
• Preferred orientation: two functions available
• Absorption correction for different geometries. Micro-absorption for Bragg-Brentano set-up
• Choice between three weighting scheme: standard least-square, maximum likelihood and unit weights
• Choice between automatic generation of hkl and/or symmetry operators and file given by user
• Magnetic structure refinement (crystallographic and spherical representation of the magnetic moment). Two methods: describing the magnetic structure in the magnetic unit cell or making use of the propagation vectors using the crystallographic unit cell. This second method is necessary for incommensurate magnetic structures
• Automatic generation of reflections for an incommensurate structure with up to 24 propagation vectors. Refinement of propagation vectors components in reciprocal lattice units
• hkl-dependence of FWHM for strain and size effects
• hkl-dependence of the position shifts of Bragg reflections for special kinds of defects
• Profile Matching: the full profile can be adjusted without prior knowledge of the structure (needs only good starting cell parameters and profile parameters)
• Quantitative analysis withour need of structure factor calculations
• Chemical (distances and angles) and magnetic (magnetic moments) slack constraints. They can be generated automatically by the program
• The instrumental resolution function (Voigt function) may be supplied in a file. A microstructural analysis is then performed
• Form factor refinement of complex objects (plastic crystals)
• Structural or magnetic model could be supplied by an external subroutine for special purposes (rigid bodies TLS is the default, polymers, small angle scattering of amphifilic crystals, description of incommensurate structure in real direct space, etc)
• Single crystal data or integrated intensities can be used as observations (alone or in combination with a powder profile)
• Neutron (or X-ray) powder patterns can be mixed with integrated intensities of X-ray (or neutron) for single crystal or powder data
• Full multi-pattern capabilities. The user may mix several powder diffraction patterns (eventually heterogeneous: X-rays, TOF neutrons, etc.) with total control of the weighting scheme
• Montecarlo/Simulated Annealing algorithms have been introduced to search the starting parameters of a structural problem using integrated intensity data

See: https://www.ill.eu/sites/fullprof/php/programs.html

4. PowDLL

PowDLL is a .NET dynamic link library used for the interconversion procedure between variable formats of Powder X-Ray files. The DLL is capable of handling the most common file formats (binary and ASCII). The library can be used as a reusable component with any .NET language or as a standalone utility.

See: http://users.uoi.gr/nkourkou/powdll/

5. Software Ic

The software packages currently developed at IC are:

• Sir: a widely used package for the solution and refinement of macro and small  molecules using either X-ray or electron diffraction single-crystal data.
• EXPO2014/EXPO2013: an integrated package for the indexation of a powder diffraction pattern, the extraction of integrated intensities, the space group determination, the crystal structure solution viaDirect Methods and/or by a direct-space approach, and the structure refinement by the Rietveld technique.
• QualX2.0/QualX: a computer program for phase identification using powder diffraction data.
• Quanto: a Rietveld program for quantitative phase analysis of polycrystalline mixtures from powder diffraction data.
• SunBIM: a suite of programs for the supra- and sub-molecular X-ray imaging of nano and bio materials with SAXS, WAXS, GISAXS and GIWAXS techniques
• RootProf: An interactive, general purpose tool for processing unidimensional profiles with specific applications to X-ray diffraction measurements
• OChemdb: an on-line portal, using an appropriately designed database of already solved crystal structures, for searching and analysing crystal-chemical information of organic, metal-organic and inorganic structures, and providing statistics on desired bond distances, bond angles, torsion angles, and space groups.

The software is free for academic and non-profit research institutions, while it requires the payment of a license fee to commercial users.

To download the software packages, academic and no-profit users must first register to the web site, choosing the software packages of their interest and accepting all the terms and conditions of the on-line Academic License Agreement. After completing the registration, users will receive a confirmation e-mail and will be allowed tologin to download the selected packages.

Registered users can download freely the previous versions of our packages (such as Sir97, Sir2004, EXPO2004, EXPO2009, EXPO2013 and QualX) for non-commercial use from the Old Software section of the web site.

Commercial users must fill the Order Form and send it by email or fax to our office, together with a signed copy of the Commercial License Agreement.

The license covers the use of all the requested programs under all the supported operating systems for an unlimited time on an unlimited number of computers.

See: http://www.ba.ic.cnr.it/softwareic/

X-ray diffraction, abbreviated as XRD, is an old and well-known technique for studying the structure and properties of crystals. The basis of the XRD method is single-color X-ray diffraction by atoms of a substance. Diffraction generally occurs when light strikes an obstacle. When it hits an obstacle, the light beam either bends and propagates or passes through tiny pores on the obstacle. The diffraction phenomenon is visible for all electromagnetic rays, including X-rays. Interference between the X-ray electric vector and the electrons of the material through which the beam passes can be constructive or destructive. In constructive interference, the X-ray diffraction pattern is characterized by a pattern of atomic arrangement in a regular crystal structure. In fact, when an X-ray is shone on a crystal, its diffraction occurs according to the structural characteristic pattern of the crystal.

Bragg’s Law

Due to the regular structure in a crystal, it can be assumed to be regular plates with specified intervals. Diffraction occurs because the distance between the regular layers of a crystal is close to the X-ray wavelength. The X-ray strikes the angle θ with the surface, causing part of the initial beam to propagate at the same angle θ and the other part to enter the inner plates of the crystal. This process is repeated for many pages of a crystal. The distance traveled by X-rays in contact with surface atoms is less than the distance traveled in contact with the inner layer of the crystal. The distance traveled depends on the distance between the two layers and the angle of X-ray radiation.

X-ray diffraction – XRD – analium
Figure 1 X-ray collision and its diffraction method in XRD method

Figure 1 shows that the difference in path traveled between the first and second layers is equal to:

BG = BC = dSinθ (1)

Constructive diffraction occurs when the difference in the length of the X-ray path is an exact multiple of the wavelength. Therefore, for the total distance traveled, it is equal to:

(2) nλ = 2dSinθ

This equation is known as the Bragg relation. In this relation λ the wavelength of the source d is the distance between the crystal plates and θ is the angle of incidence and n is an integer. Note that based on this equation, the X-ray will only be reflected at specific angles obtained from the rearrangement of Equation 1:

   (3) Sinθ = nλ / 2d

An XRD spectrum is obtained by plotting the intensity in θ. By changing θ and knowing the wavelength of the source λ, d is obtained at any moment.

XRD device
Similar to many XRD component analyzers, they include a source, a wavelength selector, a sample location, a detector, and a signal converter. Figure 2 shows an overview of an X-ray diffraction device (XRD).

X-ray diffraction device (XRD) – analium
Figure 2 Overview of an X-ray diffraction device (XRD)

X-ray tubes with tungsten filament are commonly used as sources. Source intensity can be adjusted by adjusting the current flowing through it. The beam wavelength can also be controlled by applying applied voltage control.

A monochromator or filter is used to create a monochromatic beam. A variety of scintillation and semiconductor gas-filled detectors are used in XRD devices.

Preparation of XRD samples
The sample must be well ground to obtain a homogeneous powder from the crystalline sample. In this case, it is possible to place a large number of crystalline particles in the desired direction and in accordance with the Bragg equation. The samples are mixed with a suitable adhesive and then molded.

Crystallization is a very important step in the preparation of XRD samples and requires special skills and expertise.

In powder X-ray diffraction, the diffraction pattern is obtained from the powder form of the sample. XRD powder is usually lighter and simpler than crystalline diffraction. Because in powder form, there is no need to prepare single crystal. In the powder method, the mass pattern (bulk) of the sample is also obtained.

XRD Analysis Tips
The sample must have a crystalline structure.
Has little accuracy in quantifying phases (phases with values ​​less than 5% are not detected).
In qualitative analysis, the element does not perform well.
The method is fast and powerful and with convenient access.
XRD Application Background
Determining the crystal structure and accurate measurement of lattice parameters
Determining fuzzy diagrams
Chemical identification and analysis
Determining the quality and direction of plates in single crystals

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Raman spectroscopy is a powerful vibrational technique used widely in chemistry, materials science, geology, biology, and industrial laboratories. To make the most of this analytical tool, proper software is essential for spectrum acquisition, visualization, processing, and interpretation. While many commercial solutions exist, there are also free and open tools that are capable, flexible, and ideal for researchers, students, and laboratories on a budget.

1. Spectragryph – Free Optical Spectroscopy Software

Spectragryph Official Site (free for academic and private use)

Spectragryph is a versatile and widely-used optical spectroscopy package supporting Raman, FTIR, UV-VIS, fluorescence, LIBS, and XRF formats. It allows users to import multiple vendor file formats, plot and edit spectra, perform baseline correction, smoothing, peak labeling, and automated batch processing. The software is free for academic and private use, and offers features such as spectral database search, multi-spectrum display, and hardware control for live data acquisition. Spectroscopy

Key Advantages:

• Multi-format support and drag-and-drop data handling
• Batch export, undo/redo, and interactive visualization
• Ability to integrate free spectral libraries like RRUFF mineral spectra Spectroscopy

⚠️ Note: Distribution and licensing for commercial use require a paid license; free academic licenses may need re-verification over time. Spectroscopy

2. RRUFF – Free Raman Database & Identification Tools

RRUFF Raman & Mineral Database (with download tools)

The RRUFF Project provides a comprehensive open database of Raman spectra, X-ray diffraction patterns, and chemical data for thousands of minerals. Although the original “CrystalSleuth” software (used to search and compare spectra) is bundled in legacy downloads, the RRUFF data itself can be imported into many newer tools and databases for free identification and research purposes. RRUFF

Why It’s Useful:

• Large library of reference Raman spectra for minerals
• Free access for educational and research use
• Can be used with software such as Spectragryph or third-party viewers RRUFF

3. Raman Tool Set – Free Basic Raman Processor

While not listed in your original links, Raman Tool Set is a notable free program dedicated to Raman data analysis. It supports baseline correction, normalization, smoothing, and chemometric functions like PCA and cluster analysis — all valuable for simple spectral processing without cost. Wikipedia

Commercial Software You May Compare

Though not free, these commercial solutions represent the professional standard in Raman data handling. Including them in your article gives context about what users gain by paying — and helps highlight the value of free alternatives.

Bruker OPUS

Bruker’s OPUS software is a professional spectroscopy suite supporting IR, NIR, and Raman data acquisition and evaluation. It offers advanced visualization, database tools, and compliance-ready features for regulated environments. Bruker

Thermo Scientific GRAMS/AI

GRAMS/AI is a robust spectroscopy platform often bundled with Thermo instruments. It provides extensive processing and analysis functions for Raman as well as other spectroscopies, widely used in research and industry (Thermo Scientific documentation). Invalid URL

Renishaw Raman Software

Renishaw offers dedicated software for its Raman instruments, focused on spectral acquisition, processing, and material identification. This platform integrates features for instrument control and data analysis tailored to Renishaw hardware. Invalid URL

HORIBA LabSpec 6

LabSpec 6 is a comprehensive spectroscopy suite used with HORIBA Raman systems. It includes visualization, hyperspectral mapping, baseline correction, multivariate analysis, and reporting tools. This software illustrates the advanced feature sets available in commercial packages. Spectroscopy Online

Raman-Analytik Software

Some spectrometer vendors (e.g., Raman-Analytik) provide their own analysis tools with fast fingerprinting, background removal, and database search capabilities — often bundled with hardware or available for download. Raman Analytik

Tips for Using Free Raman Software

• Combine tools with databases: Pair free analysis software (like Spectragryph) with open spectral libraries such as RRUFF for improved identification.
• Watch format compatibility: Free tools vary in supported file types — converting proprietary formats (e.g., Bruker or Renishaw raw files) may require intermediate export or converters.
• Consider workflow needs: If advanced imaging, 3D mapping, or multivariate quantification is required, commercial packages may offer higher performance — but for basic peak analysis, free tools suffice.

Introduction to Raman spectroscopy

When an electromagnetic radiation passes through a transparent medium, existing species scatter part of the beam in all directions. In 1928, C. V. Raman discovered that the wavelength corresponding to a small fraction of the radiation scattered by certain molecules was different from the wavelength of the original radiation (ie, inelastic scattering occurs). Wavelengths vary depending on the molecular structure of the compounds. Raman spectroscopy is based on the analysis of these differences to determine the molecular structure of different compounds [1].

Scattering is a physical process in which a type of radiation such as light, sound, or even a beam of moving particles (such as ions, electrons, etc.) collides with particles or different surfaces in a direct path in which He is moving and deviates and is forced to move in one or more other directions (Figure 1). Scattering usually occurs in all directions [2].
Due to the collision of light with matter, we will have two types of scattering according to the wavelength of the scattered radiation:

1. Rayleigh scattering is caused by particles that are much smaller than the wavelength of the radiation. Due to this type of scattering, the radiation wavelength does not change and is also classified as elastic scattering. The most obvious example of this type of scattering is the blue color of the sky, which occurs due to the scattering of shorter wavelengths in the visible spectrum.
2. Raman scattering, in which the initial wavelength changes due to the transfer of energy between the photons and the matter molecules, and the wavelength increases due to the loss of energy, or the wavelength decreases due to the capture of energy. Finds. The magnitude of these energy changes (whether decreasing or increasing) is proportional to the frequency of the molecular vibrations of the light scattering species. Raman scattering will be divided into two general categories. The first group, which has a longer wavelength (less energy) than the original radiation, is called Stokes, and the second group, which has a shorter wavelength (more energy) than the original radiation, is known as anti-Stokes. 2].
3. Spectrum Raman
   Figure 2 shows a part of a Raman spectrum for CCl4 species in which the sample is irradiated with a laser source with a wavelength of 488 nm. In a horizontal axis Raman spectrum, generally in terms of the scattered radiation wave number (ῡ) or, as shown below, in terms of the changes made in the scattered beam wave number (ῡ2) relative to the source radiation wave number (ῡ1), ie in terms of the wave number changes ( 2) (which in practice indicates the scatter created in a specific wave number). While the vertical axis shows the intensity of the peaks in relative terms. Note that the relationship between the wave number of a radiation and its wavelength (λ), frequency (υ) and energy (E) is as follows and has a unit of cm-1:
   ῡ = 1 / λ and ῡ = υ / c
   E = hυ = hcῡ

As can be seen in the figure below, the Stokes lines are more intense, which is justified by their higher probability of occurrence, as photons are more likely to lose energy due to contact with the material environment than to receive them. Is energy. Another thing to keep in mind is that the amount of Raman Shifts (written numerically above the peaks) is independent of the laser wavelength used to excite the sample. It should also be noted that Riley scattering is located exactly at the wavelength equal to the source wavelength, its displacement rate is zero and its intensity is much higher than the Stokes and anti-Stokes lines [2].

Before continuing the discussion, it is necessary to point out that due to the continuity of the material, in order to better understand the following sections, it is better to first read the article on infrared spectroscopy. Below, due to the great similarity and complementarity of infrared and Raman spectroscopy techniques, a comparison is made on the differences.

3. Investigation of differences between Raman technique and infrared spectroscopy
   Studies have shown that shifts in the wavelength (wave number) of the source due to Raman scattering are in the infrared spectral range. In simpler terms, the difference between the energy of the source radiation and the scattered radiation is equal to the energy of the waves in the middle infrared range (see the article Infrared Spectroscopy). As mentioned in the article on infrared spectroscopy, this amount of energy is sufficient only for transitions between molecular vibrational levels of molecules (Molecular Vibrational Levels), and in this respect two methods are similar to each other. The Raman scattering spectrum and the infrared spectrum for a particular species are often very similar. There are many similarities between the two methods, but it should be noted that despite these similarities, the two techniques are different in principle and theory in that they are usually used as a complement to each other. In the paper introducing the infrared spectroscopy method, it is mentioned that one of the necessary conditions for a particular bond to be active in infrared spectroscopy is to cause a net change in dipole moment due to the absorption of radiation (Refer to the main article).

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Fourier Transform Infrared Spectroscopy (FTIR) is one of the most widely used analytical techniques in materials science, chemistry, pharmaceuticals, polymers, nanotechnology, and environmental studies. While collecting an FTIR spectrum is straightforward, extracting reliable structural and chemical insights requires proper software.

In this guide, we introduce the most useful free, trial, and professional FTIR data-analysis tools available today—along with a note about Analyzetest.com, a professional service for accurate spectrum interpretation.

1. Protea Free FTIR Software

Website: protea.ltd.uk/free-ftir-software.html

Protea offers a fully free FTIR software package designed for spectral visualization, peak picking, baseline correction, and spectral library comparison. It is lightweight, beginner-friendly, and suitable for quick analysis of standard FTIR spectra in educational or research environments.

2. IRPal

Website: irpal.soft112.com

IRPal is a simple but effective tool for peak identification and functional group prediction. It is particularly useful for students or early researchers who need a fast interpretation of IR absorption bands based on standard chemical references.

3. OriginLab (Fully Functional Trial)

Website: originlab.com/demodownload.aspx

OriginLab is one of the most powerful scientific data-analysis platforms. With its fully functional trial version, users can perform:

• Advanced curve fitting
• Peak deconvolution
• Baseline subtraction
• 2D/3D plotting
• Customizable script-based analysis (LabTalk, Python)

For FTIR, OriginLab excels at Gaussian/Lorentzian peak fitting, especially when analyzing overlapping carbonyl, hydroxyl, or phosphate bands.

4. irAnalyze (30-Day Trial)

Website: labcognition.com/en/irAnalyze.html

irAnalyze provides sophisticated tools for:

• Spectral interpretation
• Band assignment
• Functional group identification
• Library search
• Chemometric processing

Its algorithms are optimized for polymer and organic chemistry applications.

5. VibSpec / IRIS (10-Day Free Access)

Website: vibspec.com/html/software_eng.html

VibSpec includes IRIS, an advanced interpretation tool based on vibrational spectroscopy theory. It is designed for researchers who want:

• Automatic interpretation suggestions
• Group frequency analysis
• Raman-IR combined interpretation
• A knowledge-based expert system

Although the free usage is limited to 10 days, the software is highly valuable for academic research.

6. ir-spectra (Free Demo)

Website: ir-spectra.com

ir-spectra is a user-friendly spectral viewer supporting:

• Baseline correction
• Peak assignment
• Library matching

Its simplicity makes it suitable for quick checks and undergraduate laboratories.

7. Gnuplotting (Free, Open Source)

Website: gnuplotting.org

Gnuplot is an open-source plotting engine widely used for scientific visualization. Although not an FTIR-specific tool, it provides:

• High-quality spectral plotting
• Curve fitting
• Batch processing
• Script-based automation

It is ideal for researchers who prefer command-line tools or need automated analysis pipelines.

8. QtiPlot

Website: qtiplot.com

QtiPlot is an affordable Origin-like software offering:

• Signal processing
• Spectral smoothing
• Peak fitting
• Customizable graphs

It is commonly used in universities as a low-cost alternative to OriginLab.

9. ACD/Spectrus Processor (30-Day Trial)

Website: acdlabs.com/software-solutions/acd-spectrus-processor/

Spectrus Processor is a professional-grade tool for handling FTIR, Raman, NMR, MS, and other spectroscopic data. Its FTIR capabilities include:

• Intelligent peak assignment
• Structure-spectrum correlation
• Spectral library management
• Multi-technique data integration

This is an excellent solution for industrial laboratories.

10. Bio-Rad Spectroscopy Software (Two-Week Trial)

Website: bio-rad.com/en-in/category/spectroscopy-software

Bio-Rad provides robust solutions for FTIR and Raman analysis, offering:

• KnowItAll spectral libraries
• Automatic band assignment
• Advanced data processing
• Chemometrics and PCA

Widely trusted in professional and industrial environments.

Need Expert FTIR Interpretation?

Software is powerful, but correct interpretation still requires expertise—especially when dealing with:

• Overlapping peaks
• Polymer composites
• Nanomaterials
• Organic/inorganic hybrid systems
• Plant-extract–based green synthesis
• Reaction mechanism analysis

If you need accurate, publication-ready FTIR analysis, you can use our professional service:

👉 Analyzetest.com — Expert Analysis for FTIR, XRD, SEM, EIS, and all Scientific Data

We provide:

• Peak assignment
• Functional group identification
• Comparison between raw materials, intermediates, and final products
• Quality-control validation
• Complete report preparation for theses, papers, and industrial R&D

Whether you’re a student, researcher, or industry professional, our team can help you transform your spectra into meaningful scientific insights.