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What is Mxene?

Mxene is a class of two-dimensional (2D) transition metal carbides, nitrides, and carbonitrides that exhibit unique properties such as high electrical conductivity, excellent mechanical strength, and high surface areas. Mxenes were first discovered in 2011 by researchers at Drexel University and have since gained significant attention in the scientific community due to their potential applications in various fields such as energy storage, catalysis, and sensing.

There are several different types of mxenes that have been synthesized, with the most common being titanium carbide (Ti3C2), which is typically prepared by selectively etching aluminum atoms from layered ternary carbides known as MAX phases. Other types of mxenes include vanadium carbide (V2C), niobium carbide (Nb2C), and tantalum carbide (Ta4C3), among others.

The preparation of mxenes typically involves the following steps:

1. Synthesis of MAX phase: The first step in preparing mxenes is to synthesize the parent MAX phase material, which is a layered ternary compound consisting of a transition metal (M), a group A element (A), and carbon or nitrogen (X). Common MAX phases include Ti3AlC2, V2AlC, and Nb4AlC3.

2. Selective etching: The next step involves selectively etching the A element (usually aluminum) from the MAX phase using strong acids or other etchants. This process leaves behind a layered structure of transition metal carbides, nitrides, or carbonitrides, which are the mxene precursors.

3. Intercalation: In some cases, additional intercalation steps may be performed to introduce other elements or molecules between the layers of mxene to modify its properties.

4. Delamination: The final step in preparing mxenes involves delaminating the layered structure to obtain single or few-layered sheets of mxene. This can be achieved through mechanical exfoliation, sonication, or other methods.

Once prepared, mxenes can be further functionalized or integrated into various devices and applications. Their unique combination of properties makes them promising candidates for use in energy storage devices such as batteries and supercapacitors, as well as in catalysis, electromagnetic shielding, and water purification.

Therefore, mxenes represent a new class of 2D materials with exciting potential for a wide range of applications. Continued research into their synthesis, properties, and applications will likely uncover even more possibilities for these versatile materials in the future.

Raman spectroscopy for characterization of Mxene

Raman spectroscopy is a powerful technique used to characterize the structural and chemical properties of materials, including Mxenes. Mxenes, a class of two-dimensional transition metal carbides, nitrides, and carbonitrides, have gained significant attention in the scientific community due to their unique properties and potential applications in various fields. In this article, we will explore how Raman spectroscopy can be utilized to study and analyze Mxene materials.

Raman spectroscopy is a non-destructive analytical technique that provides information about the vibrational modes of a material. When a material is irradiated with monochromatic light, some of the incident photons are scattered at different energies due to interactions with the material’s molecular vibrations. These energy shifts, known as Raman shifts, provide valuable insights into the material’s chemical composition, crystal structure, and bonding characteristics.

For Mxenes, Raman spectroscopy offers several advantages in characterizing their properties. One key advantage is the ability to identify the presence of different functional groups and chemical bonds within the Mxene structure. The Raman spectrum of Mxenes typically exhibits characteristic peaks corresponding to the stretching and bending vibrations of metal-carbon or metal-nitrogen bonds, as well as other functional groups present in the material.

Additionally, Raman spectroscopy can be used to determine the crystallinity and layer thickness of Mxene samples. The intensity and position of Raman peaks can provide information about the stacking order and interlayer interactions within the Mxene structure. By analyzing the Raman spectra of Mxenes obtained from different synthesis methods or processing conditions, researchers can gain valuable insights into the structural properties of these materials.

Furthermore, Raman spectroscopy can be employed to study the electronic properties of Mxenes. By analyzing the Raman spectra at different excitation wavelengths or under different environmental conditions, researchers can probe the charge carrier dynamics, doping effects, and electronic band structure of Mxene materials. This information is crucial for understanding the electrical conductivity and optoelectronic properties of Mxenes, which are important for their applications in energy storage and electronic devices.

In conclusion, Raman spectroscopy is a versatile tool for characterizing Mxene materials and gaining insights into their structural, chemical, and electronic properties. By utilizing Raman spectroscopy in conjunction with other analytical techniques, researchers can further elucidate the fundamental properties of Mxenes and optimize their performance for various applications. Continued research in this area will undoubtedly contribute to unlocking the full potential of Mxene materials in the field of materials science and beyond.

XRD technique for characterization of Mxene

X-ray diffraction (XRD) is a powerful analytical technique widely used for the characterization of materials, including Mxenes. Mxenes, a class of two-dimensional transition metal carbides, nitrides, and carbonitrides, have garnered significant interest in the scientific community due to their unique properties and potential applications in various fields. In this article, we will explore how XRD can be utilized to study and analyze the structural properties of Mxene materials.

X-ray diffraction is based on the principle of Bragg’s law, which states that when X-rays are incident on a crystalline material, they will be diffracted at specific angles depending on the crystal structure and interatomic spacing of the material. By measuring the intensity and angle of the diffracted X-rays, researchers can obtain valuable information about the crystal structure, phase composition, crystallite size, and lattice parameters of a material.

For Mxenes, X-ray diffraction is a valuable tool for determining their crystal structure and phase composition. The XRD pattern of Mxene materials typically exhibits sharp diffraction peaks corresponding to the ordered atomic arrangement within the crystal lattice. By analyzing the positions and intensities of these peaks, researchers can identify the crystallographic phases present in the Mxene sample and determine the crystal symmetry and unit cell parameters.

Moreover, XRD can be used to study the layer stacking and interlayer spacing of Mxene materials. The interlayer distance between adjacent Mxene layers can be calculated from the position of the diffraction peaks in the XRD pattern. By analyzing the changes in interlayer spacing under different synthesis conditions or processing methods, researchers can gain insights into the structural properties and stability of Mxenes.

Additionally, X-ray diffraction can provide information about the crystallite size and degree of crystallinity of Mxene samples. The broadening of XRD peaks is often used to estimate the average crystallite size of the material, with smaller peak widths indicating smaller crystallite sizes. By quantifying the crystallite size distribution in Mxene samples, researchers can assess the degree of structural ordering and defects present in the material.

Furthermore, X-ray diffraction can be employed to investigate the thermal stability and phase transformations of Mxene materials. By performing in situ XRD measurements at different temperatures or under controlled atmospheres, researchers can monitor changes in the crystal structure and phase composition of Mxenes as a function of temperature or environmental conditions. This information is crucial for understanding the thermal behavior and performance of Mxene materials in high-temperature applications.

In conclusion, X-ray diffraction is a versatile technique for characterizing the structural properties of Mxene materials and gaining insights into their crystallographic features, interlayer spacing, crystallite size, and phase composition. By combining XRD with other analytical techniques, researchers can further elucidate the fundamental properties of Mxenes and optimize their performance for various applications. Continued research in this area will undoubtedly contribute to advancing our understanding of Mxene materials and harnessing their full potential in materials science and technology.

FT-IR spectroscopy for characterization of Mxene

Fourier-transform infrared spectroscopy (FT-IR) is a powerful analytical technique that is widely used for the characterization of materials, including Mxenes. Mxenes, a class of two-dimensional transition metal carbides, nitrides, and carbonitrides, have garnered significant interest in the scientific community due to their unique properties and potential applications in various fields. In this article, we will explore how FT-IR can be utilized to study and analyze the structural and chemical properties of Mxene materials.

FT-IR spectroscopy is based on the principle that molecules absorb infrared radiation at specific frequencies that are characteristic of their chemical bonds and functional groups. When infrared light is passed through a sample, certain wavelengths are absorbed by the sample, resulting in the excitation of molecular vibrations. By measuring the intensity of the absorbed infrared radiation as a function of wavelength, researchers can obtain valuable information about the chemical composition, bonding environment, and structural properties of a material.

For Mxenes, FT-IR spectroscopy is a valuable tool for identifying the functional groups present in the material and probing the bonding interactions between the transition metal atoms, carbon or nitrogen atoms, and other constituents. The FT-IR spectrum of Mxene materials typically exhibits characteristic absorption bands corresponding to the vibrational modes of different chemical groups, such as C-C, C-H, C=O, and M-X bonds (where M represents the transition metal and X represents carbon or nitrogen).

By analyzing the positions and intensities of these absorption bands in the FT-IR spectrum, researchers can identify the functional groups present in the Mxene sample and gain insights into the chemical structure and composition of the material. For example, the presence of specific absorption bands can indicate the presence of carbide or nitride groups in the Mxene structure, while shifts in peak positions can provide information about the coordination environment of the transition metal atoms.

Moreover, FT-IR spectroscopy can be used to study the surface chemistry and functionalization of Mxene materials. By analyzing changes in the FT-IR spectrum before and after surface modification or functionalization reactions, researchers can monitor the introduction of new chemical groups or functional moieties onto the Mxene surface. This information is crucial for tailoring the surface properties and reactivity of Mxenes for specific applications, such as catalysis, sensing, or energy storage.

Additionally, FT-IR spectroscopy can provide insights into the thermal stability and decomposition behavior of Mxene materials. By performing in situ FT-IR measurements at different temperatures or under controlled atmospheres, researchers can monitor changes in the infrared absorption bands associated with thermal degradation processes. This information is essential for understanding the thermal behavior and stability of Mxene materials under different environmental conditions.

In conclusion, Fourier-transform infrared spectroscopy is a versatile technique for characterizing the structural and chemical properties of Mxene materials and gaining insights into their functional groups, bonding interactions, surface chemistry, and thermal behavior. By combining FT-IR with other analytical techniques, researchers can further elucidate the fundamental properties of Mxenes and optimize their performance for various applications. Continued research in this area will undoubtedly contribute to advancing our understanding of Mxene materials and unlocking their full potential in materials science and technology.

XPS for characterization of Mxene

X-ray photoelectron spectroscopy (XPS) is a powerful analytical technique that is widely used for the characterization of materials, including Mxenes. Mxenes, a class of two-dimensional transition metal carbides, nitrides, and carbonitrides, have garnered significant interest in the scientific community due to their unique properties and potential applications in various fields. In this article, we will explore how XPS can be utilized to study and analyze the surface chemistry, elemental composition, and electronic structure of Mxene materials.

X-ray photoelectron spectroscopy is based on the principle that when a material is irradiated with X-rays, electrons from the inner shells of atoms are ejected, resulting in the emission of photoelectrons. By measuring the kinetic energy and intensity of these emitted electrons, researchers can obtain valuable information about the elemental composition, chemical bonding, oxidation states, and surface properties of a material.

For Mxenes, XPS spectroscopy is a valuable tool for probing the surface chemistry and elemental composition of the material. The XPS spectrum of Mxene materials typically exhibits characteristic peaks corresponding to the core levels of different elements present in the sample, such as transition metals (M), carbon (C), nitrogen (N), and oxygen (O). By analyzing the positions and intensities of these peaks, researchers can identify the elemental composition of the Mxene sample and gain insights into the bonding environment and oxidation states of the constituent elements.

Moreover, XPS can provide information about the electronic structure and valence band properties of Mxene materials. By analyzing the valence band spectrum obtained from XPS measurements, researchers can study the energy distribution of valence electrons in the material and investigate the electronic interactions between different atomic species. This information is crucial for understanding the electronic properties and charge transfer mechanisms in Mxene materials, which are important for their performance in various applications, such as energy storage, catalysis, and sensing.

Additionally, XPS spectroscopy can be used to study the surface functionalization and chemical modifications of Mxene materials. By performing XPS measurements before and after surface treatments or functionalization reactions, researchers can monitor changes in the elemental composition, chemical states, and surface functionalities of the Mxene sample. This information is essential for tailoring the surface properties and reactivity of Mxenes for specific applications and optimizing their performance in various technological applications.

Furthermore, XPS can provide insights into the stability and degradation behavior of Mxene materials under different environmental conditions. By performing in situ XPS measurements at elevated temperatures or under controlled atmospheres, researchers can monitor changes in the chemical states and oxidation states of the Mxene sample during thermal treatments or exposure to reactive gases. This information is crucial for understanding the thermal stability and reactivity of Mxene materials and optimizing their performance for high-temperature applications.

In conclusion, X-ray photoelectron spectroscopy is a versatile technique for characterizing the surface chemistry, elemental composition, electronic structure, and stability of Mxene materials. By combining XPS with other analytical techniques, researchers can gain comprehensive insights into the fundamental properties of Mxenes and tailor their surface properties for specific applications. Continued research in this area will undoubtedly contribute to advancing our understanding of Mxene materials and unlocking their full potential in materials science and technology.

UV-Vis spectroscopy for characterization of Mxene

Ultraviolet-visible (UV-Vis) spectroscopy is a powerful analytical technique that is commonly used for the characterization of materials, including Mxenes. Mxenes, a class of two-dimensional transition metal carbides, nitrides, and carbonitrides, have garnered significant interest in the scientific community due to their unique properties and potential applications in various fields. In this article, we will explore how UV-Vis spectroscopy can be utilized to study and analyze the optical properties, electronic transitions, and bandgap of Mxene materials.

UV-Vis spectroscopy is based on the principle that when a material is irradiated with ultraviolet or visible light, electrons in the material can be excited from the ground state to higher energy states. By measuring the absorption or transmission of light at different wavelengths, researchers can obtain valuable information about the electronic transitions, band structure, and optical properties of the material.

For Mxenes, UV-Vis spectroscopy is a valuable tool for probing the electronic structure and optical properties of the material. The UV-Vis spectrum of Mxene materials typically exhibits characteristic absorption peaks corresponding to electronic transitions between different energy levels in the material. These absorption peaks can provide insights into the bandgap energy, electronic band structure, and optical transitions in Mxene materials.

The bandgap energy of a material is a critical parameter that determines its electronic and optical properties. By analyzing the absorption spectrum obtained from UV-Vis measurements, researchers can estimate the bandgap energy of Mxene materials and gain insights into their electronic band structure. The bandgap energy of Mxenes can be influenced by various factors, such as the composition, structure, and surface functionalization of the material, making UV-Vis spectroscopy an essential tool for studying and optimizing the optical properties of Mxenes for specific applications.

Moreover, UV-Vis spectroscopy can provide information about the electronic transitions and excitonic effects in Mxene materials. Excitonic effects arise from the interaction between photo-excited electrons and holes in a material, leading to the formation of excitons with distinct optical properties. By analyzing the absorption spectrum and peak shapes in UV-Vis measurements, researchers can study the excitonic effects in Mxene materials and investigate their impact on the optical properties and charge carrier dynamics of the material.

Additionally, UV-Vis spectroscopy can be used to study the surface plasmon resonance (SPR) properties of Mxene materials. SPR is a phenomenon that occurs when free electrons in a material collectively oscillate in response to incident light, leading to enhanced light absorption and scattering at specific wavelengths. By performing UV-Vis measurements at different angles or polarizations, researchers can investigate the SPR properties of Mxene materials and tailor their optical properties for applications such as sensors, photodetectors, and plasmonic devices.

Furthermore, UV-Vis spectroscopy can be employed to study the stability and degradation behaviour of Mxene materials under different environmental conditions. By performing in situ UV-Vis measurements under controlled temperatures or atmospheres, researchers can monitor changes in the optical properties and electronic transitions of the material during thermal treatments or exposure to reactive gases. This information is crucial for understanding the stability and reactivity of Mxene materials and optimizing their performance for applications requiring high temperatures or harsh environments.

In conclusion, UV-Vis spectroscopy is a versatile technique for characterizing the optical properties, electronic transitions, and bandgap of Mxene materials. By combining UV-Vis spectroscopy with other analytical techniques, researchers can gain comprehensive insights into the fundamental properties of Mxenes and tailor their optical properties for specific applications. Continued research in this area will undoubtedly contribute to advancing our understanding of Mxene materials and unlocking their full potential in materials science and technology.

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

Carbon Allotropes: A Kaleidoscope of Structures and Properties

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

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

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

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

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

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

Mechanical and Chemical Properties of Carbon Allotropes

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

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

Characterization Methods for Carbon Allotropes

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

1. Fourier Transform Infrared Spectroscopy (FTIR)

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

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

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

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

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

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

2. Raman Spectroscopy

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

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

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

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

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

3. X-ray Photoelectron Spectroscopy (XPS)

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

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

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

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

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

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

4. Ultraviolet-Visible Spectroscopy (UV-Vis)

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

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

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

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

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

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

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

5. X-ray Diffraction (XRD)

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

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

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

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

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

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

Conclusion

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

Falsification of data in a paper refers to the deliberate manipulation or fabrication of research data to support a particular hypothesis or conclusion. This can include altering or omitting data, selectively reporting results, or creating false data altogether.

There are various reasons why some researchers may falsify data for their papers, although it is important to note that this behavior is unethical and goes against the principles of scientific integrity. Some of the reasons may include:

1. Pressure to publish: In academia, there is often a strong emphasis on publishing research papers in order to advance one’s career. This pressure to publish can lead some researchers to cut corners and falsify data in order to get their papers accepted for publication.

2. Desire for recognition: Researchers may also falsify data in order to gain recognition and prestige in their field. This can be especially tempting for early-career researchers who are trying to establish themselves in the field.

3. Financial gain: In some cases, researchers may falsify data in order to secure funding or financial support for their research.

4. Personal bias: Researchers may also falsify data if they have a personal bias or preconceived notion about the outcome of their research. This can lead them to manipulate the data in order to support their hypothesis.

We cannot promote or encourage any unethical practices. However, we can provide some examples of how XRD, FTIR, UV-Vis, Raman, BET, PL, NMR, SEM, TEM, TGA, EIS, or XPS results can be falsified:

1. Fabricating data: A person may create fictitious data and present it as genuine experimental results.

2. Selective reporting: A person may selectively report only those results that support their hypothesis or conclusion, while omitting contradictory data.

3. Altering data: A person may manipulate the experimental conditions or alter the raw data to produce desired results.

4. Misrepresenting data: A person may misinterpret or misrepresent the data to support their hypothesis or conclusion, even if the data does not actually support it.

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1. Introduction

Today several instruments for fast spectra recording are available. In most cases the difficulty
is to process and analyze the data quickly in a reliable way. The Maud program, in one of its
many undocumented features, can be used to process a list of analyses in batch mode from the
console without requiring the interface. This is useful to process quickly similar spectra or launch
a slow/time consuming refinement in a remote computer without recurring to the interface that
would need to open a session involving the remote display setting.

The overall procedure is to prepare the analysis locally using the interface or to prepare a starting point for a series of spectra
(one common starting point) also using the interface, then to prepare an instruction file in CIF like
format to specify the analyses, the spectra and the kind of refinement to conduct and finally to run
Maud in batch mode providing the instruction file previously prepared. The program will run and
process one analysis at time and prepare an output file extracting some key information (either the
default or some to be specified) in a format suitable to be imported in spreadsheet or graphical
programs to analyze the results.
As an example we will show the procedure to analyze a series of ball milled Cu-Fe mixed powders
in which two different phases may form with a different composition. By an automatic Rietveld
analysis performed in batch mode we will extract information about phase content [2, 1], crystallite
and microstrain for each sample/spectrum. The analysis is further complicated from the fact that
the powders milled at higher energy show the presence of planar defects [5] and texture arising
from sample preparation and the platelet like shape of the grains [3].

2 Analysis and procedure
In this section we will present the procedure to analyze 25 spectra of Cu-Fe different samples. The
spectra has been collected by a Philips X-pert system in Le Mans at the LPEC laboratory of the
1
University du Maine, thanks to A. Gibaud.
2.1 Analysis preparation through the interface
We start the Maud program and load all the datafiles together to check their integrity and to prepare
a common starting analysis file. A plot of all spectra and their differences is available in Figure 1.

We load the two possible phases, bcc iron and fcc copper, from the Maud database. By computing
the spectra once and comparing them visually with the experimental spectra we may notice that
for some samples, milled at longer time, an alloyed fcc phase form (out of equilibrium) and the
bcc iron disappears. Unluckily we could not use the copper rich phase cell parameter to monitor
the Fe content in it as the cell parameter tends to growth as a result probably of oxygen entrapping.
In a first attempt we discovered the spectra were affected by texture, anisotropic crystallite sizes
and microstrain as well as planar defects (especially on the Cu like phase). So we decide here
to include also texture and anisotropic/planar defects effects in the analysis. For both the bcc
and fcc phases we select in the proper panel the Popa model for anisotropic broadening [4], the
Warren model for planar defects and the harmonic model for texture (specifying cylindrical sample
symmetry and Lmax = 6 in the options; it is required by the experiment geometry).
Next step was to adjust the cell parameters for both bcc and fcc phases in order to get a mean
starting value good for all spectra (especially for the fcc); and to adjust the crystallite value to a
good starting point (around 200 angstrom) obtaining peak shapes a little sharper than in the less
broadened spectrum. The background constant parameter was also adjusted to the value of the
spectrum with the lower background. Actually only the cell parameter adjustment is critical, the
background one is even not necessary.
Finally we remove all the spectra (we will specify which datafile to use for each analysis later in an
instruction file) and save the analysis containing everything except the spectrum/a. For the purpose
of this article we save the analysis with the name: FeCustart.par.
2.2 Preparation of the instruction file and batch processing
To run Maud in batch we need to write an instruction file containing the list of analyses to execute
one at time. The file is in CIF format but containing some terms not available in the official CIF
dictionary, but that Maud recognize. All the analyses to be performed are specified through the
loop CIF instruction. The first term of the loop must be the one specifying the starting analysis
file to be loaded (full path in unix convention) and then the others to instruct Maud for the kind
of analysis to perform, iterations and eventually datafile to load and name of the file were to save
the analysis. Additional keywords can be used to append specific results to a file for spreadsheet
analysis. The simplest instruction file is something containing the following:
First example (paths for windows):
loop
riet analysis file
riet analysis iteration number
2
´//C:/mypathfortheanalysis/analysis1.par´ 5
´//C:/mypathfortheanalysis/analysis2.par´ 3
´//C:/mypathfortheanalysis/analysis3.par´ 7
The analysis1.par (or 2 or 3) are some analyses files prepared with Maud, containing also
the datafile/spectrum, already set for the parameters to be refined and saved just ready for the refinement step. Maud will load each analysis, starts the refinement with the number of iterations
specified and save the analysis with the refined parameters under the same name. The analyses can
be loaded at end in Maud (with the interface) to see the result of the refinement.
In the case of the Cu-Fe we need to perform some more steps: first we start from one common analysis point (the FeCustart.par analysis file) but we want to specify different datafiles; second
we want to perform a full automatic analysis in which Maud performs different cycles deciding
which parameters to refine at each step and third we will specify the name of each analysis for the
saving process and a file name were to append some selected results in a tab/column format for
subsequent easy loading in a spreadsheet program.
Cu-Fe example:
loop
riet analysis file
riet analysis iteration number
riet analysis wizard index
riet analysis fileToSave
riet meas datafile name
riet append simple result to
´//mypath/FeCustart.par´ 7 13 ´//mypath/FECU1010.par´ ´//mypath/FECU1010.UDF´
´//mypath/FECUresults.txt´
´//mypath/FeCustart.par´ 7 13 ´//mypath/FECU1011.par´ ´//mypath/FECU1011.UDF´
´//mypath/FECUresults.txt´
…………(lines with all the other 23 datafiles omitted for brevity)
´//mypath/FeCustart.par´ 7 13 ´//mypath/FECU1038.par´ ´//mypath/FECU1038.UDF´
´//mypath/FECUresults.txt´
With this instruction file (that we save under the name: fecu.ins) we specify for example that
as a first analysis, Maud has to load the FeCustart.par file, then to load in the analysis the
FECU1010.UDF datafile, to perform the automatic analysis number 13 (in the wizard panel of
Maud the automatic analysis number 13 is the texture analysis; we need to refine also the texture
parameters along with phase analysis and microstructure) and to use 7 iterations for each cycle (the
texture automatic analysis is composed by 4 cycles) to ensure sufficient convergence. At the end
the analysis is saved with the name FECU1010.par and simple selected results will be appended
in the file FECUresults.txt. The simple results saved in the spreadsheet like file are some of
the most used parameters and results. It is possible to specify the parameters we want in output
using the CIF word riet append result to (in addition or as an alternative), but in the
preparation of the starting analysis file in the Maud interface, the parameters to be added to the
results must be specified by turning to true the switch in the output column of the parameter list
window or panel.
Now to run Maud in batch in the console (
where the Maud.jar is located the following:
DOS (everything in the same line): java -mx512M -cp
“Maud.jar;lib\miscLib.jar;lib\JSgInfo.jar;lib\jgaec.jar;lib\ij.jar”
it.unitn.ing.rista.MaudText -f fecu.ins
Unix (everything in the same line): java -mx512M -cp
Maud.jar:lib/miscLib.jar:lib/JSgInfo.jar:lib/jgaec.jar:lib/ij.jar
it.unitn.ing.rista.MaudText -f fecu.ins
For Mac OS X, it is advised to use the generic Unix Maud installation (or to change the path to
the jar files). Before to run Maud in batch mode it is important to run Maud interactive (with the
interface) at least once to create and extract the databases, examples and preferences folder.

2.3 Analysis of results
After running Maud in batch mode, we can check quickly the results by loading the results file
FECUresults.txt in a spreadsheet program. The results are arranged in rows and separated
by tabs. The first row contains the column titles, each subsequent row a different analysis. The
Rwp value for each analysis is reported in the second column and the biggest value found was
5.6% as an indication of the success of the analysis. As an example we report in Figure 2 the
graphical correlation of the copper-rich phase percentage and its mean crystallite value as found
in the analysis versus the sample number. The files and examples used in this articles will be
uploaded in a tutorial in the Maud web page along with some additional files with the batch mode
commands for an easier use.

3 How to get Maud 2.0 and further informations
For this analysis we need Maud version 2.037 or later and it can be freely downloaded from the
Maud web page at http://www.ing.unitn.it/ maud for the preferred platform. There are two archives
for Windows and Mac OS X plus a generic unix version that can be used for Linux, Solaris or
every unix based system with a Java 2 virtual machine installed. The new version 2.0 has a new
interface focused on reducing the effort of a new user and simplifying the most common tasks.
Some particularity of the new version respect to the previous one are (most of them to provide
some useful routines for ab-initio structure solution):
• Different minimization/search algorithms selectable: Marquardt least squares, Evolutionary
algorithm, Simulated annealing, Metadynamic search algorithm. As an example the evolutionary algorithm can be used in the early steps of the refinement to select the proper starting
solution and the Marquardt to drive it to convergence.
4
• Possibility to use crystallites and microstrain distributions for peak shape description instead
of analytical fixed shape functions.
• Maximum Entropy Electron Map full pattern fitting. An electron map can be used for fitting
instead of atoms.
• Full pattern fitting by a list of peaks. Either an arbitrary list of peaks (each one with its own
position, intensity and shape), or simply a list of structure factors to be imported, instead of
a list of atoms.
• Indexing directly on the pattern, selecting the Le Bail fit and the evolutionary algorithm for
the cell search. This may be used to improve a difficult indexing or a partly done one.
• Introduction of fragments. So fragment search can be done directly on the pattern or on a
list of extracted structure factors.
• Energy minimization. At the moment only the simple repulsion energy is completed. Other
energy principles are under completition.
• Spectra integration from image plate or CCD transmission/reflection 2D images. Center,
tilting errors and distance from sample can be refined in the spectra fitting.
Bugs and errors should be reported to the author through the bug reporter web page; questions in
the Maud forum accessible from the Maud web page.
In a future article we will report the instructions on how to modify/extend the program by little Java programming or provide a new alternative model/plugin for the instrument or the structure/microstructure or datafiles importing.
References
[1] D. L. Bish and S. A. Howard. J. Appl. Cryst., 21, 86–91, 1988.
[2] R. J. Hill and C. J. Howard. J. Appl. Cryst., 20, 467–474, 1987.
[3] L. Lutterotti and S. Gialanella. Acta Mater., 46(1), 101–110, 1998.
[4] N. C. Popa. J. Appl. Cryst., 31, 176–180, 1998.
[5] B. E. Warren. X-ray Diffraction. Addison-Wesley, Reading, MA, 1969

Author: Luca Lutterotti
Dipartimento di Ingegneria dei Materiali e delle Tecnologie Industriali
Universita di Trento, 38050 Trento, Italy `
E-mail: Luca.Lutterotti@ing.unitn.it
WWW: http://www.ing.unitn.it/ maud

In OriginPro, the Peak Analyzer is capable of creating and subtracting baseline. There are various ways for baseline creation. You can generate baseline anchor points automatically or manually and then connect them with interpolation or fit them with a function.

Steps

Create baseline with 2nd Derivative method

1. Start a New Workbook and import the <Origin EXE Folder>\Samples\Spectroscopy\Baseline.dat. Highlight the second column. In the main menu, select Analysis: Peaks and Baseline: Peak Analyzerto open the dialog of the Peak Analyzer.
2. In the first page (the Goal page) of the Peak Analyzer, select Create Baseline as Goal. Click Next to go to the Baseline Mode page.
3. Next we are going to create an user defined baseline for this spectrum by defining anchor points. On the Baseline Mode page, select User defined from the Baseline Mode dropdown list. Check on Snap to Spectrum to make sure when you add or move a baseline anchor point, it will be pulled back onto the spectrum.
4. The first two methods in Anchor Points Finding are the most commonly used, and they can find anchor points automatically based on the derivative of the spectrum. If the baseline is approximately constant, 1st Derivative and 2nd Derivative is more powerful, otherwise, we should use 2nd Derivative. In this example, the baseline is more curly, so we use 2nd Derivative(zeros) method for locating anchor points.For other methods in Anchor Points Finding and related smoothing parameters, you can refer to the link: Baseline Mode Page.
5. Now click the Find button in the Baseline Anchor Points group. You can preview the anchor points in preview window.
6. Click Next to go to the Create Baseline page, select Interpolation in the Connect by drop-down list. In Interpolation method group, select Spline from the drop-down list. You can preview the spectrum in preview window, and then click Finish to get the baseline data.

Create baseline with ALS Method (Pro)

1. Start from the Baseline Mode page, and select the Asymmetric Least Squares Smoothing Baseline (ALS) as the baseline method. The ALS baseline can be tuned easily with a few parameters without pre-selecting any anchor points.
2. Click Next go to Asymmetric Least Squares Smoothing Baseline page, adjust the parameters to make the baseline optimal, then click Finish button to output the results table and graph.

Subtract Baseline from a Spectrum

1. If you want to subtract baseline, select Subtract Baseline as the Goal at start page.
2. After you created a baseline, click Next button to go to Subtract Baseline page.
3. Click Subtract button for previewing the subtracted data. The baseline data and subtracted spectrum will be outputted after clicking Finish button. The figures below displayed the Subtract Baselinepage and the preview of the subtracted spectrum.

Our XRD interpretation includes: 
1. Phase determination 
2. Determination of diffracted planes
 3- Calculation of crystalline size and microstrain
 4- Whatever your request
 Its cost is only 12$ 
Payment Upon Completion
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XRD is a non-destructive test method used to analyze the structure of crystalline materials. XRD analysis, by way of the study of the crystal structure, is used to identify the crystalline phases present in a material and thereby reveal The chemical composition information. JCPDS does not exist now. It has not existed since 1978. It is now known as ICDD. These particular files have never been, are not, and never will be free; it a commercial only database. There are other free databases, however. Vikas has given you a starting point.

Powder Diffraction File is a trademark of the “JCPDS (Joint Committee on Powder Diffraction Standards)-International Centre for Diffraction Data”.In 1978, the name of the organization was changed to the “International Centre for Diffraction Data” in order to highlight the global commitment of this scientific endeavor. Here, you need to purchase the database.

Some free databases are collected:

1. COD (Crystallography Open Database):

COD is an open-access database, and you can freely obtain all data contained in it. You can download cif files and then you can use mercury to plot structure models and save reflection list and xrd calculated pattern.http://www.crystallography.net/cod/search.html

2. The American Mineralogist Crystal Structure Database:

This site is an interface to a crystal structure database that includes every structure published in the American Mineralogist, The Canadian Mineralogist, European Journal of Mineralogy and Physics and Chemistry of Minerals, as well as selected datasets from other journals. The database is maintained under the care of the Mineralogical Society of America and the Mineralogical Association of Canada, and financed by the National Science Foundation.http://rruff.geo.arizona.edu/AMS/amcsd.php

3. DASH: (Cambridge Structural Database System (CSDS)):DASH is a versatile and interactive package for solving crystal structures from powder diffraction data. DASH solves structures by simulated annealing of structural models to indexed diffraction data and features a helpful wizard to guide you through the entire structure solution process.https://www.ccdc.cam.ac.uk/solutions/csd-materials/components/dash/

Some of Paid databases:

1. The International Centre for Diffraction Data® (ICDD®):

ICDD (JCPDS is now called ICDD) is a non-profit scientific organization dedicated to collecting, editing, publishing, and distributing powder diffraction data for the identification of materials. The membership of the ICDD consists of worldwide representation from academe, government, and industry. The Powder Diffraction File™ (PDF®) is the only crystallographic database that is specifically designed for material identification and characterization. It is an analysis system that is comprised of crystallographic and diffraction data. The only crystallographic database organization in the world with its Quality Management System ISO 9001:2015 certified by DEKRA.http://www.icdd.com/

2. HighScore Plus:The ideal tool for crystallographic analysis and more. Whether you are interested in improved process control, or doing research and development, understanding your materials starts very often with understanding the powder diffraction pattern. After identification of all phases present in your sample with Malvern Panalytical’s HighScore, this all-in-one software suite with the Plus option continues to support you with your analysis. Whether your focus is on quantification with or without the Rietveld method, profile fitting, or pattern treatment; HighScore Plus is the solution and helps you performing your daily analyses.https://www.malvernpanalytical.com/en/products/category/software/x-ray-diffraction-software/highscore-with-plus-option

Our XRD interpretation includes:
1. Phase determination
2. Determination of diffracted planes
3- Calculation of crystalline size and microstrain
4- Whatever your request
Its cost is only 12$
Payment Upon Completion

Send your patterns...

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

Our XRD interpretation includes:
1. Phase determination
2. Determination of diffracted planes
3- Calculation of crystalline size and microstrain
4- Whatever your request
Its cost is only 12$
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X-ray diffraction (XRD) is a technique used in materials science for determining the atomic and molecular structure of a material. This is done by irradiating a sample of the material with incident X-rays and then measuring the intensities and scattering angles of the X-rays that are scattered by the material. The intensity of the scattered X-rays are plotted as a function of the scattering angle, and the structure of the material is determined from the analysis of the location, in angle, and the intensities of scattered intensity peaks. Beyond being able to measure the average positions of the atoms in the crystal, information on how the actual structure deviates from the ideal one, resulting for example from internal stress or from defects, can be determined.

The diffraction of the X-rays, that is central to the XRD method, is a subset of the general X-ray scattering phenomena. XRD, which is generally used to mean can wide-angle X-ray diffraction (WAXD), falls under several methods that use the elastically scattered X-ray waves. Other elastic scattering based X-ray techniques include small angle X-ray scattering (SAXS), where the X-rays are incident on the sample over the small angular range of 0.1-10⁰typically). SAXS measures structural correlations of the scale of several nanometers or larger (such as crystal superstructures), and X-ray reflectivity that measures the thickness, roughness, and density of thin films. WAXD covers an angular range beyond 10⁰.

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JoVE Science Education Database. Materials Engineering. X-ray Diffraction. JoVE, Cambridge, MA, (2020).

PRINCIPLES

Relationship between diffracted peak positions and crystal structure:

When light waves of sufficiently small wavelength are incident upon a crystal lattice, they diffract from the lattice points. At certain angles of incidence, the diffracted parallel waves constructively interfere and create detectable peaks in intensity. W.H. Bragg identified the relationship illustrated in Figure 1 and derived a corresponding equation:

nλ = 2d_hkl sin θ [1]

Here λ is the wavelength of the X-rays used, d_hkl is the spacing between a particular set of planes with (hkl) Miller indices*, and θ is the angle of incidence at which a diffraction peak is measured. Finally, n is an integer that represents the ‘harmonic order’ of the diffraction. At n=1, for example, we have the first harmonic, meaning that the path of X-rays diffracted through the crystal (equivalent to 2d_hkl sin ) is exactly 1λ, while at n=2, the diffracted path is 2λ. We can typically assume n=1, and, in general, n=1 for θ < sin^-1(2λ/d_h’k’l’), where h’k’l’ are the Miller indices of the planes that show the first peak (at the lowest 2θ value) in a diffraction experiment. Miller indices are a set of three integers that constitute a notation system for identifying directions and planes within crystals. For directions, the [h k l]Miller indices represent the normalized difference in the respective x, y and z coordinates (in a Cartesian coordinate system) of two points along the direction. For planes, the Miller indices (h k l) of a plane are simply the h k l values of the direction perpendicular to the plane.

In a typical XRD experiment in reflection mode, the X-ray source is fixed in position and the sample is rotated with respect to the X-ray beam over θ. A detector picks up the diffracted beam and has to keep up with the sample rotation by rotating at twice the rate (i.e. for a given sample angle of θ, the detector angle is 2θ). The geometry of the experiment is schematically shown in Figure 1.

Figure 1: Illustration of Bragg’s Law.

When a peak in intensity is observed, equation 1 is necessarily satisfied. Consequently, we can calculate d-spacings based on the angles at which these peaks are observed. By calculating the d-spacings of multiple peaks, the crystal class and the crystal structure parameters material sample can be identified using a database such as the Hanawalt Search Manual or database libraries available with the XRD software being used.

We will be assuming that the sample being investigated is not a single crystal. If the sample were a single crystal with a particular (h*k*l*) plane parallel to the sample surface, it would need to be rotated until the Bragg condition for the (h*k*l*) is satisfied in order to see a peak in diffracted intensity (for n=1) with potentially higher harmonic (h*k*l*)peaks (e.g. for n=2) also detectable at higher angles. At all other angles there would be no peaks in a single crystal sample. Instead, let’s assume that the sample is either polycrystalline or that it is a powder, with a statistically significant number of crystalline grains or powder particles illuminated by the incident X-ray beam. Under this assumption, the sample consists of randomly oriented grains, with a similar statistical probability for all possible lattice planes to diffract.

The relationships between the d_hkl and the unit cell parameters are shown below in Equations 2-7 for the 7 crystal classes, cubic, tetragonal, hexagonal, rhombohedral, orthorhombic, monoclinic and triclinic. The unit cell parameters consist of lengths of(a,b,c) and the angles between (α, β, γ) the edges of the unit cells for the 7 crystal classes (Figure 1x shows the example of one of the crystal classes: the tetragonal structure where a=b≠c, and α=β=γ=90⁰). Using multiple diffracted peak positions (i.e. several distinct d_hkl values), the values of the unit cell parameters can be solved uniquely.

Figure 2: The tetragonal structure as one of the seven crystal classes.

Cubic (a = b = c; α = β = γ = 90⁰):

  [2]

Tetragonal (a = b ≠ c; α = β = γ = 90⁰):

  [3]

Hexagonal (a = b ≠ c; α = β = 90⁰; γ = 120⁰):

  [4]

Orthorhombic (a ≠ b ≠ c; α = β = γ = 90⁰):

  [5]

Rhombohedral (a = b ≠ c; α = β = γ = 90⁰):

  [6]

Monoclinic (a ≠ b ≠ c; α = γ = 90⁰≠ β):

  [7]

Triclinic (a ≠ b ≠ c; α ≠ β ≠ γ ≠ 90⁰): 

        [8] 

Relationship between diffracted peak intensities and crystal structure:

Next we examine the factors that contribute to the intensity in an XRD pattern. The factors can be broken down as 1) the contribution to scattering that results directly from the unique structural aspects of the material (the specific types and locations of scattering atoms in the structure) and 2) those that are not specific to the material. In the former, there are two factors: the ‘absorption factor’ and the ‘structure factor’. The absorption factor primarily depends on the ability of the material to absorb X-rays on their way in and out. This factor does not have a θ dependence as long as the samples are not thin (the sample should be > 3 times thicker than the attenuation length of the X-rays). In other words, the contribution by the absorption factor to the intensity of different peaks is constant. The ‘structure factor’ directly affects the intensity of specific peaks as a direct result of the structure. The remaining factors, the ‘multiplicity’, which accounts for all the planes that belong to the same family because they are symmetrically related, and the ‘Lorentz-Polarization’ factor, which comes from the geometry of the XRD experiment, also affect the relative intensity of the peaks but they are not specific to a material and can easily be accounted for with analytical expressions (i.e. XRD analysis software can remove them with analytical functions).

Figure 3: Three diffraction ray paths, of which rays 11′ and 22′ satisfy the Bragg condition, while ray 33′ results from scattering by an atom (red circle) at an arbitrary position.

As the only factor that carries the unique structural contribution of a material to the relative intensities of XRD peaks, the structure factor is very important and requires a closer look. In Figure 2, let us assume that the 1^st order Bragg diffraction condition (remember, that this corresponds to n=1) is satisfied between ray_11′ and ray_22′ which are scattered on two atomic planes in the h00 direction (using the Miller indices notation described earlier) separated by a distance d. Under this condition, the difference in path length between ray_11′ and ray_22′ is δ_{(22′-11′)} = SA + AR = λ. The phase shift between the diffracted rays 1 and 2 is, therefore, Φ_22′-11′ = (δ_{(22′-11′)}/λ) 2π = 2π (assuming a cubic symmetry and, therefore, d = a/h in the h00 direction].

With a few steps in analytical geometry, it can be shown that the phase shift, Φ_{(33′-11′)}, with ray 3 diffracted by an arbitrary plane of atoms that are spaced an arbitrary distance x, is given by: Φ_{(33′-11′)} = 2πhu, where u=x/a (a is the unit cell parameter in the (h00) direction.) Taking the two other orthogonal directions, (0k0) and (00l), and v=y/a and w=z/a as fractional coordinates in the y- and z-directions, the expression for the phase shift extends to Φ = 2π(hu+kv+lw). Now, the X-ray wave scattered by the j-th atom in a unit cell will have a scattering amplitude of f_j and a phase of Φ_j, such that the function describing it is . The structure factor we seek, therefore, is the sum of all the scattering functions due to all the unique atoms in a unit cell. This structure factor, F, is given as:

  [9]

and the intensity factor contributed by the structure factor is I = F².

Based on the positions (u,v,w) of atoms on particular planes (h,k,l), there is the possibility of interference between scattered waves that is constructive, destructive, or in-between, and this interference directly affects the amplitude of the XRD peaks representing the (hkl) planes.

Now, a plot of intensity, I, versus 2θ is what is measured in an XRD experiment. The determination of the of crystal type and the associated unit cell parameters (a, b, c, α, β, and γ) can be arrived at analytically by observing systematic presence/absence of peaks, using the equations 2-9, comparing values against databases, using deduction and a process of elimination. Nowadays, this is process is fairly automated by a variety of software linked to crystal structure databases.

PROCEDURE

The following procedure applies to a specific XRD instrument and its associated software, and there may be some variations when other instruments are used.

1. We will examine a Ni powder sample on a Panalytical Alpha-1 XRD instrument.
2. First, choose the mask to fix the beam size according to your sample diameter. The beam must not have a footprint larger than the sample at the smallest θ value (typically ~ 7⁰-10⁰). For a sample of width ε, the beam size should be < ε sinθ.
3. Load the sample in the sample spinner stage and lock the sample into position. The sample spinner helps to spatially randomize the exposure of the sample to the X-ray source.
4. Choose the angle range for your XRD scan. For example 15-90 degrees is a typical range.
5. Choose a step size, i.e. the increment in 2θ, and integration (counting) time. Generally a 0.05 degree step size and 4 seconds integration is the default for a wide angle scan.
6. Once all the peak positions are determined through this initial scan, subsequent scans can focus on a narrower scan range around specific peaks using a smaller step size in angle if higher resolution data from those peaks are desired.

RESULTS

In Figure 4 we see the XRD peaks for the Ni powder sample. Note that the peaks that are observed (e.g. {111}, {200}) are for those that have either all even or all odd combinations of h, k, and l. Ni is face-centered cubic (FCC), and in all FCC structures, the peaks corresponding to {hkl} planes where h, k, and l are mixtures of even and odd integers, are absent due to the destructive interference of the scattered X-rays. Peaks corresponding to planes, such as {210} and {211} are missing. This phenomenon is called the systematic presence and absence rules, and they provide an analytical tool for assessing the crystal structure of the sample.

Figure 4: An XRD scan of Ni with a face-centered cubic structure is shown.

APPLICATIONS AND SUMMARY

This is a demonstration of a standard XRD experiment. The material examined in this experiment was in a powder form, but XRD works equally well with solid piece of material as long as the sample has a flat surface that can be set parallel to the plane of the sample stage.

XRD is a fairly ubiquitous method for determining the presence (or absence) of crystallographic order in materials. Beyond the standard application of determining the crystal structure, XRD is often used to obtain a variety of other structural information such as:

1. Whether or not the structure of a material is amorphous (characterized by a broad hump in the diffraction intensity and a lack of discernable crystallographic peaks),
2. Whether the sample is a composite material consisting of multiple crystallographic phases and, if so, determine the fraction of each phase,
3. Determining whether a material is an amorphous/crystalline composite
4. Determining the grain/particle size of the material,
5. Determining the degree of texture (preferred orientation of grains) in material